Public Jet Trigger Plots for Collision Data

Introduction
2018 pp Data
- Jet Trigger Efficiency Plots, small-R trigger calibration options (May 30, 2018)
2017 pp Data
2016 pp Data
2015 pp Data
Developments and Performance for LHC Run II
- Calorimeter Full Scan versus Partial Scan timing and performance ATL-COM-DAQ-2015-006 (February 9, 2015)
Phase I Upgrade Performance Plots
- Global Feature Extraction (gFEX) Performance Plots ATL-COM-DAQ-2014-087 (August 21, 2014)
2012 pp Data
- Jet Trigger Performance Plots ATL-COM-DAQ-2012-210 (January 09, 2013)
2011 PbPb Data
- HLT Jet Trigger Performance Plots ATLAS-COM-DAQ-2011-148 (December 7, 2011)
2011 pp Data @ 7 TeV
2010 pp Data @ 7 TeV
Outdated

Introduction

Approved plots that can be shown by ATLAS speakers at conferences and similar events.
Please do not add figures on your own. Contact the responsible project leader in case of questions and/or suggestions.

2018 pp Data

Jet Trigger Efficiency Plots, small-R trigger calibration options (May 30, 2018)

Efficiencies are shown for an unprescaled single-jet trigger with two different calibrations applied to jets in the ATLAS high-level trigger (HLT), for 2017 (closed markers) and 2018 data (open markers). Offline jets are selected with ABS(η) < 2.8. The red circles show a calibration using only calorimeter information, while the blue squares show a calibration that also includes track information. Both calibrations include the Global Sequential Calibration (GSC) [1]. The GSC corrects jets according to their longitudinal shower shape and associated track characteristics without changing the overall energy scale. It can be split into parts involving calorimeter-based variables, and parts involving track-based variables. Since tracking is not guaranteed to be available for all jet thresholds, options are provided with and without the track-based corrections. The additional track-based corrections allow for improved agreement between the scale of trigger and offline jets as a function of p_T, and thus the trigger efficiency rises more rapidly. Statistical uncertainties only are shown. [1] ATLAS Collaboration, Jet global sequential corrections with the ATLAS detector in proton-proton collisions at sqrt(s) = 8 TeV, ATLAS-CONF-2015-002.	png pdf eps
Efficiencies are shown for an unprescaled six-jet trigger with two different calibrations applied to jets in the ATLAS high-level trigger (HLT), for 2017 (closed markers) and 2018 data (open markers). Offline jets are selected with ABS(η) < 2.8. The red circles show a calibration using only calorimeter information, while the blue squares show a calibration that also includes track information. Both calibrations include the Global Sequential Calibration (GSC) [1]. The GSC corrects jets according to their longitudinal shower shape and associated track characteristics without changing the overall energy scale. It can be split into parts involving calorimeter-based variables, and parts involving track-based variables. Since tracking is not guaranteed to be available for all jet thresholds, options are provided with and without the track-based corrections. The additional track-based corrections allow for improved agreement between the scale of trigger and offline jets as a function of p_T, and thus the trigger efficiency rises more rapidly. Statistical uncertainties only are shown. [1] ATLAS Collaboration, Jet global sequential corrections with the ATLAS detector in proton-proton collisions at sqrt(s) = 8 TeV, ATLAS-CONF-2015-002.	png eps pdf

Efficiencies are shown for an unprescaled single-jet trigger with two different calibrations applied to jets in the ATLAS high-level trigger (HLT), for 2017 (closed markers) and 2018 data (open markers). Offline jets are selected with ABS(η) < 2.8. The red circles show a calibration using only calorimeter information, while the blue squares show a calibration that also includes track information. Both calibrations include the Global Sequential Calibration (GSC) [1]. The GSC corrects jets according to their longitudinal shower shape and associated track characteristics without changing the overall energy scale. It can be split into parts involving calorimeter-based variables, and parts involving track-based variables. Since tracking is not guaranteed to be available for all jet thresholds, options are provided with and without the track-based corrections. The additional track-based corrections allow for improved agreement between the scale of trigger and offline jets as a function of p_T, and thus the trigger efficiency rises more rapidly. Statistical uncertainties only are shown.

[1] ATLAS Collaboration, Jet global sequential corrections with the ATLAS detector in proton-proton collisions at sqrt(s) = 8 TeV, ATLAS-CONF-2015-002.

png pdf eps

Efficiencies are shown for an unprescaled six-jet trigger with two different calibrations applied to jets in the ATLAS high-level trigger (HLT), for 2017 (closed markers) and 2018 data (open markers). Offline jets are selected with ABS(η) < 2.8. The red circles show a calibration using only calorimeter information, while the blue squares show a calibration that also includes track information. Both calibrations include the Global Sequential Calibration (GSC) [1]. The GSC corrects jets according to their longitudinal shower shape and associated track characteristics without changing the overall energy scale. It can be split into parts involving calorimeter-based variables, and parts involving track-based variables. Since tracking is not guaranteed to be available for all jet thresholds, options are provided with and without the track-based corrections. The additional track-based corrections allow for improved agreement between the scale of trigger and offline jets as a function of p_T, and thus the trigger efficiency rises more rapidly. Statistical uncertainties only are shown.

[1] ATLAS Collaboration, Jet global sequential corrections with the ATLAS detector in proton-proton collisions at sqrt(s) = 8 TeV, ATLAS-CONF-2015-002.

png eps pdf

2017 pp Data

Jet Trigger Efficiency Plots, Trimming and Mass Cuts in Large-R Jets (June 27, 2018)

Efficiencies for two unprescaled single large-R jet triggers are shown as a function of the leading offline trimmed [1] jet p_T, with trimming parameters f_cut = 0.05 and R_sub = 0.2, for 2017 data. Offline jets are selected with ABS(η) < 2.2. In red (circles) is a trigger with an E_T threshold of 460 GeV, while in blue (squares) is a trigger with an E_T threshold of 420 GeV and also a cut of 35 GeV on the mass of the selected trimmed trigger jet. The mass cut significantly suppresses the QCD di-jet background, allowing a lower E_T threshold, while retaining nearly all signal-like jets with a mass of above 50 GeV (for this trigger, the offline jets in the plot are required to have a mass above 50 GeV). A slight inefficiency due to the application of the trimming procedure is mitigated by using f_cut = 0.04 for trigger jets, while the standard offline selection uses f_cut = 0.05. Statistical uncertainties only are shown. [1] D. Krohn, J. Thaler and L.-T. Wang, Jet Trimming, JHEP 02 (2010) 084, [arXiv:0912.1342]..	png eps pdf

Efficiencies for two unprescaled single large-R jet triggers are shown as a function of the leading offline trimmed [1] jet p_T, with trimming parameters f_cut = 0.05 and R_sub = 0.2, for 2017 data. Offline jets are selected with ABS(η) < 2.2. In red (circles) is a trigger with an E_T threshold of 460 GeV, while in blue (squares) is a trigger with an E_T threshold of 420 GeV and also a cut of 35 GeV on the mass of the selected trimmed trigger jet. The mass cut significantly suppresses the QCD di-jet background, allowing a lower E_T threshold, while retaining nearly all signal-like jets with a mass of above 50 GeV (for this trigger, the offline jets in the plot are required to have a mass above 50 GeV). A slight inefficiency due to the application of the trimming procedure is mitigated by using f_cut = 0.04 for trigger jets, while the standard offline selection uses f_cut = 0.05. Statistical uncertainties only are shown.

[1] D. Krohn, J. Thaler and L.-T. Wang, Jet Trimming, JHEP 02 (2010) 084, [arXiv:0912.1342]..

png eps pdf

Jet Trigger Efficiency Plots, large-R trigger jet reconstruction (May 30, 2018)

Efficiencies for three unprescaled single large-R jet triggers are shown as a function of the leading offline trimmed [1] R=1.0 jet p_T, with trimming parameters f_cut=0.05 and R_sub=0.2, for 2017 data. Offline jets are selected with ABS(η) < 2.2. In red (closed circles) is a trigger using R=1.0 jets with area-based pileup subtraction applied, while in blue (open circles) is a trigger using R=0.4 jets, calibrated using calorimeter information as for standard 2017 small-R jet triggers, reclustered to form R=1.0 jets. In green (squares) is a trigger that applies trimming, with improved performance with respect to trimmed offline jets. A slight inefficiency due to the application of the trimming procedure is mitigated by using f_cut=0.04 for trigger jets, while the standard offline selection uses f_cut=0.05. Statistical uncertainties only are shown. [1] D. Krohn, J. Thaler and L.-T. Wang, Jet Trimming, JHEP 02 (2010) 084, [arXiv:0912.1342].	png eps pdf

Efficiencies for three unprescaled single large-R jet triggers are shown as a function of the leading offline trimmed [1] R=1.0 jet p_T, with trimming parameters f_cut=0.05 and R_sub=0.2, for 2017 data. Offline jets are selected with ABS(η) < 2.2. In red (closed circles) is a trigger using R=1.0 jets with area-based pileup subtraction applied, while in blue (open circles) is a trigger using R=0.4 jets, calibrated using calorimeter information as for standard 2017 small-R jet triggers, reclustered to form R=1.0 jets. In green (squares) is a trigger that applies trimming, with improved performance with respect to trimmed offline jets. A slight inefficiency due to the application of the trimming procedure is mitigated by using f_cut=0.04 for trigger jets, while the standard offline selection uses f_cut=0.05. Statistical uncertainties only are shown.

[1] D. Krohn, J. Thaler and L.-T. Wang, Jet Trimming, JHEP 02 (2010) 084, [arXiv:0912.1342].

png eps pdf

Jet Trigger Efficiency Plots, Updates in Large-R Jets from Level-1 to L1Topo Seeds, ATL-COM-DAQ-2017-144 (October 18, 2017) and June 27, 2018

Efficiencies for two different Level-1 triggers are shown as a function of the leading offline trimmed [1] jet p_T for large-radius jets with different numbers of subjets, using 2017 data. The first Level-1 trigger requires E_T > 100 GeV in a Δη × Δφ window of 0.8×0.8. The second is a Level-1 topological trigger requiring Σ E_T > 111 GeV within a cone. The cone algorithm evaluates, for each jet region-of-interest (ROI, Δη × Δφ window of 0.8×0.8) i satisfying E_Tⁱ > 15 GeV, Σ_{jin i} E_T^j for all jet ROIs j within a radius 1.0. The threshold of 111 GeV was chosen to give equal rate to the 0.8×0.8 square window. For jets with large numbers of subjets, this topological Level-1 trigger recovers efficiency with respect to the sliding-window algorithm. Statistical uncertainties only are shown. [1] D. Krohn, J. Thaler and L.-T. Wang, Jet Trimming, JHEP 02 (2010) 084, [arXiv:0912.1342]..	png eps pdf
Efficiencies for a Level-1 trigger requiring in a $\Delta\eta \times \Delta\phi$ window of $0.8\times0.8$ are shown as a function of the leading offline trimmed [1] jet pT for jets with different numbers of subjets. For jets with large numbers of subjets, it becomes more likely that significant energy falls outside the sliding window of the Level-1 trigger, and so the efficiency plateau is reached more slowly. Only statistical uncertainties are shown. [1] D. Krohn, J. Thaler and L.-T. Wang, Jet Trimming, JHEP 02 (2010) 084, [arXiv:0912.1342].	png pdf
Efficiencies for a Level-1 topological trigger requiring $\Sigma ET &gt; 111GeV$ within a cone are shown as a function of the leading offline trimmed [1] jet pT for jets with different numbers of subjets. The cone algorithm evaluates, for each jet region-of-interest (ROI, $\Delta\eta \times \Delta\phi$ window of $0.8\times0.8$ ) i satisfying , $\Sigma_{j\in i} E_T^j$ for all jet ROIs j within a radius 1.0. The threshold of 111 GeV was chosen to give equal rate to the $0.8\times0.8$ square window. For jets with large numbers of subjets, this new Level-1 trigger recovers efficiency with respect to the sliding-window algorithm. Only statistical uncertainties are shown. [1] D. Krohn, J. Thaler and L.-T. Wang, Jet Trimming, JHEP 02 (2010) 084, [arXiv:0912.1342].	png pdf

Efficiencies for two different Level-1 triggers are shown as a function of the leading offline trimmed [1] jet p_T for large-radius jets with different numbers of subjets, using 2017 data. The first Level-1 trigger requires E_T > 100 GeV in a Δη × Δφ window of 0.8×0.8. The second is a Level-1 topological trigger requiring Σ E_T > 111 GeV within a cone. The cone algorithm evaluates, for each jet region-of-interest (ROI, Δη × Δφ window of 0.8×0.8) i satisfying E_Tⁱ > 15 GeV, Σ_{jin i} E_T^j for all jet ROIs j within a radius 1.0. The threshold of 111 GeV was chosen to give equal rate to the 0.8×0.8 square window. For jets with large numbers of subjets, this topological Level-1 trigger recovers efficiency with respect to the sliding-window algorithm. Statistical uncertainties only are shown.

[1] D. Krohn, J. Thaler and L.-T. Wang, Jet Trimming, JHEP 02 (2010) 084, [arXiv:0912.1342]..

png eps pdf

Efficiencies for a Level-1 trigger requiring in a $\Delta\eta \times \Delta\phi$ window of $0.8\times0.8$ are shown as a function of the leading offline trimmed [1] jet pT for jets with different numbers of subjets. For jets with large numbers of subjets, it becomes more likely that significant energy falls outside the sliding window of the Level-1 trigger, and so the efficiency plateau is reached more slowly. Only statistical uncertainties are shown.

[1] D. Krohn, J. Thaler and L.-T. Wang, Jet Trimming, JHEP 02 (2010) 084, [arXiv:0912.1342].

png pdf

Efficiencies for a Level-1 topological trigger requiring $\Sigma ET &gt; 111GeV$ within a cone are shown as a function of the leading offline trimmed [1] jet pT for jets with different numbers of subjets. The cone algorithm evaluates, for each jet region-of-interest (ROI, $\Delta\eta \times \Delta\phi$ window of $0.8\times0.8$ ) i satisfying , $\Sigma_{j\in i} E_T^j$ for all jet ROIs j within a radius 1.0. The threshold of 111 GeV was chosen to give equal rate to the $0.8\times0.8$ square window. For jets with large numbers of subjets, this new Level-1 trigger recovers efficiency with respect to the sliding-window algorithm. Only statistical uncertainties are shown.

[1] D. Krohn, J. Thaler and L.-T. Wang, Jet Trimming, JHEP 02 (2010) 084, [arXiv:0912.1342].

png pdf

Jet Trigger Efficiency Plots, Calibration Updates in Small-R Jets, ATL-COM-DAQ-2017-144 (October 18, 2017)

Efficiencies are shown for a single-jet trigger with three different calibrations applied to jets in the ATLAS high-level trigger (HLT). Offline jets are selected with $ABS(\eta) &lt; 2.8$ . In green (open squares) the calibration applied in 2016 data, in red (closed circles) the updated calibration applied in 2017, utilising only calorimeter information, and in blue (open circles) this updated calibration additionally with track information. The extra calibration steps include the Global Sequential Calibration (GSC) [1] and the application of in situ corrections. The GSC corrects jets according to their longitudinal shower shape and associated track characteristics without changing the overall energy scale. It can be split into parts involving calorimeter-based variables, and parts involving track-based variables. Since tracking is not guaranteed to be available for all jet thresholds, options are provided with and without the track-based corrections. The data-driven eta-intercalibration correction [2] is the most important in situ correction added, and corrects differences in jet response as a function of $\eta$ . Together, these additional corrections allow for improved agreement between the scale of trigger and offline jets as a function of both $\eta$ and $p_\mathrm{T}$ , and thus the trigger efficiency rises much more rapidly. [1] ATLAS-CONF-2015-002, [2] ATLAS-CONF-2015-017.	png pdf
Efficiencies are shown for an unprescaled 6-jet trigger with two different calibrations applied to jets in the ATLAS high-level trigger (HLT). Offline jets are selected with $ABS(\eta) &lt; 2.8$ . In red (closed circles) the updated calibration applied in 2017, utilising only calorimeter information, and in blue (open circles) this updated calibration additionally with track information. The extra calibration steps include the Global Sequential Calibration (GSC) [1] and the application of in situ corrections. The GSC corrects jets according to their longitudinal shower shape and associated track characteristics without changing the overall energy scale. It can be split into parts involving calorimeter-based variables, and parts involving track-based variables. Since tracking is not guaranteed to be available for all jet thresholds, options are provided with and without the track-based corrections. The data-driven eta-intercalibration correction [2] is the most important in situ correction added, and corrects differences in jet response as a function of $\eta$ . Together, these additional corrections allow for improved agreement between the scale of trigger and offline jets as a function of both $\eta$ and $p_\mathrm{T}$ , and thus the trigger efficiency rises more rapidly. Only statistical uncertainties are shown. [1] ATLAS-CONF-2015-002, [2] ATLAS-CONF-2015-017.	png pdf

Efficiencies are shown for a single-jet trigger with three different calibrations applied to jets in the ATLAS high-level trigger (HLT). Offline jets are selected with $ABS(\eta) &lt; 2.8$ . In green (open squares) the calibration applied in 2016 data, in red (closed circles) the updated calibration applied in 2017, utilising only calorimeter information, and in blue (open circles) this updated calibration additionally with track information. The extra calibration steps include the Global Sequential Calibration (GSC) [1] and the application of in situ corrections. The GSC corrects jets according to their longitudinal shower shape and associated track characteristics without changing the overall energy scale. It can be split into parts involving calorimeter-based variables, and parts involving track-based variables. Since tracking is not guaranteed to be available for all jet thresholds, options are provided with and without the track-based corrections. The data-driven eta-intercalibration correction [2] is the most important in situ correction added, and corrects differences in jet response as a function of $\eta$ . Together, these additional corrections allow for improved agreement between the scale of trigger and offline jets as a function of both $\eta$ and $p_\mathrm{T}$ , and thus the trigger efficiency rises much more rapidly.

[1] ATLAS-CONF-2015-002, [2] ATLAS-CONF-2015-017.

png pdf

Efficiencies are shown for an unprescaled 6-jet trigger with two different calibrations applied to jets in the ATLAS high-level trigger (HLT). Offline jets are selected with $ABS(\eta) &lt; 2.8$ . In red (closed circles) the updated calibration applied in 2017, utilising only calorimeter information, and in blue (open circles) this updated calibration additionally with track information. The extra calibration steps include the Global Sequential Calibration (GSC) [1] and the application of in situ corrections. The GSC corrects jets according to their longitudinal shower shape and associated track characteristics without changing the overall energy scale. It can be split into parts involving calorimeter-based variables, and parts involving track-based variables. Since tracking is not guaranteed to be available for all jet thresholds, options are provided with and without the track-based corrections. The data-driven eta-intercalibration correction [2] is the most important in situ correction added, and corrects differences in jet response as a function of $\eta$ . Together, these additional corrections allow for improved agreement between the scale of trigger and offline jets as a function of both $\eta$ and $p_\mathrm{T}$ , and thus the trigger efficiency rises more rapidly. Only statistical uncertainties are shown.

[1] ATLAS-CONF-2015-002, [2] ATLAS-CONF-2015-017.

png pdf

Jet Trigger Efficiency Plots, Trimming and Mass Cuts in Large-R Jets, ATL-COM-DAQ-2017-063 (July 4, 2017)

* https://cds.cern.ch/record/2271959/

This plot is superseded by the more recent one above ("Jet Trigger Efficiency Plots, Trimming and Mass Cuts in Large-R Jets (June 27, 2018)"). Efficiencies for HLT large-R single-jet triggers are shown as a function of the leading offline trimmed [1] jet $p_\mathrm{T}$ for jets with $ABS(\eta) &lt; 2.0$ and jet mass above 50 GeV. Two large-R jet triggers, from the 2017 menu, are shown. Blue circles represent a trimmed large-R jet trigger with a $p_\mathrm{T}$ threshold of 420 GeV. Adding an additional 30 GeV cut on the jet mass of the selected trimmed trigger jet is shown in green triangles. The mass cut significantly suppresses the QCD di-jet background, allowing a lower $p_\mathrm{T}$ threshold of 390 GeV, while retaining nearly all signal-like jets with a mass of above 50 GeV. A slight inefficiency is observed in both triggers due to small differences between the application of the trimming procedure to trigger and offline jets. To mitigate this effect, $f_\mathrm{cut}=0.04$ will be used for trigger jets, while the standard offline selection uses $f_\mathrm{cut}=0.05$ . Events used to measure the performance of each trigger are selected from fully-efficient, lower-threshold jet triggers. Only statistical uncertainties are shown. [1] D. Krohn, J. Thaler and L.-T. Wang, Jet Trimming, JHEP 02 (2010) 084, [arXiv:0912.1342].	png pdf
Efficiencies for HLT large-R triggers are shown as a function of the second leading offline trimmed [1] jet $p_\mathrm{T}$ for jets with $ABS(\eta) &lt; 2.0$ and jet mass above 50 GeV. Two large-R jet triggers, from the 2017 menu, are shown. Blue circles represent a single-jet trimmed large-R jet trigger with a $p_\mathrm{T}$ threshold of 420 GeV. Adding an additional 30 GeV cut on the leading and second leading jet masses of the selected trimmed trigger jets is shown in green triangles. The mass cuts significantly suppress the QCD di-jet background, allowing a much lower $p_\mathrm{T}$ threshold of 330 GeV, while retaining nearly all signal-like jets with a mass of above 50 GeV. A slight inefficiency is observed in both triggers due to small differences between the application of the trimming procedure to trigger and offline jets. To mitigate this effect, $f_\mathrm{cut}=0.04$ will be used for trigger jets, while the standard offline selection uses $f_\mathrm{cut}=0.05$ . Events used to measure the performance of each trigger are selected from fully-efficient, lower-threshold jet triggers. Only statistical uncertainties are shown. [1] D. Krohn, J. Thaler and L.-T. Wang, Jet Trimming, JHEP 02 (2010) 084, [arXiv:0912.1342].	png pdf
Efficiencies for an HLT large-R trigger is shown as a function of the second leading offline trimmed [1] jet mass for jets with $ABS(\eta) &lt; 2.0$ and jet $p_\mathrm{T}$ above 400 GeV. The red circles represent a dijet trimmed large-R jet trigger with a $p_\mathrm{T}$ threshold of 330 GeV and an additional 30 GeV cut on the jet masses of the leading and second leading selected trimmed trigger jets. The mass cuts significantly suppress the QCD di-jet background, allowing the much lower $p_\mathrm{T}$ threshold, while retaining nearly all signal-like jets with a mass of above 50 GeV. A slight inefficiency is observed due to small differences between the application of the trimming procedure to trigger and offline jets. To mitigate this effect, $f_\mathrm{cut}=0.04$ will be used for trigger jets, while the standard offline selection uses $f_\mathrm{cut}=0.05$ . Events used to measure the performance of each trigger are selected from fully-efficient, lower-threshold jet triggers. Only statistical uncertainties are shown. [1] D. Krohn, J. Thaler and L.-T. Wang, Jet Trimming, JHEP 02 (2010) 084, [arXiv:0912.1342].	png pdf

This plot is superseded by the more recent one above ("Jet Trigger Efficiency Plots, Trimming and Mass Cuts in Large-R Jets (June 27, 2018)"). Efficiencies for HLT large-R single-jet triggers are shown as a function of the leading offline trimmed [1] jet $p_\mathrm{T}$ for jets with $ABS(\eta) &lt; 2.0$ and jet mass above 50 GeV. Two large-R jet triggers, from the 2017 menu, are shown. Blue circles represent a trimmed large-R jet trigger with a $p_\mathrm{T}$ threshold of 420 GeV. Adding an additional 30 GeV cut on the jet mass of the selected trimmed trigger jet is shown in green triangles. The mass cut significantly suppresses the QCD di-jet background, allowing a lower $p_\mathrm{T}$ threshold of 390 GeV, while retaining nearly all signal-like jets with a mass of above 50 GeV. A slight inefficiency is observed in both triggers due to small differences between the application of the trimming procedure to trigger and offline jets. To mitigate this effect, $f_\mathrm{cut}=0.04$ will be used for trigger jets, while the standard offline selection uses $f_\mathrm{cut}=0.05$ . Events used to measure the performance of each trigger are selected from fully-efficient, lower-threshold jet triggers. Only statistical uncertainties are shown.

[1] D. Krohn, J. Thaler and L.-T. Wang, Jet Trimming, JHEP 02 (2010) 084, [arXiv:0912.1342].

png pdf

Efficiencies for HLT large-R triggers are shown as a function of the second leading offline trimmed [1] jet $p_\mathrm{T}$ for jets with $ABS(\eta) &lt; 2.0$ and jet mass above 50 GeV. Two large-R jet triggers, from the 2017 menu, are shown. Blue circles represent a single-jet trimmed large-R jet trigger with a $p_\mathrm{T}$ threshold of 420 GeV. Adding an additional 30 GeV cut on the leading and second leading jet masses of the selected trimmed trigger jets is shown in green triangles. The mass cuts significantly suppress the QCD di-jet background, allowing a much lower $p_\mathrm{T}$ threshold of 330 GeV, while retaining nearly all signal-like jets with a mass of above 50 GeV. A slight inefficiency is observed in both triggers due to small differences between the application of the trimming procedure to trigger and offline jets. To mitigate this effect, $f_\mathrm{cut}=0.04$ will be used for trigger jets, while the standard offline selection uses $f_\mathrm{cut}=0.05$ . Events used to measure the performance of each trigger are selected from fully-efficient, lower-threshold jet triggers. Only statistical uncertainties are shown.

[1] D. Krohn, J. Thaler and L.-T. Wang, Jet Trimming, JHEP 02 (2010) 084, [arXiv:0912.1342].

png pdf

Efficiencies for an HLT large-R trigger is shown as a function of the second leading offline trimmed [1] jet mass for jets with $ABS(\eta) &lt; 2.0$ and jet $p_\mathrm{T}$ above 400 GeV. The red circles represent a dijet trimmed large-R jet trigger with a $p_\mathrm{T}$ threshold of 330 GeV and an additional 30 GeV cut on the jet masses of the leading and second leading selected trimmed trigger jets. The mass cuts significantly suppress the QCD di-jet background, allowing the much lower $p_\mathrm{T}$ threshold, while retaining nearly all signal-like jets with a mass of above 50 GeV. A slight inefficiency is observed due to small differences between the application of the trimming procedure to trigger and offline jets. To mitigate this effect, $f_\mathrm{cut}=0.04$ will be used for trigger jets, while the standard offline selection uses $f_\mathrm{cut}=0.05$ . Events used to measure the performance of each trigger are selected from fully-efficient, lower-threshold jet triggers. Only statistical uncertainties are shown.

[1] D. Krohn, J. Thaler and L.-T. Wang, Jet Trimming, JHEP 02 (2010) 084, [arXiv:0912.1342].

png pdf

Jet Trigger Efficiency Plots, Calibration Updates in Small-R Jets, ATL-COM-DAQ-2017-063 (July 4, 2017)

* https://cds.cern.ch/record/2271959/

Efficiencies are shown for an unprescaled single-jet trigger with three different calibrations applied to jets in the ATLAS high-level trigger (HLT). Offline jets are selected with $ABS(\eta) &lt; 2.8$ . In red (closed circles) the calibration steps applied in 2016 data, in blue (open circles) the updated calibration applied in 2017, utilising only calorimeter information, and in green (open squares) this updated calibration additionally with track information. The extra calibration steps include the global sequential corrections [1] and the application of in situ corrections. The Global Sequential Calibration (GSC) corrects jets according to their longitudinal shower shape and associated track characteristics without changing the overall energy scale. They can be split into parts involving calorimeter-based variables, and parts involving track-based variables. Since tracking is not guaranteed to be available for all jet thresholds, options are provided with and without the track-based corrections. The data-driven eta-intercalibration correction [2] is the most important in situ correction added, and fixes differences in jet response as a function of eta. Together, these additional corrections allow for improved agreement between the scale of trigger and offline jets as a function of both eta and $p_\mathrm{T}$ , and thus the trigger efficiency rises much more rapidly. [1] ATLAS-CONF-2015-002, [2] ATLAS-CONF-2015-017.	png pdf
Efficiencies are shown for an unprescaled 6-jet trigger with two different calibrations applied to jets in the ATLAS high-level trigger (HLT). Offline jets are selected with $ABS(\eta) &lt; 2.8$ . In red (closed circles) the updated calibration applied in 2017, utilising only calorimeter information, and in blue (open circles) this updated calibration additionally with track information. The extra calibration steps for 2017 include the global sequential corrections [1] and the application of in situ corrections. The Global Sequential Calibration (GSC) corrects jets according to their longitudinal shower shape and associated track characteristics without changing the overall energy scale. They can be split into parts involving calorimeter-based variables, and parts involving track-based variables. The data-driven eta-intercalibration correction [2] is the most important in situ correction added, and fixes differences in jet response as a function of eta. The track-based corrections reduce the acceptance below the trigger threshold while retaining the same efficiency above it, allowing the same offline threshold to be maintained for a lower rate. [1] ATLAS-CONF-2015-002, [2] ATLAS-CONF-2015-017.	png pdf

Efficiencies are shown for an unprescaled single-jet trigger with three different calibrations applied to jets in the ATLAS high-level trigger (HLT). Offline jets are selected with $ABS(\eta) &lt; 2.8$ . In red (closed circles) the calibration steps applied in 2016 data, in blue (open circles) the updated calibration applied in 2017, utilising only calorimeter information, and in green (open squares) this updated calibration additionally with track information. The extra calibration steps include the global sequential corrections [1] and the application of in situ corrections. The Global Sequential Calibration (GSC) corrects jets according to their longitudinal shower shape and associated track characteristics without changing the overall energy scale. They can be split into parts involving calorimeter-based variables, and parts involving track-based variables. Since tracking is not guaranteed to be available for all jet thresholds, options are provided with and without the track-based corrections. The data-driven eta-intercalibration correction [2] is the most important in situ correction added, and fixes differences in jet response as a function of eta. Together, these additional corrections allow for improved agreement between the scale of trigger and offline jets as a function of both eta and $p_\mathrm{T}$ , and thus the trigger efficiency rises much more rapidly.

[1] ATLAS-CONF-2015-002, [2] ATLAS-CONF-2015-017.

png pdf

Efficiencies are shown for an unprescaled 6-jet trigger with two different calibrations applied to jets in the ATLAS high-level trigger (HLT). Offline jets are selected with $ABS(\eta) &lt; 2.8$ . In red (closed circles) the updated calibration applied in 2017, utilising only calorimeter information, and in blue (open circles) this updated calibration additionally with track information. The extra calibration steps for 2017 include the global sequential corrections [1] and the application of in situ corrections. The Global Sequential Calibration (GSC) corrects jets according to their longitudinal shower shape and associated track characteristics without changing the overall energy scale. They can be split into parts involving calorimeter-based variables, and parts involving track-based variables. The data-driven eta-intercalibration correction [2] is the most important in situ correction added, and fixes differences in jet response as a function of eta. The track-based corrections reduce the acceptance below the trigger threshold while retaining the same efficiency above it, allowing the same offline threshold to be maintained for a lower rate.

[1] ATLAS-CONF-2015-002, [2] ATLAS-CONF-2015-017.

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2016 pp Data

Jet Trigger Efficiency Plots, Adding Trimming and Mass Cuts to Large-R Jets, ATL-COM-DAQ-2017-007 (February 14, 2017)

* https://cds.cern.ch/record/2244774/

Efficiencies for HLT large-R single-jet triggers are shown as a function of the leading offline trimmed [1] jet $p_\mathrm{T}$ for jets with $ABS(\eta) &lt; 2.0$ . Three types of large-R jet triggers are shown, with thresholds chosen to result in equal rates. Red circles represent the standard large-R jet triggers used in the 2015 and 2016 datasets. Applying trimming in the trigger is shown using blue squares, resulting in improved agreement between trigger and offline jets, thus the trigger efficiency rises more rapidly. Adding an additional $30\,\mathrm{GeV}$ cut on the jet mass of the selected trimmed trigger jet is shown in green triangles. The mass cut significantly suppresses the QCD di-jet background, leading to a much lower $p_\mathrm{T}$ threshold for an equivalent rate while retaining nearly all signal-like jets with a mass of above $50\,\mathrm{GeV}$ . A slight inefficiency is observed in the green triangles due to small differences between the application of the trimming procedure to trigger and offline jets. To mitigate this effect, $f_\mathrm{cut}=0.04$ is used for trigger jets, while the standard offline selection uses $f_\mathrm{cut}=0.05$ . Events used to measure the performance of each trigger are selected from fully-efficient, lower-threshold jet triggers. Only statistical uncertainties are shown. [1] D. Krohn, J. Thaler and L.-T. Wang, Jet Trimming, JHEP 02 (2010) 084, [arXiv:0912.1342].	png pdf
Efficiencies for HLT large-R di-jet triggers are shown as a function of the second leading offline trimmed [1] jet $p_\mathrm{T}$ for jets with $ABS(\eta) &lt; 2.0$ . Three types of large-R jet triggers are shown, with thresholds chosen to result in equal rates. Red circles represent the standard large-R jet triggers used in the 2015 and 2016 datasets. Applying trimming in the trigger is shown using blue squares, resulting in improved agreement between trigger and offline jets, thus the trigger efficiency rises more rapidly. Adding an additional $30\,\mathrm{GeV}$ cut on the jet mass of both selected trimmed trigger jets is shown in green triangles. The mass cut significantly suppresses the QCD di-jet background, leading to a much lower $p_\mathrm{T}$ threshold for an equivalent rate while retaining nearly all signal-like jets with a mass of above $50\,\mathrm{GeV}$ . A slight inefficiency is observed in the green triangles due to small differences between the application of the trimming procedure to trigger and offline jets. To mitigate this effect, $f_\mathrm{cut}=0.04$ is used for trigger jets, while the standard offline selection uses $f_\mathrm{cut}=0.05$ . Events used to measure the performance of each trigger are selected from fully-efficient, lower-threshold jet triggers. Only statistical uncertainties are shown. [1] D. Krohn, J. Thaler and L.-T. Wang, Jet Trimming, JHEP 02 (2010) 084, [arXiv:0912.1342].	png pdf

Efficiencies for HLT large-R single-jet triggers are shown as a function of the leading offline trimmed [1] jet $p_\mathrm{T}$ for jets with $ABS(\eta) &lt; 2.0$ . Three types of large-R jet triggers are shown, with thresholds chosen to result in equal rates. Red circles represent the standard large-R jet triggers used in the 2015 and 2016 datasets. Applying trimming in the trigger is shown using blue squares, resulting in improved agreement between trigger and offline jets, thus the trigger efficiency rises more rapidly. Adding an additional $30\,\mathrm{GeV}$ cut on the jet mass of the selected trimmed trigger jet is shown in green triangles. The mass cut significantly suppresses the QCD di-jet background, leading to a much lower $p_\mathrm{T}$ threshold for an equivalent rate while retaining nearly all signal-like jets with a mass of above $50\,\mathrm{GeV}$ . A slight inefficiency is observed in the green triangles due to small differences between the application of the trimming procedure to trigger and offline jets. To mitigate this effect, $f_\mathrm{cut}=0.04$ is used for trigger jets, while the standard offline selection uses $f_\mathrm{cut}=0.05$ . Events used to measure the performance of each trigger are selected from fully-efficient, lower-threshold jet triggers. Only statistical uncertainties are shown.

[1] D. Krohn, J. Thaler and L.-T. Wang, Jet Trimming, JHEP 02 (2010) 084, [arXiv:0912.1342].

png pdf

Efficiencies for HLT large-R di-jet triggers are shown as a function of the second leading offline trimmed [1] jet $p_\mathrm{T}$ for jets with $ABS(\eta) &lt; 2.0$ . Three types of large-R jet triggers are shown, with thresholds chosen to result in equal rates. Red circles represent the standard large-R jet triggers used in the 2015 and 2016 datasets. Applying trimming in the trigger is shown using blue squares, resulting in improved agreement between trigger and offline jets, thus the trigger efficiency rises more rapidly. Adding an additional $30\,\mathrm{GeV}$ cut on the jet mass of both selected trimmed trigger jets is shown in green triangles. The mass cut significantly suppresses the QCD di-jet background, leading to a much lower $p_\mathrm{T}$ threshold for an equivalent rate while retaining nearly all signal-like jets with a mass of above $50\,\mathrm{GeV}$ . A slight inefficiency is observed in the green triangles due to small differences between the application of the trimming procedure to trigger and offline jets. To mitigate this effect, $f_\mathrm{cut}=0.04$ is used for trigger jets, while the standard offline selection uses $f_\mathrm{cut}=0.05$ . Events used to measure the performance of each trigger are selected from fully-efficient, lower-threshold jet triggers. Only statistical uncertainties are shown.

[1] D. Krohn, J. Thaler and L.-T. Wang, Jet Trimming, JHEP 02 (2010) 084, [arXiv:0912.1342].

png pdf

Jet Trigger Efficiency Plots, Adding the Global Sequential Calibration, ATL-COM-DAQ-2016-185 (November 29, 2016)

* https://cds.cern.ch/record/2234832/

Efficiencies are shown for the lowest un-prescaled single-jet trigger with (red, closed circules) and without (blue, open circles) the updated calibration applied to jets in the ATLAS high-level trigger (HLT), selecting jets with $ABS(\eta) &lt; 2.8$ . The updated calibration includes updated MC calibrations, the addition of global sequential corrections [1], and the application of in situ corrections. The Global Sequential Calibration (GSC) corrects jets according to their longitudinal shower shape and associated track characteristics without changing the overall energy scale. These track-based corrections represent a significant part of the difference in how offline and HLT jets are calibrated. The data-driven eta-intercalibration correction [2] is the most important in situ correction added, which fixes differences in jet response as a function of eta. Together, these additional corrections allow for improved agreement between the scale of trigger and offline jets as a function of both eta and $p_\mathrm{T}$ , and thus the trigger efficiency rises much more rapidly. The trigger with updated corrections was added for commissioning during summer 2016. [1] ATLAS-CONF-2015-002 [2] ATLAS-CONF-2015-017	png pdf

Efficiencies are shown for the lowest un-prescaled single-jet trigger with (red, closed circules) and without (blue, open circles) the updated calibration applied to jets in the ATLAS high-level trigger (HLT), selecting jets with $ABS(\eta) &lt; 2.8$ . The updated calibration includes updated MC calibrations, the addition of global sequential corrections [1], and the application of in situ corrections. The Global Sequential Calibration (GSC) corrects jets according to their longitudinal shower shape and associated track characteristics without changing the overall energy scale. These track-based corrections represent a significant part of the difference in how offline and HLT jets are calibrated. The data-driven eta-intercalibration correction [2] is the most important in situ correction added, which fixes differences in jet response as a function of eta. Together, these additional corrections allow for improved agreement between the scale of trigger and offline jets as a function of both eta and $p_\mathrm{T}$ , and thus the trigger efficiency rises much more rapidly. The trigger with updated corrections was added for commissioning during summer 2016.

[1] ATLAS-CONF-2015-002
[2] ATLAS-CONF-2015-017

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Jet Trigger Efficiency Plots, Hadronic top selection, ATL-COM-DAQ-2016-130 (September 20, 2016)

* https://cds.cern.ch/record/2215535

Efficiency of the HLT_5j65_0eta240_L14J15 trigger as a function of the transverse momentum of the 5th leading offline jet. The trigger selection requires 5 jets with $E_\mathrm{T} &gt; 65\,\mathrm{GeV}$ and $ABS(\eta) &lt; 2.4$ (HLT) and 4 jets with $E_\mathrm{T} &gt; 15\,\mathrm{GeV}$ and $ABS(\eta)&lt;3.1$ (Level 1). The offline event selection is defined to mimic all-hadronic decays of a top-antitop pair, requiring 5 jets with $p_\mathrm{T} &gt; 55\,\mathrm{GeV}$ and the 6th jets with $p_\mathrm{T} &gt; 25\,\mathrm{GeV}$ , all jets having $ABS(\eta)&lt;2.4$ , and at least two jets b-tagged. The trigger efficiency is determined using events retained with a lower threshold trigger that is found to be fully efficient in the phase space of interest. The data collected in 2016 is compared to top-antitop events simulated with PowHeg + Pythia6. The error bars show the statistical uncertainty only. The slight inefficiency in the plateau region is due to the coincidence in the $ABS(\eta)$ cuts applied in the online and offline event selection.	png pdf
Efficiency of the HLT_6j45_0eta240 trigger as a function of the transverse momentum of the 6th leading offline jet. The trigger selection requires 6 jets with $E_\mathrm{T} &gt;45\,\mathrm{GeV}$ and $ABS(\eta)&lt;2.4$ (HLT) and 4 jets with $E_\mathrm{T} &gt; 15\,\mathrm{GeV}$ and $ABS(\eta)&lt;3.1$ (Level 1). The offline event selection is defined to mimic all-hadronic decays of a top-antitop pair, requiring 5 jets with $p_\mathrm{T} &gt; 55\,\mathrm{GeV}$ and the 6th jets with $p_\mathrm{T} &gt; 40\,\mathrm{GeV}$ , all jets having $ABS(\eta)&lt;2.4$ . and at least two jets b-tagged. The trigger efficiency is determined using events retained with a lower threshold trigger that is found to be fully efficient in the phase space of interest. The data collected in 2016 is compared to top-antitop events simulated with PowHeg + Pythia6. The error bars show the statistical uncertainty only. The slight inefficiency in the plateau regions is due to the coincidence in the $ABS(\eta)$ cuts applied in the online and offline event selection.	png pdf

Efficiency of the HLT_5j65_0eta240_L14J15 trigger as a function of the transverse momentum of the 5th leading offline jet. The trigger selection requires 5 jets with $E_\mathrm{T} &gt; 65\,\mathrm{GeV}$ and $ABS(\eta) &lt; 2.4$ (HLT) and 4 jets with $E_\mathrm{T} &gt; 15\,\mathrm{GeV}$ and $ABS(\eta)&lt;3.1$ (Level 1). The offline event selection is defined to mimic all-hadronic decays of a top-antitop pair, requiring 5 jets with $p_\mathrm{T} &gt; 55\,\mathrm{GeV}$ and the 6th jets with $p_\mathrm{T} &gt; 25\,\mathrm{GeV}$ , all jets having $ABS(\eta)&lt;2.4$ , and at least two jets b-tagged. The trigger efficiency is determined using events retained with a lower threshold trigger that is found to be fully efficient in the phase space of interest. The data collected in 2016 is compared to top-antitop events simulated with PowHeg + Pythia6. The error bars show the statistical uncertainty only. The slight inefficiency in the plateau region is due to the coincidence in the $ABS(\eta)$ cuts applied in the online and offline event selection.

png pdf

Efficiency of the HLT_6j45_0eta240 trigger as a function of the transverse momentum of the 6th leading offline jet. The trigger selection requires 6 jets with $E_\mathrm{T} &gt;45\,\mathrm{GeV}$ and $ABS(\eta)&lt;2.4$ (HLT) and 4 jets with $E_\mathrm{T} &gt; 15\,\mathrm{GeV}$ and $ABS(\eta)&lt;3.1$ (Level 1). The offline event selection is defined to mimic all-hadronic decays of a top-antitop pair, requiring 5 jets with $p_\mathrm{T} &gt; 55\,\mathrm{GeV}$ and the 6th jets with $p_\mathrm{T} &gt; 40\,\mathrm{GeV}$ , all jets having $ABS(\eta)&lt;2.4$ . and at least two jets b-tagged. The trigger efficiency is determined using events retained with a lower threshold trigger that is found to be fully efficient in the phase space of interest. The data collected in 2016 is compared to top-antitop events simulated with PowHeg + Pythia6. The error bars show the statistical uncertainty only. The slight inefficiency in the plateau regions is due to the coincidence in the $ABS(\eta)$ cuts applied in the online and offline event selection.

png pdf

Jet Trigger Efficiency Plots, 2015 vs 2016, ATL-COM-DAQ-2016-087 (August 1, 2016)

* https://cds.cern.ch/record/2200390

Efficiencies for L1 single-jet triggers are shown as a function of leading offline jet $p_\mathrm{T}$ for jets with $ABS(\eta) &lt; 2.8$ . Triggers denoted L1_JX accept an event if a jet is reconstructed at L1 with $E_\mathrm{T} &gt; X\,\mathrm{GeV}$ . The un-prescaled trigger with the lowest threshold requires a jet with $E_\mathrm{T} &gt; 100\,\mathrm{GeV}$ at L1 (L1_J100). Triggers in 2016 (filled circles) become fully efficient at the same point as was observed in 2015 (open circles), despite higher levels of pileup. Events used to measure the performance of each trigger are selected from fully-efficient, lower-threshold jet triggers. Only statistical uncertainties are shown.	png pdf
Efficiencies for HLT single-jet triggers are shown as a function of leading offline jet $p_\mathrm{T}$ for jets with $ABS(\eta) &lt; 2.8$ . Triggers denoted HLT_jX accept an event if a jet is reconstructed at HLT with $E_\mathrm{T} &gt; X\,\mathrm{GeV}$ . The un-prescaled trigger with the lowest threshold requires a jet with $E_\mathrm{T} &gt; 380\,\mathrm{GeV}$ at HLT (HLT_j380). Triggers in 2016 (filled circles) become fully efficient at the same point as was observed in 2015 (open circles), despite higher levels of pileup. The Level-1 seed trigger for each HLT item is shown in parenthesis; 'L1 random' denotes that events are randomly accepted at L1. Events used to measure the performance of each trigger are selected from fully-efficient, lower-threshold jet triggers. Only statistical uncertainties are shown.	png pdf
Efficiencies for forward L1 single-jet triggers are shown as a function of leading offline jet $p_\mathrm{T}$ for jets with $3.6 &lt; ABS(\eta) &lt; 4.5$ . Triggers denoted L1_JX.31ETA49 accept an event if a jet is reconstructed at L1 with $E_\mathrm{T} &gt; X\,\mathrm{GeV}$ and $3.1&lt;ABS(\eta)&lt;4.9$ . The un-prescaled trigger with the lowest threshold requires a jet with $E_\mathrm{T} &gt; 75\,\mathrm{GeV}$ at L1 (L1_J75.31ETA49). 2016 performance (filled circles) has shifted to become fully efficient at higher offline jet $E_\mathrm{T}$ compared to that observed in 2015 (open circles). This expected shift is due to an update made to L1 noise thresholds in anticipation of increased pileup and modified beam conditions for 2016 data-taking. Events used to measure the performance of each trigger are selected from fully-efficient, lower-threshold jet triggers. Only statistical uncertainties are shown.	png pdf
Efficiencies for HLT single-jet triggers are shown as a function of leading offline jet $p_\mathrm{T}$ for jets with $3.6 &lt; ABS(\eta) &lt; 4.5$ . Triggers denoted HLT_jX_320eta490 accept an event if a jet is reconstructed at HLT with $E_\mathrm{T} &gt; X\,\mathrm{GeV}$ and $3.2 &lt; ABS(\eta) &lt; 4.9$ . Performance in 2016 (filled circles) is compared to data taken in 2015 (open circles), showing consistent performance across a wide spectrum of jet energies. The Level-1 seed trigger for each HLT item is shown in parenthesis; 'L1 random' denotes that events are randomly accepted at L1. Events used to measure the performance of each trigger are selected from fully-efficient, lower-threshold jet triggers. Only statistical uncertainties are shown.	png pdf
Efficiencies are shown for L1 n-jet triggers as a function of the $n^{th}$ leading offline jet $p_\mathrm{T}$ . Triggers denoted L1_nJX accept an event if n jets are reconstructed at L1 with $E_\mathrm{T} &gt; X\,\mathrm{GeV}$ . In addition, jets are required to satisfy $ABS(\eta)&lt;2.8$ . Here the n-leading jets are required to be separated by $\Delta R &gt; 0.6$ . Performance in 2016 (filled circles) is seen to be consistent with that observed in 2015 (open circles). Events used to measure the performance of each trigger are selected from fully-efficient, lower-threshold single-jet triggers. Only statistical uncertainties are shown.	png pdf
Efficiencies are shown for HLT N-jet triggers as a function of the $N^{th}$ leading offline jet $p_\mathrm{T}$ . Triggers denoted HLT_NjX accept an event if N jets are reconstructed at L1 with $E_\mathrm{T} &gt; X\,\mathrm{GeV}$ . In addition, jets are required to satisfy $ABS(\eta)&lt;2.8$ . Here the N-leading jets are required to be separated by $\Delta R &gt; 0.6$ . Trigger efficiencies in 2016 (filled circles) are seen to be consistent with those observed in 2015 (open circles), and consistently independent of the jet multiplicity required. The Level-1 seed trigger for each HLT item is shown in parenthesis. Events used to measure the performance of each trigger are selected from fully-efficient, lower-threshold single-jet triggers. Only statistical uncertainties are shown.	png pdf

2015 pp Data

Jet Trigger Calibration Performance Plot ATL-COM-DAQ-2016-017 (February 26, 2016)

* https://cds.cern.ch/record/2133173

Relative jet response as a function of the jet pseudorapidity for anti- HLT jets with R=0.4 calibrated with a dedicated EM+JES scheme, for $85\mathrm{GeV} &lt; p_\mathrm{T}^\mathrm{avg} &lt; 115\mathrm{GeV}$ . Measurements are obtained using the matrix method and a dijet sample as described in ATLAS-CONF-2015-017. The A14 tune and NNPDF23LO PDF has been used for Powheg+Pythia8 samples shown in red squares, and the Sherpa default tune and CT10 PDF has been used for Sherpa 2.1 samples, in blue triangles. Data is shown as black points. The lower part of the figure shows the ratios between the data and MC relative response. The blue and red dashed lines indicate $1\pm2\%$ and $1\pm5\%$ respectively. This calibration was not applied to trigger jets during the 2015 data taking, but rather derived from HLT jets recorded during that period.	png eps

Relative jet response as a function of the jet pseudorapidity for anti-

HLT jets with R=0.4 calibrated with a dedicated EM+JES scheme, for $85\mathrm{GeV} &lt; p_\mathrm{T}^\mathrm{avg} &lt; 115\mathrm{GeV}$ . Measurements are obtained using the matrix method and a dijet sample as described in ATLAS-CONF-2015-017. The A14 tune and NNPDF23LO PDF has been used for Powheg+Pythia8 samples shown in red squares, and the Sherpa default tune and CT10 PDF has been used for Sherpa 2.1 samples, in blue triangles. Data is shown as black points. The lower part of the figure shows the ratios between the data and MC relative response. The blue and red dashed lines indicate $1\pm2\%$ and $1\pm5\%$ respectively. This calibration was not applied to trigger jets during the 2015 data taking, but rather derived from HLT jets recorded during that period.

png eps

Jet Trigger Efficiency Plots ATL-COM-DAQ-2016-016 (February 26, 2016)

* https://cds.cern.ch/record/2132747

Comparison of central ( $ABS(\eta)&lt;2.8$ ) per-event trigger efficiency turn-on curves between data and MC simulation of dijet events using Pythia 8 for four typical thresholds from the full 2015 dataset. High level trigger (HLT) jets are formed from topo-clusters at the electromagnetic energy scale. The HLT jets are then calibrated to the hadronic scale by first applying a jet-by-jet area subtraction procedure followed by a jet energy scale weighting that is dependent on the HLT jet pt and eta. Each efficiency is determined using events retained with a lower threshold trigger that is found to be fully efficient in the phase space of interest.	png eps pdf
Comparison of forward ( $3.6&lt;ABS(\eta)&lt;4.5$ ) per-event trigger efficiency turn-on curves between data and MC simulation of dijet events using Pythia 8 for four typical thresholds from the full 2015 dataset. High level trigger (HLT) jets are formed from topo-clusters at the electromagnetic energy scale. The HLT jets are then calibrated to the hadronic scale by first applying a jet-by-jet area subtraction procedure followed by a jet energy scale weighting that is dependent on the HLT jet pt and eta. Each efficiency is determined using events retained with a lower threshold trigger that is found to be fully efficient in the phase space of interest.	png eps pdf
Comparison of per-event isolated multi-jet trigger efficiency turn-on curves between data and MC simulation of dijet events using Pythia 8 for four typical threshold-multiplicity combinations from the full 2015 dataset. High level trigger (HLT) jets are formed from topo-clusters at the electromagnetic energy scale. The HLT jets are then calibrated to the hadronic scale by first applying a jet-by-jet area subtraction procedure followed by a jet energy scale weighting that is dependent on the HLT jet pt and eta. N is the number of jets above the specified threshold required to fire the trigger: 4 for HLT_4j45 and HLT_4j85 or 5 for HLT_5j45 and HLT_5j85. Isolation is enforced by requiring each of the N leading jets to be isolated by $\Delta R &gt; 0.6$ from all other reconstructed offline jets with $p_\mathrm{T}&gt;20\mathrm{GeV}$ . Each efficiency is determined using events retained with a lower threshold trigger that is found to be fully efficient in the phase space of interest.	png eps pdf
Comparison of per-event $H_\mathrm{T}$ (scalar sum of jet $p_\mathrm{T}$ for all central jets with $p_\mathrm{T}&gt;50\mathrm{GeV}$ ) trigger efficiency turn-on curves between data and MC simulation of dijet events using Pythia 8 for two typical thresholds from the full 2015 dataset. High level trigger (HLT) jets are formed from topo-clusters at the electromagnetic energy scale. The HLT jets are then calibrated to the hadronic scale by first applying a jet-by-jet area subtraction procedure followed by a jet energy scale weighting that is dependent on the HLT jet pt and eta. Each efficiency is determined using events retained with a lower threshold trigger that is found to be fully efficient in the phase space of interest.	png eps pdf
Comparison of central ( $ABS(\eta)&lt;2.6$ ) per-event trigger efficiency turn-on curves for four typical thresholds from the full 2015 dataset. Level-1 trigger (L1) jets are formed from Regions of Interest (RoIs), of size $0.8\times0.8$ in $\eta\times\phi$ , at the electromagnetic energy scale. Each efficiency is determined using events retained with a lower threshold trigger that is found to be fully efficient in the phase space of interest.	png eps pdf
Comparison of forward ( $3.6&lt;ABS(\eta)&lt;4.5$ ) per-event trigger efficiency turn-on curves for four typical thresholds from the full 2015 dataset. Level-1 trigger (L1) jets are formed from Regions of Interest (RoIs), of size $0.8\times0.8$ in $\eta\times\phi$ , at the electromagnetic energy scale. Each efficiency is determined using events retained with a lower threshold trigger that is found to be fully efficient in the phase space of interest.	png eps pdf
Comparison of per-event isolated multi-jet trigger efficiency turn-on curves for three typical threshold-multiplicity combinations from the full 2015 dataset. Level-1 trigger (L1) jets are formed from Regions of Interest (RoIs), of size $0.8\times0.8$ in $\eta\times\phi$ , at the electromagnetic energy scale. N is the number of jets above the specified threshold required to fire the trigger: 3 for L1_3J40, 4 for L1_4J15, or 6 for L1_6J15. Isolation is enforced by requiring each of the N leading jets to be isolated by $\Delta R &gt; 0.6$ from all other reconstructed offline jets with $p_\mathrm{T}&gt;20\mathrm{GeV}$ . Each efficiency is determined using events retained with a lower threshold trigger that is found to be fully efficient in the phase space of interest.	png eps pdf

Jet Trigger and Data Scouting Performance Plots ATL-COM-DAQ-2016-012 (February 17, 2016)

* https://cds.cern.ch/record/2130838

Transverse momentum of HLT jets compared to offline jets, after correcting HLT jets for pile-up and applying dedicated MC-based jet energy scale correction factors. No data-MC in-situ correction is applied to offline jets. Events are selected using the HLT_j60 single jet triggers. HLT and offline jets are matched using $\Delta R&lt;0.4$ . Each matched jet is required to have HLT and offline $\mathrm{ABS}(\eta)&lt;0.8$ . The calibration is derived for all jets in the detector acceptance.	png eps
Transverse momentum of HLT jets compared to truth particle jets in Monte Carlo simulation, after correcting HLT jets for pile-up and applying dedicated MC-based jet energy scale correction factors. Events are selected using any of the HLT single jet triggers. HLT and truth jets are matched using $\Delta R&lt;0.4$ . Each matched jet is required to have HLT and truth $\mathrm{ABS}(\eta)&lt;0.8$ . The calibration is derived for all jets in the detector acceptance.	png eps
Distribution of the transverse momentum of leading HLT jets recorded in the data scouting stream (triggered by the unprescaled L1_J75 trigger), marked as Data Scouting jets, compared to the distribution of all leading HLT recorded by any of the single jet high level triggers in the main physics stream in a single run. Jets are required to have and $\mathrm{ABS}(\eta)&lt;2.8$ . The large gain in statistics for the data scouting stream starting from $p_\mathrm{T}$ below 400 GeV is due to the absence of prescale factors that are normally applied to the HLT triggers in the standard stream.	png eps

Jet Trigger Performance Plots ATL-COM-DAQ-2015-190 (November 5, 2015)

* http://cds.cern.ch/record/2062992

Trigger rate for the data scouting chain seeded by the Level-1 trigger J75 compared to the lowest unprescaled single jet trigger j360, which selects events where a trigger jet with $p_\mathrm{T}$ > 360 GeV is present, during a time range of a single run.	png eps
Trigger rate for the data scouting chain seeded by the Level-1 trigger J75 compared to the sum of the rates of all prescaled and unprescaled central single jet triggers, during a time range of a single run.  Overlaps in the rate of the single jet triggers are considered negligible.	png eps
Trigger rate for the data scouting chain seeded by the Level-1 trigger J75 compared to the sum of rates of all prescaled and unprescaled single jet triggers, of the jet trigger seeded by the same Level-1 trigger, and to the rate of the lowest unprescaled single jet trigger,  during the time range of a single run.  Overlaps in the rate of the single jet triggers are considered negligible.	png eps

Jet Trigger Performance Plots ATL-COM-DAQ-2015-099 (July 21st, 2015)

* http://cds.cern.ch/record/2034513

Transverse momentum of HLT jets compared to offline jets. Events are selected using the HLT_j100 trigger. HLT and offline jets are matched using $\Delta R = \sqrt{(\Delta\eta^{2} + \Delta \phi^{2})} &lt; 0.3$ . Each matched jet is required to have passed the HLT_j100 selection (HLT jet $p_{T} &gt; 100$ GeV) and to have offline jet $p_{T} &gt; 120$ GeV and $0 &lt; ABS(\eta) &lt; 0.8$ . The offline $p_{T}$ cut ensures the trigger is more than 99% efficient. The HLT jets are corrected for pile-up and have MC-based jet energy scale correction factors applied.	png pdf eps
Transverse momentum response of HLT jets relative to offline jets. Events are selected using the HLT_j100 trigger. HLT and offline jets are matched using $\Delta R = \sqrt{(\Delta\eta^{2} + \Delta \phi^{2})} &lt; 0.3$ . Each matched jet is required to have passed the HLT_j100 selection (HLT jet $p_{T} &gt; 100$ GeV) and to have offline jet $p_{T} &gt; 120$ GeV and $0 &lt; ABS(\eta) &lt; 0.8$ . The offline $p_{T}$ cut ensures the trigger is more than 99% efficient. The HLT jets are corrected for pile-up and have MC-based jet energy scale correction factors applied.	png pdf eps
Example of plots used in the jet trigger monitoring. The energy and azimuthal angle is shown for all HLT jets reconstructed in events retained by any jet trigger, which is useful to understand energy spikes in the calorimeter and are used in data-quality assessment. The HLT jets are corrected for pileup and have MC-based jet energy scale correction factors applied. The jets are recorded if they are above a minimal threshold in transverse momentum. The plot extends beyond φ=π due to the choice of binning in the monitoring histograms.	png pdf eps
Example of jet trigger monitoring. The pseudo-rapidity and azimuthal angle is shown for all HLT jets reconstructed in events retained by any jet trigger, which is useful to understand energy spikes in the calorimeter and are used in data-quality assessment. The HLT jets are corrected for pileup and have MC-based jet energy scale correction factors applied.	png pdf eps
Comparison of per-event trigger efficiency turn-on curves between data and MC simulation using Pythia 8 for three typical thresholds from June 2015. High level trigger (HLT) jets are formed from topo-clusters at the electromagnetic energy scale. The HLT jets are then calibrated to the hadronic scale by first applying a jet-by-jet area subtraction procedure followed by a jet energy scale weighting that is dependent on the HLT jet pt and eta. Each efficiency is determined using events retained with a lower threshold trigger that is found to be fully efficient in the phase space of interest.	png pdf
Assessment of the spatial dependence of the per-jet trigger efficiency for a single high level trigger (HLT) jet threshold of 25 GeV in the central region of the ATLAS calorimeters ( ABS(eta) < 3.2) using data collected in June 2015. The HLT jets are formed from topo-clusters at the electromagnetic energy scale. The jets are then calibrated to the hadronic scale by first applying a jet-by-jet area subtraction procedure followed by a jet energy scale weighting that is dependent on the HLT jet pt and eta. The efficiency is evaluated for an offline jet pT selection of 30 GeV.	png pdf
Assessment of the spatial dependence of the per-jet trigger efficiency for a single high level trigger (HLT) jet threshold of 25 GeV in the forward region of the ATLAS calorimeters (3.2 < ABS(eta) < 4.4) using data collected in June 2015. The HLT jets are formed from topo-clusters at the electromagnetic energy scale. The jets are then calibrated to the hadronic scale by first applying a jet-by-jet area subtraction procedure followed by a jet energy scale weighting that is dependent on the HLT jet pt and eta. The efficiency is evaluated for an offline jet pT selection of 30 GeV.	png pdf

Developments and Performance for LHC Run II

Calorimeter Full Scan versus Partial Scan timing and performance ATL-COM-DAQ-2015-006 (February 9, 2015)

https://cds.cern.ch/record/1987584

Calorimeter Partial Scan data readout scheme: blue dots represent the Level-1 jet positions in a particular simulated event. In the Partial Scan scheme, the ATLAS data acquisition system reads out only the calorimeter data from regions around the Level-1 jet positions, corresponding to the green rectangles in the figure, and prepares three-dimensional clusters (topological clusters) from these data alone. This is followed by a jet finding step using topological clusters as input. Thus the jet finding in the Partial Scan scheme runs as if in a single-pass scan over the full calorimeter, but with the detector data suppressed outside the regions defined by Level-1 jet positions. Contrary to previous Region-by-Region Scan the Partial Scan also removes any overlap between regions. In this example the green boxes have a size of eta x phi = 1x1.	png pdf eps
Number of calorimeter cells read out in the Partial Scan and Full Scan readout schemes used by the jet trigger. The black line represents the Full Scan readout scheme, where the full calorimeter data is read out by the ATLAS data acquisition system. Calorimeter cells read out by the Partial Scan scheme are represented for two alternative settings: the red dotted line corresponds to reading out regions of eta x phi = 1x1 around the Level-1 jet positions and the blue dashed line corresponds to geometrical regions of eta x phi = 1.5x1.5. The data sample used consist of QCD di-jet events with leading-jet transverse momentum above 20 GeV and 40 simultaneous interactions per bunch-crossing.	png pdf eps
Comparison of the time taken to retrieve the calorimeter cells in the Full Scan and the Partial Scan readout schemes used in the jet trigger. The black line represents the full calorimeter readout (Full Scan). The partial calorimeter readout (Partial Scan) is represented for two alternative settings: the red dotted line corresponds to reading out regions of eta x phi = 1x1 around the Level-1 jet positions and the blue dashed line corresponds to geometrical regions of eta x phi = 1.5x1.5 around the Level-1 jets. The data sample used consist of QCD di-jet events with leading-jet transverse momentum above 20 GeV and 40 simultaneous interactions per bunch-crossing. As the Partial Scan has to select the cells to retrieve, the readout may take longer than in Full Scan when the number of cells to retrieve increase, as can be seen in the Partial Scan with regions of 1.5x1.5.	png pdf eps
Processing time for cluster reconstruction using the ATLAS Topological Cluster algorithm, from calorimeter cells read out using the Partial Scan and the Full Scan schemes employed by the jet trigger. The measured times include the clustering, splitting, cluster correction and moments calculation steps. The black line represents the full calorimeter readout (Full Scan), the partial calorimeter readout (Partial Scan) is represented for two alternative settings: the red dotted line corresponds to reading out regions of eta x phi = 1x1 around the Level-1 jet positions and the blue dashed line corresponds to reading out geometrical regions of eta x phi = 1.5x1.5 around the Level-1 jets. The data sample used consist of QCD di-jet events with leading-jet transverse momentum above 20 GeV and a mean of 40 simultaneous interactions per bunch-crossing.	png pdf eps
Jet transverse energy spectrum transposed to the full calorimeter readout (Full Scan) transverse energy scale, E_{T}^{FS}, for High Level Trigger jets matching jets identified by Level-1 (within a radius 1 around the Level-1 jet in the eta x phi plane). The black line represents the Full Scan readout scheme, where all calorimeter cells contribute to jet finding. For the partial calorimeter readout scheme (Partial Scan) the transverse energy of the closest Full Scan jet was used. Partial Scan histograms are represented for two alternative settings: the red dotted line corresponds to reading out regions of eta x phi = 1x1 around the Level-1 jet positions and the blue dashed line corresponds to reading out geometrical regions of eta x phi = 1.5x1.5 around the Level-1 jets. The data sample used consist of QCD di-jet events with leading-jet transverse momentum above 20 GeV and 40 simultaneous interactions per bunch-crossing.	png pdf eps
Ratio between the Partial Scan (PS) and Full Scan (FS) jets, for High Level Trigger jets most closely matching jets identified at Level-1 (within a radius 1 around the Level-1 jet in the eta x phi plane), represented versus the jet transverse energy for Full Scan jets, E_{T}^{FS}. For Partial Scan jets, the transverse energy of the closest Full Scan jet was used to calculate the efficiency. Partial Scan efficiencies are represented for two alternative settings: the red dotted line corresponds to reading out regions of eta x phi = 1x1 around the Level-1 jet positions and the blue dashed line corresponds to reading out geometrical regions of eta x phi = 1.5x1.5 around the Level-1 jets. The statistical error bars represent the square root of the sum of squares of weights. The error bars variation reflects the combination of the three data sets used, each data set represents a different energy region and have different weights. The data sample used consist of QCD di-jet events with leading-jet transverse momentum above 20 GeV and 40 simultaneous interactions per bunch-crossing.	png pdf eps
Relative transverse energy difference between jets reconstructed using the Partial Scan (PS) and Full Scan (FS) calorimeter readout schemes, represented versus the Full Scan jet transverse energy, E_{T}^{FS}, for High Level Trigger jets matching jets identified at Level-1 (within a radius 1 around the Level-1 jet in the eta x phi plane). Partial Scan jets are displayed for two alternative settings: the red dotted line corresponds to reading out regions of eta x phi = 1x1 around the Level-1 jet positions and the blue dashed line corresponds to reading out geometrical regions of eta x phi = 1.5x1.5 around the Level-1 jets. The error bars represent the standard error on the mean. The data sample used consist of QCD di-jet events with leading-jet transverse momentum above 20 GeV and 40 simultaneous interactions per bunch-crossing.	png pdf eps

Phase I Upgrade Performance Plots

Global Feature Extraction (gFEX) Performance Plots ATL-COM-DAQ-2014-087 (August 21, 2014)

https://cds.cern.ch/record/1749167

Per-jet efficiency turn-on curves in Monte Carlo (MC) simulation for multiple Phase I upgrade Level-1 jet trigger options. A global feature extraction (gFEX) reconstruction algorithm (closed red markers, left) from the TDAQ Phase I Upgrade Technical Design Report (TDR) CERN-LHCC-2013-018, ATLAS-TDR-023] with a 140 GeV threshold is compared to full simulation of the Run I Level-1 calorimeter jet trigger (open blue markers, left and right) with a 100 GeV threshold. The gFEX reconstruction implements a simple seeded cone algorithm with a nominal radius of R=1.0 and with a seed selection of 15 GeV applied to calorimeter towers with area 0.2x0.2 in eta-phi. The 140 GeV gFEX trigger threshold is chosen to match the L1_J100 single subjet turn-on curve. Pair-produced top quark MC simulation samples are simulated with a pile-up level equivalent to an average number of interactions per bunch-crossing, or , of 80. For each algorithm, the efficiency curves are shown as a function of the offline trimmed anti-kt R=1.0 jet pT with different offline subjet multiplicities. The trimming parameters specify that any subjets with a pT fraction of the original jet less than 5% are to be discarded. The subjets are defined using the kt clustering algorithm with a nominal radius parameter of D=0.3. For subjet counting, the subjets are required to have a subjet pT>20 GeV. The offline trimmed jets are required to be isolated from any other offline jet by at least a radial distance of DeltaR > 2.0 radians and to be within the pseudorapidity range ABS(eta) < 2.5. The turn-on curves measure per-jet efficiencies after requiring a that the the Level-1 gFEX jet be within DeltaR < 1.0 of the offline trimmed jet.	png pdf eps png pdf eps
Event-level Level-1 trigger efficiency turn-on curves as a function of the offline trimmed jet pT for (left) ttbar events and (right) WH -> lnu+bbbar events. Both processes are simulated with a pile-up level equivalent to an average number of interactions per bunch-crossing, or , of 80. The efficiency curves are shown as a function of the offline trimmed anti-kt R=1.0 jet pT, where the offline trimmed jet is required to have a mass between 100<m<220 GeV (left, top) or 100<m<150 GeV (right, Higgs). The trimming parameters specify that any subjets with a pT fraction of the original jet less than 5% are to be discarded. The subjets are defined using the kt clustering algorithm with a nominal radius parameter of D=0.3. In each case, three trigger selections are shown: (blue open circles) full simulation of the existing Run I Level-1 calorimeter jet trigger with a 100 GeV threshold, (black open squares) full simulation of a sum jet transverse energy (ET) trigger, or HT trigger, with a 200 GeV threshold, formed using Run 1 Level-1 calorimeter jets, (red closed circles) and a Phase I gFEX-based reconstruction algorithm with a 140 GeV threshold CERN-LHCC-2013-018, ATLAS-TDR-023]. The gFEX reconstruction implements a simple seeded cone algorithm with a nominal radius of R=1.0 and with a seed selection of 15 GeV applied to calorimeter towers with area 0.2x0.2 in eta-phi. The 140 GeV gFEX trigger threshold is chosen to match the L1_J100 single subjet turn-on curve.	png pdf eps png pdf eps
Correlation between the offline event energy density (rho) ATLAS-CONF-2013-083] on the horizontal axis and a simplified calculation of the event energy density in the Level-1 calorimeter trigger using the gFEX. (left) The correlation for ttbar events and (right) ZH -> nunu+bbbar events events. Both Monte Carlo simulation samples are simulated with a pile-up level equivalent to an average number of interactions per bunch-crossing () of 80. In each case, the average value of rho measured by the gFEX trigger for a given offline rho is similar. The pseudorapidity range -1.6<ABS(eta)<0.0 is used for the calculation of rho in the trigger, corresponding to the area covered by one of the four FPGAs in the current design. Only calorimeter towers with area 0.2x0.2 in eta-phi and energies less than 6 GeV are used in the calculation. The correlation coefficient is shown in both cases to be greater than 90%.	png pdf eps png pdf eps

2012 pp Data

Jet Trigger Performance Plots ATL-COM-DAQ-2012-210 (January 09, 2013)

* http://cds.cern.ch/record/1498621

[eps]	[eps]	[eps]
The efficiency for L2 full scan at electromagnetic (EM) calibration scale and hadronic (EM+JES) calibration scale scale. The efficiency is plotted as a function of the leading offline jet pT. The minimum jet ET thresholds are 15 GeV for the EM-scale trigger, and 35 GeV for the EM+JES-scale trigger, so that both curves attain 99% efficiency point for same offline jet pT of 60 GeV. The rate for this EM+JES trigger is 18% smaller than the one for the EM trigger.	The efficiency for L1 (0.8x0.8 sliding window) and L2 full scan (anti-kT R=0.4 with trigger towers as inputs) jets to satisfy a six jet trigger (trigger run offline) in events where at least six anti-kT R=0.4 jets have been identified offline with ABS(eta) < 2.8, ET > 30 GeV (these events were pre-selected using a four jet trigger). The efficiency is plotted as a function of the sixth offline jet ET. The L2 full scan uses as input to the anti-kT algorithm all trigger towers read-out at L1, no only the ones that were accepted by the L1 selection, allowing for a reduction of the L1 inefficiency, which is mostly due to the sliding window algorithm.	The efficiency for L2 full scan and L2 partial scan (anti-kT R=0.4) jets to satisfy a six jet trigger (trigger run offline) in events where at least six anti-kT R=0.4 jets have been identified offline with ABS(eta) < 2.8, ET > 30 GeV (these events were pre-selected using a four jet trigger). The efficiency is plotted as a function of the sixth offline jet ET. The L2 full scan uses as input to the anti-kT algorithm the trigger towers from the complete detector. The L2 partial scan used as input to the anti-kT algorithm the calorimeter cells only from those regions of the detector in which significant jet activity was found by the L2 full scan.

2011 PbPb Data

HLT Jet Trigger Performance Plots ATLAS-COM-DAQ-2011-148 (December 7, 2011)

* https://cdsweb.cern.ch/record/1402460

Efficiency of the primary HLT jet trigger used for the 2011 heavy ion run. The efficiency was evaluated using the data from the PbPb collisions at \sqrt{s_{NN}}=2.76 TeV corresponding to an integrated luminosity of 2.4 ub-1. The HLT trigger algorithm is anti-kt R=0.2 with a threshold of ET=20 GeV. The HLT trigger algorithm is seeded by events with total transverse energy greater than 10 GeV identified by the Level 1 trigger. Efficiency is evaluated with respect to offline anti-kt R=0.2 jets. Both the offline and HLT jets are at the electromagnetic scale.	[eps]
Jet position resolution (in pseudorapidity) of the primary HLT jet trigger used for the 2011 heavy ion run. The jet position was evaluated using the data from the PbPb collisions at \sqrt{s_{NN}}=2.76 TeV corresponding to an integrated luminosity of 2.4 ub-1. The HLT trigger algorithm is anti-kt R=0.2 with a threshold of ET=20 GeV. The HLT trigger algorithm is seeded by events with total transverse energy greater than 10 GeV identified by the Level 1 trigger. The jet position resolution is evaluated with respect to offline anti-kt R=0.2 jets.	[eps]

Efficiency of the primary HLT jet trigger used for the 2011 heavy ion run. The efficiency was evaluated using the data from the PbPb collisions at \sqrt{s_{NN}}=2.76 TeV corresponding to an integrated luminosity of 2.4 ub-1. The HLT trigger algorithm is anti-kt R=0.2 with a threshold of ET=20 GeV. The HLT trigger algorithm is seeded by events with total transverse energy greater than 10 GeV identified by the Level 1 trigger. Efficiency is evaluated with respect to offline anti-kt R=0.2 jets. Both the offline and HLT jets are at the electromagnetic scale.

[eps]

Jet position resolution (in pseudorapidity) of the primary HLT jet trigger used for the 2011 heavy ion run. The jet position was evaluated using the data from the PbPb collisions at \sqrt{s_{NN}}=2.76 TeV corresponding to an integrated luminosity of 2.4 ub-1. The HLT trigger algorithm is anti-kt R=0.2 with a threshold of ET=20 GeV. The HLT trigger algorithm is seeded by events with total transverse energy greater than 10 GeV identified by the Level 1 trigger. The jet position resolution is evaluated with respect to offline anti-kt R=0.2 jets.

[eps]

2011 pp Data @ 7 TeV

Jet trigger performance plots : ATLAS jet trigger plots on 2011 performance ATL-COM-DAQ-2013-082 (September 20, 2013)

* ATL-COM-DAQ-2013-082

Figure 1: Efficiency for various Level-1 trigger chains in data and Pythia and Herwig Monte Carlo, calculated using the bootstrap method. For data, the efficiency is computed with respect to events taken by an independent trigger 100% efficient in the relevant region.	[pdf]
Figure 2: Efficiency for various Level-2 trigger chains in data and Pythia and Herwig Monte Carlo, calculated using the bootstrap method. For data, the efficiency is computed with respect to events taken by a Level-1 trigger 100% efficient in the relevant region.	[pdf]
Figure 3: Efficiency for various Event Filter trigger chains in data and Pythia and Herwig Monte Carlo, calculated using the bootstrap method. For data, the efficiency is computed with respect to events taken by a Level-2 trigger 100% efficient in the relevant region.	[pdf]
Figure 4: Efficiency for low-pT Event Filter trigger chains in data and Pythia and Herwig Monte Carlo, calculated using the bootstrap method. For data, the efficiency is computed with respect to events taken by a Level-2 trigger 100% efficient in the relevant region. In thsese chains, events from the prescaled random trigger are input directly to Event Filter, without having to pass L1 nor L2, allowing to considerably lower the threshold below those of full efficiency of the previous levels.	[pdf]
Figure 5: Efficiency for high-pT Event Filter trigger chains in data and Pythia and Herwig Monte Carlo, calculated using the bootstrap method. For data, the efficiency is computed with respect to events taken by a Level-2 trigger 100% efficient in the relevant region. These Event-Filter chains are all seeded by the same Level-1 and Level-2 combination.	[pdf]
Figure 6: Offset between transverse energies of jets at Event Filter (where jet energies are the sum of the electromagnetic and hadronic components) and offline (where a proper compensation for hadronic energy is applied) as a funciton of the offline jet pseudorapidity, for jets with offline pT > 100 GeV	[pdf]
Figure 7: Transverse energy resolution as a function of the jet offline pseudo-rapidity for jets with offline pT > 100 GeV	[pdf]
Figure 8: Offset between transverse energies of jets at Event Filter (where jet energies are the sum of the electromagnetic and hadronic components) and offline (where a proper compensation for hadronic energy is applied) as a funciton of the offline jet transverse momentum, for jets with offline pseudo-rapidity between 0 and 0.75	[pdf]
Figure 9: Transverse energy resolution as a function of jet offline transverse momentum, for jets with offline pseudo-rapidity between 0 and 0.75	[pdf]

L1.5 Jet Trigger Plots ATL-COM-DAQ-2013-045 (June 25, 2013)

* ATL-COM-DAQ-2013-045

Ratio of the per-jet L1.5 trigger efficiency as a function of the minimum separation between any two offline anti-kt jets having ET>40 GeV. Only statistical uncertainties are shown. The rise at low DeltaR indicates that L1.5 jet finding was more efficient than L1 jet finding for event configurations with several nearby jets. The result is independent of the tower size. The trigger was re-run offline on a sample collected with a random trigger. Jet finding at L1 used a sliding window algorithm on 0.2x0.2 towers. The L1.5 jet trigger ran within L2 but used either 0.1x0.1 or 0.2x0.2 towers from L1 and applied the anti-kt jet algorithm.	[ps]
Jet resolution in eta for trigger jets as compared to geometrically-matched offline anti-kt jets. Only statistical uncertainties are shown. The trigger was re-run offline on a sample collected with a random trigger. Jet finding at L1 used a sliding window algorithm on 0.2x0.2 towers. The L1.5 jet trigger ran within L2 but used either 0.1x0.1 or 0.2x0.2 towers from L1 and applied the anti-kt jet algorithm. Jet finding at L2 used the cone jet algorithm but with the full calorimeter segmentation.	[ps]

Ratio of the per-jet L1.5 trigger efficiency as a function of the minimum separation between any two offline anti-kt jets having ET>40 GeV. Only statistical uncertainties are shown. The rise at low DeltaR indicates that L1.5 jet finding was more efficient than L1 jet finding for event configurations with several nearby jets. The result is independent of the tower size. The trigger was re-run offline on a sample collected with a random trigger. Jet finding at L1 used a sliding window algorithm on 0.2x0.2 towers. The L1.5 jet trigger ran within L2 but used either 0.1x0.1 or 0.2x0.2 towers from L1 and applied the anti-kt jet algorithm.

[ps]

Jet resolution in eta for trigger jets as compared to geometrically-matched offline anti-kt jets. Only statistical uncertainties are shown. The trigger was re-run offline on a sample collected with a random trigger. Jet finding at L1 used a sliding window algorithm on 0.2x0.2 towers. The L1.5 jet trigger ran within L2 but used either 0.1x0.1 or 0.2x0.2 towers from L1 and applied the anti-kt jet algorithm. Jet finding at L2 used the cone jet algorithm but with the full calorimeter segmentation.

[ps]

L1.5 Jet Trigger Performance with 2011 Data ATL-COM-DAQ-2012-009 (March 18, 2012)

* ATL-COM-DAQ-2012-009

Schematic illustrating the jet trigger implementation in 2011. Jets were identified at Level 1 (L1) using 0.2x0.2 towers (nominal) with a sliding windows algorithm. In the original scheme, jets were found at Level 2 (L2) with a simple cone jet algorithm, seeded by the L1 jets (region-of-interest or ROI), using noise-suppressed calorimeter cells. An unseeded anti-kT jet algorithm was run over topological clusters formed from calorimeter cells at Event Filter (EF) independent of any jets found at L1 or L2. In the new scheme (added during the 2011 Heavy Ion run), jets could also be found at L2 with an unseeded anti-kT jet algorithm using either the 0.1x0.1 (egamma/tau) or 0.2x0.2 (jet/Etmiss) towers from the L1 calorimeter system. These L1.5 jets could be used in L2 trigger decisions and could seed L2 ROI-based jet finding.	[ps]
The time taken to read out the tower information from the Level 1 calorimeter readout system. The black solid line shows the time taken to read out 0.2x0.2 (jet/Etmiss) towers and the blue dashed line shows the time for 0.1x0.1 (egamma/tau) towers. The timing was measured during 2011 Pb-Pb collisions at sqrt{s_{NN}}=2.76 TeV. The nominal time limit at this stage of the trigger is about 40 ms.	[eps]
The time taken to find jets using the anti-kT jet algorithm with a distance parameter R=0.4. The black solid line shows the time taken for 0.2x0.2 (jet/Etmiss) towers and the blue dashed line shows the time for 0.1x0.1 (egamma/tau) towers. The timing was measured during 2011 Pb-Pb collisions at sqrt{s_{NN}}=2.76 TeV. The nominal time limit at this stage of the trigger is about 40 ms.	[eps]
The jet position resolution (in pseudorapidity) of the L1, L1.5 and L2 jet triggers in 2011 proton-proton collisions (trigger run offline). The jet finding algorithm for L1 is a 0.8x0.8 sliding window, for L1.5 it is anti-kT with R=0.4 and for L2 a three-iteration cone R=0.4 seeded by a L1 jet. The jet position resolution is evaluated with respect to offline anti-kT R=0.4 jets. The offset toward high Delta eta observed at L1 is an artefact of how L1 position is recorded.	[eps]
The efficiency for L1 (0.8x0.8 sliding window) and L1.5 (anti-kT R=0.4) jets to satisfy a six jet trigger (trigger run offline) in events where at least six anti-kT R=0.4 jets have been identified offline with ABS(eta) <2.8, ET>30 GeV (these events were pre-selected using a four jet trigger). The efficiency is plotted as a function of the sixth offline jet ET.	[eps]

Jet Trigger Performance 2011 Data ATLAS-COM-DAQ-2011-063 (October 10, 2011)

* http://cdsweb.cern.ch/record/1374159

#EffEFLowComp The efficiency for anti-kt jets with R=0.4 to satisfy the Event Filter (EF) inclusive jet trigger for three choices of threshold. The EF-jet conditions were applied to random-triggered events. The efficiency is plotted as a function of the offline calibrated jet ET for jets with central rapidities and in two different data-taking scenarios: before (empty markers) and after (full markers) pile-up noise suppression was applied to EF-jets. The overall 99% efficiency point improved by ~5 GeV by suppressing the pile-up noise. (Jet energies in the trigger are measured at the electromagnetic scale.)	[eps]
The efficiency for anti-kt jets with R=0.4 to satisfy the Event Filter (EF) inclusive jet trigger for three choices of threshold. The EF-jet conditions were applied to random-triggered events. The efficiency is plotted as a function of the offline calibrated jet ET for jets with central rapidities. The figure is identical to the one shown in Fig.~ #EffEFLowComp, but only the results with pile-up noise suppression are shown. (Jet energies in the trigger are measured at the electromagnetic scale.)	[eps]
#EffEF40Comp The efficiency for anti-kt jets with R=0.4 to satisfy Level 1 (L1), Level 2 (L2), and the Event Filter (EF) inclusive jet trigger for a single L1->L2->EF trigger chain. Different thresholds are applied at each level of the trigger to increase rejection of events while keeping acceptance for events with high probability of satisfying the overall jet trigger. The efficiency is plotted as a function of the offline calibrated jet ET for jets with central rapidities and in two different data-taking scenarios: before (empty markers) and after (full markers) pile-up noise suppression was applied to both L2- and EF-jets. The overall 99\% efficiency point improved by ~5 GeV by suppressing the pile-up noise. The shift due to noise suppression is larger at L2 than at EF since the EF jets are based on topological clusters of calorimeter cells and already included some noise suppression. (Jet energies in the trigger are measured at the electromagnetic scale.)	[eps]
The efficiency for anti-kt jets with R=0.4 to satisfy Level 1 (L1), Level 2 (L2), and the Event Filter (EF) inclusive jet trigger for a single L1->L2->EF trigger chain. Different thresholds are applied at each level of the trigger to increase rejection of events while keeping acceptance for events with high probability of satisfying the overall jet trigger. The efficiency is plotted as a function of the offline calibrated jet ET for jets with central rapidities. The figure is identical to the one shown in Fig. #EffEF40Comp, but only the results with pile-up noise suppression are shown. (Jet energies in the trigger are measured at the electromagnetic scale.)	[eps]
#EffEF55Comp The efficiency for anti-kt jets with R=0.4 to satisfy Level 1 (L1), Level 2 (L2), and the Event Filter (EF) inclusive jet trigger for a single L1->L2->EF trigger chain. Different thresholds are applied at each level of the trigger to increase rejection of events while keeping acceptance for events with high probability of satisfying the overall jet trigger. The efficiency is plotted as a function of the offline calibrated jet ET for jets with forward rapidities and in two different data-taking scenarios: before (empty markers) and after (full markers) pile-up noise suppression was applied to both L2- and EF-jets. The overall 99\% efficiency point improved by ~2 GeV by suppressing the pile-up noise. The shift due to noise suppression is larger at L2 than at EF since the EF jets are based on topological clusters of calorimeter cells and already included some noise suppression. (Jet energies in the trigger are measured at the electromagnetic scale.)	[eps]
The efficiency for anti-kt jets with R=0.4 to satisfy Level 1 (L1), Level 2 (L2), and the Event Filter (EF) inclusive jet trigger for a single L1->L2->EF trigger chain. Different thresholds are applied at each level of the trigger to increase rejection of events while keeping acceptance for events with high probability of satisfying the overall jet trigger. The efficiency is plotted as a function of the offline calibrated jet ET for jets with forward rapidities. The figure is identical to the one shown in Fig. #EffEF55Comp, but only the results with pile-up noise suppression are shown. (Jet energies in the trigger are measured at the electromagnetic scale.)	[eps]
#EffEF75Comp The efficiency for anti-kt jets with R=0.4 to satisfy Level 1 (L1), Level 2 (L2), and the Event Filter (EF) inclusive jet trigger for a single L1->L2->EF trigger chain. Different thresholds are applied at each level of the trigger to increase rejection of events while keeping acceptance for events with high probability of satisfying the overall jet trigger. The efficiency is plotted as a function of the offline calibrated jet ET for jets with central rapidities and in two different data-taking scenarios: before (empty markers) and after (full markers) pile-up noise suppression was applied to both L2- and EF-jets. The overall 99\% efficiency point improved by ~5 GeV by suppressing the pile-up noise. The shift due to noise suppression is larger at L2 than at EF since the EF jets are based on topological clusters of calorimeter cells and already included some noise suppression. (Jet energies in the trigger are measured at the electromagnetic scale.)	[eps]
The efficiency for anti-kt jets with R=0.4 to satisfy Level 1 (L1), Level 2 (L2), and the Event Filter (EF) inclusive jet trigger for a single L1->L2->EF trigger chain. Different thresholds are applied at each level of the trigger to increase rejection of events while keeping acceptance for events with high probability of satisfying the overall jet trigger. The efficiency is plotted as a function of the offline calibrated jet ET for jets with central rapidities. The figure is identical to the one shown in Fig. #EffEF75Comp, but only the results with pile-up noise suppression are shown. (Jet energies in the trigger are measured at the electromagnetic scale.)	[eps]
The efficiency for anti-kt jets with R=0.4 to satisfy the Event Filter (EF) inclusive jet trigger for five choices of threshold. The EF-jet conditions were applied to events selected with lower-ET jet triggers. The efficiency is plotted as a function of the offline calibrated jet ET for jets with central rapidities. These data correspond to the period when pile-up noise was suppressed during L2 and EF jet finding. (Jet energies in the trigger are measured at the electromagnetic scale.)	[eps]

2010 pp Data @ 7 TeV

Performance of the ATLAS Jet Trigger in the Early √s = 7 TeV Data ATLAS-CONF-2010-094 (September 24, 2010)

* http://cdsweb.cern.ch/record/1299109

The efficiency for anti-kT jets with R=0.4 to satisfy the Level 1 trigger with a 5 GeV threshold as a function of the calibrated offline jet pT. Trigger jets were evaluated at electromagnetic scale. The efficiency was extracted using a minimum bias trigger. Data are indicated by the closed black circles; results from the Pythia simulation are represented by the open red circles.	[png] [eps]
The efficiency for anti-kT jets with R=0.4 to satisfy the Level 1 trigger with a 5 GeV threshold as a function of the offline jet eta for jets with calibrated pT between 40 and 60 GeV. Trigger jets were evaluated at electromagnetic scale. The efficiency was extracted using a minimum bias trigger. Data are indicated by the closed black circles; results from the Pythia simulation are represented by the open red circles. The dips in the efficiency near ABS(eta) =1.5 are in the transition region between the barrel and end-cap calorimeters.	[png] [eps]
The efficiency for anti-kT jets with R=0.4 to satisfy the Level 1 trigger with a 5 GeV threshold as a function of the offline jet eta for jets with calibrated pT above 60 GeV. Trigger jets were evaluated at electromagnetic scale. The efficiency was extracted using a minimum bias trigger. Data are indicated by the closed black circles; results from the Pythia simulation are represented by the open red circles.	[png] [eps]
The efficiency for anti-kT jets with R=0.4 to satisfy the Level 1 trigger with a 15 GeV threshold as a function of the calibrated offline jet pT. Trigger jets were evaluated at electromagnetic scale. The efficiency was extracted using a minimum bias trigger. Data are indicated by the closed black circles; results from the Pythia simulation are represented by the open red circles.	[png] [eps]
The efficiency for an event with at least three anti-kT jets reconstructed with R=0.4, plotted as a function of the calibrated pT of the third jet, to have fired a Level 1 trigger that required at least three Level 1 jets which satisfied 5 GeV thresholds. Trigger jets were evaluated at electromagnetic scale. The efficiency was extracted using a minimum bias trigger. Data are indicated by the closed black circles; results from the Pythia simulation are represented by the open red circles.	[png] [eps]
Delta eta between an offline anti-kT R=0.4 jet and the nearest Level 2 trigger jet. Data are indicated by the points; results from the Pythia dijet simulation are represented by the yellow filled histogram. The High Level Trigger, which includes Level 2, was not needed to reduce the rate for this data set and did not contribute to the trigger decision.	[png] [eps]
The efficiency for anti-kT jets with R=0.4 to satisfy the Level 2 trigger with a 30 GeV threshold as a function of the calibrated offline jet p_T. Trigger jets were evaluated at electromagnetic scale. No Level 1 trigger requirement was applied for this distribution. Data are indicated by the closed black circles; results from the Pythia simulation are represented by the open red circles. The High Level Trigger, which includes Level 2, was not needed to reduce the rate for this data set and did not contribute to the trigger decision.	[png] [eps]

Measurement of inclusive jet and dijet cross sections in proton-proton collision data at 7 TeV centre-of-mass energy using the ATLAS detector ATLAS-CONF-2011-047 (March 20, 2011)

* http://cdsweb.cern.ch/record/1338578

Fig. 2a: Combined L1+L2 jet trigger efficiency as a function of reconstructed jet pT for anti-kt jets with R = 0.6 in the central region ABS(y) < 0.3 (a) and barrel-endcap transition region 1.2 < ABS(y) < 2.1 (b), shown for different L2 trigger thresholds. The trigger thresholds are at the electromagnetic scale, while the jet pT is at the calibrated scale (see Sec. 6.3). The highest trigger chain does not apply a threshold at L2, so its L1 threshold is listed.	[eps]
Fig. 2b: Combined L1+L2 jet trigger efficiency as a function of reconstructed jet pT for anti-kt jets with R = 0.6 in the central region ABS(y) < 0.3 (a) and barrel-endcap transition region 1.2 < ABS(y) < 2.1 (b), shown for different L2 trigger thresholds. The trigger thresholds are at the electromagnetic scale, while the jet pT is at the calibrated scale (see Sec. 6.3). The highest trigger chain does not apply a threshold at L2, so its L1 threshold is listed.	[eps]
Fig. 3a: Combined L1+L2 jet trigger efficiency as a function of reconstructed jet pT for anti-kt jets with R = 0.6 in the HEC-FCal transition region 2.8 < ABS(y) < 3.6 (a) and FCal region 3.6 < ABS(y) < 4.4 (b), shown for various L2 trigger thresholds. Due to the presence of a dead FCal trigger tower, which spans 0.9% of the (eta;phi)-acceptance, the efficiency is not expected to reach 100%. This inefficiency is accounted for in the measurement.	[eps]
Fig. 3b: Combined L1+L2 jet trigger efficiency as a function of reconstructed jet pT for anti-kt jets with R = 0.6 in the HEC-FCal transition region 2.8 < ABS(y) < 3.6 (a) and FCal region 3.6 < ABS(y) < 4.4 (b), shown for various L2 trigger thresholds. Due to the presence of a dead FCal trigger tower, which spans 0.9% of the (eta;phi)-acceptance, the efficiency is not expected to reach 100%. This inefficiency is accounted for in the measurement.	[eps]

Search for New Physics in Dijet Mass and Angular Distributions in pp Collisions at sqrt(s) = 7 TeV Measured with the ATLAS Detector New J. Phys. 13 (2011) 053044 (March 20, 2011)

* http://cdsweb.cern.ch/record/1337270

The efficiency for passing the primary first-level trigger as a function of the dijet invariant mass, mjj. The uncertainties are statistical.	[eps]
The invariant mass of the leading two reconstructed jets for events passing L1_J55 (red) and L1_J95 (black), after requiring the pt of the leading jet to be greater than 150 GeV.	[eps]
The efficiency for passing L1_J95 using L1_J55 as the baseline. We plot the ratio of events passing L1_J55 and L1_J95 to events passing L1_J55. Note the y-axis starts from 95%.	[eps]
Trigger mjj efficiency curves, for the highest threshold triggers used for the angular analysis: Periods A-F L1 J95.	[eps]
Trigger mjj efficiency curves, for the highest threshold triggers used for the angular analysis: Periods G-I EF L1J95 NoAlg.	[eps]

Measurement of Jet Mass and Substructure for Inclusive Jets in √s = 7 TeV pp Collisions with the ATLAS Experiment ATLAS-CONF-2011-073 (May 20, 2011)

* http://cdsweb.cern.ch/record/1352454

J95 trigger efficiency (per event) for anti-kt R=0.6 jets using the J30 trigger as a reference. The results are compared to Pythia QCD dijet Monte Carlo samples without pile-up and with in-time pile-up overlaid with the signal interaction.	[eps]
J95 trigger efficiency (per event) for anti-kt R=1.0 jets using the J30 trigger as a reference. The results are compared to Pythia QCD dijet Monte Carlo samples without pile-up and with in-time pile-up overlaid with the signal interaction.	[eps]
J95 trigger efficiency (per event) for Cambridge-Aachen R=1.2 jets using the J30 trigger as a reference. The results are compared to Pythia QCD dijet Monte Carlo samples without pile-up and with in-time pile-up overlaid with the signal.	[eps]
J95 trigger efficiency (per event) for Cambridge-Aachen R=1.2 jets after splitting and filtering using the J30 trigger as a reference. The results are compared to Pythia QCD dijet Monte Carlo samples without pile-up and with in-time pile-up overlaid with the signal interaction.	[eps]
J95 using J30 as a reference, per event efficiencies for events with an anti-kt R=0.6 jet with pT > 300 GeV. The efficiency of the trigger is shown as a function of mass.	[eps]
J95 using J30 as a reference, per event efficiencies for events with an anti-kt R=1.0 jet with pT > 300 GeV. The efficiency of the trigger is shown as a function of mass.	[eps]
J95 using J30 as a reference, per event efficiencies for events with an Cambridge-Aachen R=1.2 jet with pT > 300 GeV. The efficiency of the trigger is shown as a function of mass.	[eps]
J95 using J30 as a reference, per event efficiencies for events with an Cambridge-Aachen R=1.2 jet after splitting and filtering with pT > 300 GeV. The efficiency of the trigger is shown as a function of mass.	[eps]

Outdated

Inclusive Jet Trigger Efficiencies for the Early 2011 Data ATLAS-COM-DAQ-2011-031 (May 27, 2011)

* http://cdsweb.cern.ch/record/1351823

The efficiency for anti-kt jets with R=0.4 to satisfy the Level 1 (L1), Level 2 (L2), and Event Filter (EF) inclusive jet trigger for a single L1->L2->EF trigger chain. Different thresholds are applied at each level of the trigger to increase rejection of events while keeping acceptance for events with high probability of satisfying the overall jet trigger. The efficiency is plotted as a function of the offline calibrated jet ET for jets with forward rapidities. (Jet energies are measured at electromagnetic scale in the trigger.)	[eps]
The efficiency for anti-kt jets with R=0.4 to satisfy the Event Filter (EF) inclusive jet trigger for three choices of threshold. The EF-jet conditions were applied to random-triggered events. The efficiency is plotted as a function of the offline calibrated jet ET for jets with central rapidities. (Jet energies are measured at electromagnetic scale in the trigger.)	[eps]
The efficiency for anti-kt jets with R=0.4 to satisfy the Event Filter (EF) inclusive jet trigger for three choices of threshold. The EF-jet conditions were applied to random-triggered events. The efficiency is plotted as a function of the offline calibrated jet ET for jets with forward rapidities. (Jet energies are measured at electromagnetic scale in the trigger.)	[eps]
The efficiency for anti-kt jets with R=0.4 to satisfy the Event Filter (EF) inclusive jet trigger for three choices of threshold. The EF-jet conditions were applied to events selected with lower-ET jet triggers. The efficiency is plotted as a function of the offline calibrated jet ET for jets with central rapidities. (Jet energies are measured at electromagnetic scale in the trigger.) The efficiency for the Level 1 (L1) and Level 2 (L2) thresholds are shown in <ahref="/twiki/pub/AtlasPublic/JetTriggerPublicResults/EF_j10_j15_j20.eps">[eps] together with the efficiency for the 100 GeV EF threshold.	[eps]
The efficiency for anti-kt jets with R=0.4 to satisfy the Event Filter (EF) inclusive jet trigger binned in the number of reconstructed $pp$ interactions in the triggered events. The EF-jet conditions were applied to events selected with a lower ET jet trigger (shown in Fig. [eps]). The efficiency is plotted as a function of the offline calibrated jet E_T for jets with forward rapidities where the effects of multiple overlapping $pp$ interactions are expected to be greatest. (Jet energies are measured at electromagnetic scale in the trigger.)	[eps]
The efficiency for anti-kt jets with R=0.4 to satisfy the Level 1 (L1), Level 2 (L2), and Event Filter (EF) inclusive jet trigger for a single L1->L2->EF trigger chain. Different thresholds are applied at each level of the trigger to increase rejection of events while keeping acceptance for events with high probability of satisfying the overall jet trigger. The efficiency is plotted as a function of the offline calibrated jet ET for jets with central rapidities. (Jet energies are measured at electromagnetic scale in the trigger.)	[eps]
The efficiency for anti-kt jets with R=0.4 to satisfy the Level 1 (L1), Level 2 (L2), and Event Filter (EF) inclusive jet trigger for a single L1->L2->EF trigger chain. Different thresholds are applied at each level of the trigger to increase rejection of events while maintaining acceptance for events with high probability of satisfying the overall jet trigger. The efficiency is plotted as a function of the offline calibrated jet ET for jets with central rapidities. (Jet energies are measured at electromagnetic scale in the trigger.)	[eps]

Measurement of inclusive jet and dijet cross sections in proton-proton collisions at 7 TeV centre-of-mass energy with the ATLAS detector EPJC 71 (2011) 1 (September 27, 2010)

* http://cdsweb.cern.ch/record/1295801

Inclusive-jet L1 trigger efficiency as a function of reconstructed jet pT for jets identified using the anti-kt algorithm with R = 0.4.	[eps]
Inclusive-jet L1 trigger efficiency as a function of reconstructed jet pT for jets identified using the anti-kt algorithm with R = 0.4.	[eps]

Early 2010 pp 7 TeV data

Et distribution of Level 1 trigger jets at the electromagnetic scale, for events selected in a single ATLAS run from April 2010 by the Minimum Bias trigger, expected to be 100% efficient for jetevents. The three colours represent jets selected by the three lowest trigger thresholds of 5, 10 and 15 GeV, respectively.	[gif]
Pseudorapidity difference between Level 1 trigger and offline jets, reconstructed with the AntiKt algorithm (cone size 0.6) from a single run taken by ATLAS in April 2010. The three colours represent the three lowest jet trigger thresholds of 5, 10 and 15 GeV at the electromagnetic scale	[gif]
Absolute value of the azimuthal angle difference between Level 1 trigger and offline jets, reconstructed with the AntiKt algorithm (cone size 0.6) from a single run taken by ATLAS in April 2010. The three colours represent the three lowest jet trigger thresholds of 5, 10 and 15 GeV at the electromagnetic scale. The rise for values close to π corresponds to the second jet (all combinations of offline and trigger jets are included)	[gif]

Performance of the ATLAS jet trigger with p-p collisions at sqrt(s)=900 GeV ATLAS-CONF-2010-028 (May 17, 2010)

* http://cdsweb.cern.ch/record/1277660

L1 Efficiency for the trigger selecting jets above 5 counts (~ 5 GeV) as a function of the offline jet pT at the EM scale. The turn-on is shown for data (black triangle points) and MC simulations (red circle points). The following fit function has been used: The fit parameter for MC: μ = (12.6 + 0.7) GeV, σ = (3.6 + 0.4) GeV, ε = 0.96 + 0.02.	[jpg]

L1 Efficiency for the trigger selecting jets above 5 counts (~ 5 GeV) as a function of the offline jet pT at the EM scale. The turn-on is shown for data (black triangle points) and MC simulations (red circle points). The following fit function has been used:

The fit parameter for MC:
μ = (12.6 + 0.7) GeV, σ = (3.6 + 0.4) GeV, ε = 0.96 + 0.02.

[jpg]

L1 Efficiency for the trigger selecting jets above 10 counts (~ 10 GeV) as a function of the offline jet pT at the EM scale. The turn-on is shown for data (black triangle points) and MC simulations (red circle points). The following fit function has been used: The fit parameter for MC: μ = (18.3 + 1.0) GeV, σ = (3.6 + 0.4) GeV, ε = 0.92 + 0.03.	[jpg]

L1 Efficiency for the trigger selecting jets above 10 counts (~ 10 GeV) as a function of the offline jet pT at the EM scale. The turn-on is shown for data (black triangle points) and MC simulations (red circle points). The following fit function has been used:

The fit parameter for MC:
μ = (18.3 + 1.0) GeV, σ = (3.6 + 0.4) GeV, ε = 0.92 + 0.03.

[jpg]

L1 Efficiency for the trigger selecting Electromagnetic clusters in the forward region ( ABS(pseudorapidity) >3.2) above 3 counts (~ 3 GeV) as a function of the raw offline cluster transverse energy. The turn-on is shown for data (black triangle points) and MC simulations (red circle points).	[gif]

L1 Efficiency for the trigger selecting Electromagnetic clusters in the forward region ( ABS(pseudorapidity) >3.2) above 3 counts (~ 3 GeV) as a function of the raw offline cluster transverse energy. The turn-on is shown for data (black triangle points) and MC simulations (red circle points).

[gif]

Major updates:
-- StevenSchramm - 26-Feb-2016
-- DavidMiller - 21-July-2015
-- DavideGerbaudo - 09-Jan-2013
-- MichaelBegel - 31-May-2011

Responsible: Main.StevenSchramm
Subject: public

Attachments

Topic attachments
I	Attachment	History	Action	Size	Date	Who	Comment
eps	2016-02-26-EtaIntercalibration.eps	r1	manage	28.3 K	2016-02-26 - 13:19	StevenSchramm
png	2016-02-26-EtaIntercalibration.png	r1	manage	90.2 K	2016-02-26 - 13:19	StevenSchramm
eps	2016-02-26-HLT_HT.eps	r1	manage	21.2 K	2016-02-26 - 13:25	StevenSchramm
pdf	2016-02-26-HLT_HT.pdf	r1	manage	20.9 K	2016-02-26 - 13:25	StevenSchramm
png	2016-02-26-HLT_HT.png	r1	manage	133.7 K	2016-02-26 - 13:26	StevenSchramm
eps	2016-02-26-HLT_central.eps	r1	manage	33.3 K	2016-02-26 - 13:24	StevenSchramm
pdf	2016-02-26-HLT_central.pdf	r1	manage	21.0 K	2016-02-26 - 13:24	StevenSchramm
png	2016-02-26-HLT_central.png	r1	manage	27.8 K	2016-02-26 - 13:24	StevenSchramm
eps	2016-02-26-HLT_forward.eps	r1	manage	26.5 K	2016-02-26 - 13:25	StevenSchramm
pdf	2016-02-26-HLT_forward.pdf	r1	manage	18.3 K	2016-02-26 - 13:25	StevenSchramm
png	2016-02-26-HLT_forward.png	r1	manage	29.9 K	2016-02-26 - 13:25	StevenSchramm
eps	2016-02-26-HLT_multi.eps	r1	manage	22.9 K	2016-02-26 - 13:26	StevenSchramm
pdf	2016-02-26-HLT_multi.pdf	r1	manage	18.5 K	2016-02-26 - 13:26	StevenSchramm
png	2016-02-26-HLT_multi.png	r1	manage	28.1 K	2016-02-26 - 13:27	StevenSchramm
eps	2016-02-26-L1_central.eps	r1	manage	17.8 K	2016-02-26 - 13:27	StevenSchramm
pdf	2016-02-26-L1_central.pdf	r1	manage	17.5 K	2016-02-26 - 13:27	StevenSchramm
png	2016-02-26-L1_central.png	r1	manage	28.7 K	2016-02-26 - 13:27	StevenSchramm
eps	2016-02-26-L1_forward.eps	r1	manage	17.0 K	2016-02-26 - 13:27	StevenSchramm
pdf	2016-02-26-L1_forward.pdf	r1	manage	16.8 K	2016-02-26 - 13:27	StevenSchramm
png	2016-02-26-L1_forward.png	r1	manage	29.8 K	2016-02-26 - 13:27	StevenSchramm
eps	2016-02-26-L1_multi.eps	r1	manage	15.4 K	2016-02-26 - 13:27	StevenSchramm
pdf	2016-02-26-L1_multi.pdf	r1	manage	16.3 K	2016-02-26 - 13:27	StevenSchramm
png	2016-02-26-L1_multi.png	r1	manage	24.7 K	2016-02-26 - 13:27	StevenSchramm
pdf	2016-08-01_HLT_central.pdf	r1	manage	28.5 K	2016-08-01 - 22:18	StevenSchramm
png	2016-08-01_HLT_central.png	r1	manage	98.1 K	2016-08-01 - 22:19	StevenSchramm
pdf	2016-08-01_HLT_forward.pdf	r1	manage	18.9 K	2016-08-01 - 22:19	StevenSchramm
png	2016-08-01_HLT_forward.png	r1	manage	110.0 K	2016-08-01 - 22:19	StevenSchramm
pdf	2016-08-01_HLT_multi.pdf	r1	manage	18.5 K	2016-08-01 - 22:19	StevenSchramm
png	2016-08-01_HLT_multi.png	r1	manage	121.8 K	2016-08-01 - 22:19	StevenSchramm
pdf	2016-08-01_L1_central.pdf	r1	manage	24.7 K	2016-08-01 - 22:20	StevenSchramm
png	2016-08-01_L1_central.png	r1	manage	108.2 K	2016-08-01 - 22:20	StevenSchramm
pdf	2016-08-01_L1_forward.pdf	r1	manage	19.2 K	2016-08-01 - 22:20	StevenSchramm
png	2016-08-01_L1_forward.png	r1	manage	104.7 K	2016-08-01 - 22:20	StevenSchramm
pdf	2016-08-01_L1_multi.pdf	r1	manage	19.2 K	2016-08-01 - 22:20	StevenSchramm
png	2016-08-01_L1_multi.png	r1	manage	96.0 K	2016-08-01 - 22:20	StevenSchramm
pdf	2016-09-20-top-5j65.pdf	r2 r1	manage	18.4 K	2016-09-20 - 08:04	StevenSchramm
png	2016-09-20-top-5j65.png	r1	manage	30.7 K	2016-09-20 - 00:14	StevenSchramm
pdf	2016-09-20-top-6j45.pdf	r2 r1	manage	17.4 K	2016-09-20 - 08:05	StevenSchramm
png	2016-09-20-top-6j45.png	r1	manage	29.0 K	2016-09-20 - 00:14	StevenSchramm
pdf	2016-11-29-gsc.pdf	r1	manage	125.8 K	2016-11-29 - 11:45	StevenSchramm
png	2016-11-29-gsc.png	r1	manage	125.4 K	2016-11-29 - 11:50	StevenSchramm
pdf	2017-02-14-LargeR-DiJet.pdf	r1	manage	18.8 K	2017-02-14 - 22:17	StevenSchramm
png	2017-02-14-LargeR-DiJet.png	r1	manage	169.3 K	2017-02-14 - 22:17	StevenSchramm
pdf	2017-02-14-LargeR-SingleJet.pdf	r1	manage	17.9 K	2017-02-14 - 22:17	StevenSchramm
png	2017-02-14-LargeR-SingleJet.png	r1	manage	161.0 K	2017-02-14 - 22:17	StevenSchramm
pdf	2017-07-04-HLT_largeR_multi_m.pdf	r1	manage	15.9 K	2017-07-04 - 22:09	WillKalderon
png	2017-07-04-HLT_largeR_multi_m.png	r1	manage	271.6 K	2017-07-04 - 22:09	WillKalderon
pdf	2017-07-04-HLT_largeR_single_pt.pdf	r1	manage	17.4 K	2017-07-04 - 22:09	WillKalderon
png	2017-07-04-HLT_largeR_single_pt.png	r1	manage	294.1 K	2017-07-04 - 22:09	WillKalderon
pdf	2017-07-04-HLT_multi.pdf	r1	manage	15.0 K	2017-07-04 - 22:09	WillKalderon
png	2017-07-04-HLT_multi.png	r1	manage	227.7 K	2017-07-04 - 22:09	WillKalderon
pdf	2017-07-04-HLT_single.pdf	r1	manage	19.3 K	2017-07-04 - 22:09	WillKalderon
png	2017-07-04-HLT_single.png	r1	manage	294.3 K	2017-07-04 - 22:09	WillKalderon
pdf	2017-07-04_HLT_largeR_multi_pt.pdf	r1	manage	18.7 K	2017-07-04 - 22:09	WillKalderon
png	2017-07-04_HLT_largeR_multi_pt.png	r1	manage	304.3 K	2017-07-04 - 22:09	WillKalderon
pdf	2017-22-10-HLT_largeR_SC.pdf	r1	manage	129.2 K	2017-10-22 - 07:53	AlexMartyniuk
png	2017-22-10-HLT_largeR_SC.png	r1	manage	359.8 K	2017-10-22 - 07:53	AlexMartyniuk
pdf	2017-22-10-HLT_largeR_SlidingWindow.pdf	r1	manage	132.9 K	2017-10-22 - 07:53	AlexMartyniuk
png	2017-22-10-HLT_largeR_SlidingWindow.png	r1	manage	359.0 K	2017-10-22 - 07:53	AlexMartyniuk
pdf	2017-22-10-HLT_multi.pdf	r1	manage	115.1 K	2017-10-22 - 07:53	AlexMartyniuk
png	2017-22-10-HLT_multi.png	r1	manage	276.8 K	2017-10-22 - 07:53	AlexMartyniuk
pdf	2017-22-10-HLT_single.pdf	r1	manage	161.9 K	2017-10-22 - 07:53	AlexMartyniuk
png	2017-22-10-HLT_single.png	r1	manage	400.3 K	2017-10-22 - 07:54	AlexMartyniuk
eps	DS_vs_HLTj_approved.eps	r1	manage	30.4 K	2015-11-05 - 23:31	StevenSchramm
png	DS_vs_HLTj_approved.png	r1	manage	57.6 K	2015-11-05 - 23:31	StevenSchramm
eps	DS_vs_all_approved.eps	r1	manage	41.1 K	2015-11-05 - 23:32	StevenSchramm
png	DS_vs_all_approved.png	r1	manage	73.7 K	2015-11-05 - 23:32	StevenSchramm
eps	DS_vs_j360_approved.eps	r1	manage	30.0 K	2015-11-05 - 23:09	StevenSchramm
png	DS_vs_j360_approved.png	r1	manage	57.4 K	2015-11-05 - 23:09	StevenSchramm
eps	EF_FJ30_fj50_fj55.eps	r1	manage	14.7 K	2011-05-31 - 21:13	MichaelBegel
png	EF_FJ30_fj50_fj55.png	r1	manage	31.1 K	2011-05-31 - 21:14	MichaelBegel
eps	EF_J50_j70_j75.eps	r1	manage	14.1 K	2011-05-31 - 21:15	MichaelBegel
png	EF_J50_j70_j75.png	r1	manage	29.9 K	2011-05-31 - 21:15	MichaelBegel
eps	EF_J75_j95_j100.eps	r1	manage	13.8 K	2011-05-31 - 20:53	MichaelBegel
png	EF_J75_j95_j100.png	r1	manage	30.6 K	2011-05-31 - 20:53	MichaelBegel
eps	EF_fj10_fj15_fj20.eps	r1	manage	14.7 K	2011-05-31 - 21:08	MichaelBegel
png	EF_fj10_fj15_fj20.png	r1	manage	27.5 K	2011-05-31 - 21:08	MichaelBegel
eps	EF_fj55_vertex.eps	r1	manage	15.2 K	2011-05-31 - 21:13	MichaelBegel
png	EF_fj55_vertex.png	r1	manage	30.2 K	2011-05-31 - 21:13	MichaelBegel
eps	EF_j100_j135_j180.eps	r1	manage	17.1 K	2011-05-31 - 21:14	MichaelBegel
png	EF_j100_j135_j180.png	r1	manage	32.2 K	2011-05-31 - 21:14	MichaelBegel
eps	EF_j10_j15_j20.eps	r1	manage	15.3 K	2011-05-31 - 21:14	MichaelBegel
png	EF_j10_j15_j20.png	r1	manage	28.4 K	2011-05-31 - 21:14	MichaelBegel
pdf	EFhigh.pdf	r1	manage	38.2 K	2013-09-20 - 23:45	MarkSutton
png	EFhigh.png	r2 r1	manage	57.5 K	2013-09-20 - 23:42	MarkSutton
pdf	EFlow.pdf	r1	manage	26.0 K	2013-09-20 - 23:45	MarkSutton
png	EFlow.png	r2 r1	manage	44.4 K	2013-09-20 - 23:42	MarkSutton
pdf	EFmedium.pdf	r1	manage	42.5 K	2013-09-20 - 23:45	MarkSutton
png	EFmedium.png	r2 r1	manage	54.0 K	2013-09-20 - 23:42	MarkSutton
eps	Edited_L1J20_PS_dataLoad_Scheme_mc11_8TeV_JZ4W.eps	r1	manage	3386.6 K	2015-03-10 - 13:07	RicardoGoncalo	PS vs FS
pdf	Edited_L1J20_PS_dataLoad_Scheme_mc11_8TeV_JZ4W.pdf	r1	manage	14.7 K	2015-03-10 - 13:07	RicardoGoncalo	PS vs FS
png	Edited_L1J20_PS_dataLoad_Scheme_mc11_8TeV_JZ4W.png	r1	manage	11.5 K	2015-03-10 - 13:07	RicardoGoncalo	PS vs FS
eps	EffEF40.eps	r1	manage	16.5 K	2011-10-10 - 14:40	MichaelBegel
png	EffEF40.png	r1	manage	31.4 K	2011-10-10 - 14:41	MichaelBegel
eps	EffEF40Comp.eps	r1	manage	22.3 K	2011-10-10 - 14:36	MichaelBegel
png	EffEF40Comp.png	r1	manage	42.8 K	2011-10-10 - 14:37	MichaelBegel
eps	EffEF55.eps	r1	manage	17.4 K	2011-10-10 - 14:46	MichaelBegel
png	EffEF55.png	r1	manage	30.8 K	2011-10-10 - 14:47	MichaelBegel
eps	EffEF55Comp.eps	r1	manage	23.4 K	2011-10-10 - 14:47	MichaelBegel
png	EffEF55Comp.png	r1	manage	39.3 K	2011-10-10 - 14:47	MichaelBegel
eps	EffEF75.eps	r1	manage	20.3 K	2011-10-10 - 14:49	MichaelBegel
png	EffEF75.png	r1	manage	36.8 K	2011-10-10 - 14:50	MichaelBegel
eps	EffEF75Comp.eps	r1	manage	27.7 K	2011-10-10 - 14:50	MichaelBegel
png	EffEF75Comp.png	r1	manage	49.9 K	2011-10-10 - 14:50	MichaelBegel
eps	EffEFHigh.eps	r1	manage	21.4 K	2011-10-10 - 15:01	MichaelBegel
png	EffEFHigh.png	r1	manage	43.5 K	2011-10-10 - 15:01	MichaelBegel
eps	EffEFLow.eps	r1	manage	13.7 K	2011-10-10 - 14:29	MichaelBegel
png	EffEFLow.png	r1	manage	28.1 K	2011-10-10 - 14:29	MichaelBegel
eps	EffEFLowComp.eps	r1	manage	19.5 K	2011-10-10 - 13:11	MichaelBegel
png	EffEFLowComp.png	r1	manage	41.2 K	2011-10-10 - 13:12	MichaelBegel
eps	Effi_v3.eps	r1	manage	17.0 K	2011-12-07 - 14:24	MichaelBegel
png	Effi_v3.png	r1	manage	14.2 K	2011-12-07 - 14:24	MichaelBegel
pdf	HLT_j25_320eta490_Preliminary.pdf	r1	manage	36.5 K	2015-07-22 - 01:42	DavidMiller	Jet trigger performance plots (ATL-COM-DAQ-2015-099: https://cds.cern.ch/record/2034513)
png	HLT_j25_320eta490_Preliminary.png	r1	manage	138.2 K	2015-07-22 - 01:42	DavidMiller	Jet trigger performance plots (ATL-COM-DAQ-2015-099: https://cds.cern.ch/record/2034513)
pdf	HLT_j25_Preliminary.pdf	r1	manage	50.3 K	2015-07-22 - 01:42	DavidMiller	Jet trigger performance plots (ATL-COM-DAQ-2015-099: https://cds.cern.ch/record/2034513)
png	HLT_j25_Preliminary.png	r1	manage	147.6 K	2015-07-22 - 01:42	DavidMiller	Jet trigger performance plots (ATL-COM-DAQ-2015-099: https://cds.cern.ch/record/2034513)
pdf	HLT_j60_j150_j360_Preliminary.pdf	r1	manage	48.1 K	2015-07-22 - 01:42	DavidMiller	Jet trigger performance plots (ATL-COM-DAQ-2015-099: https://cds.cern.ch/record/2034513)
png	HLT_j60_j150_j360_Preliminary.png	r1	manage	101.1 K	2015-07-22 - 01:42	DavidMiller	Jet trigger performance plots (ATL-COM-DAQ-2015-099: https://cds.cern.ch/record/2034513)
eps	JPR_v3.eps	r1	manage	11.2 K	2011-12-07 - 14:24	MichaelBegel
png	JPR_v3.png	r2 r1	manage	24.1 K	2011-12-07 - 14:30	MichaelBegel
pdf	L1J100_ATLASprelim.pdf	r1	manage	18.0 K	2017-10-22 - 22:28	AlexMartyniuk
png	L1J100_ATLASprelim.png	r2 r1	manage	96.3 K	2017-10-22 - 22:51	AlexMartyniuk
eps	L1J20_CellMaker_ContainerSize_mc12_14TeV_JZ123W_mu40.eps	r1	manage	10.5 K	2015-03-10 - 13:07	RicardoGoncalo	PS vs FS
pdf	L1J20_CellMaker_ContainerSize_mc12_14TeV_JZ123W_mu40.pdf	r1	manage	14.6 K	2015-03-10 - 13:07	RicardoGoncalo	PS vs FS
png	L1J20_CellMaker_ContainerSize_mc12_14TeV_JZ123W_mu40.png	r1	manage	20.7 K	2015-03-10 - 13:07	RicardoGoncalo	PS vs FS
eps	L1J20_CellMaker_TotalTime_mc12_14TeV_JZ123W_mu40.eps	r1	manage	9.1 K	2015-03-10 - 13:07	RicardoGoncalo	PS vs FS
pdf	L1J20_CellMaker_TotalTime_mc12_14TeV_JZ123W_mu40.pdf	r1	manage	14.0 K	2015-03-10 - 13:07	RicardoGoncalo	PS vs FS
png	L1J20_CellMaker_TotalTime_mc12_14TeV_JZ123W_mu40.png	r1	manage	19.3 K	2015-03-10 - 13:07	RicardoGoncalo	PS vs FS
eps	L1J20_ClusterMaker_TotalTime_mc12_14TeV_JZ123W_mu40.eps	r1	manage	10.4 K	2015-03-10 - 13:24	RicardoGoncalo	FS vs PS
pdf	L1J20_ClusterMaker_TotalTime_mc12_14TeV_JZ123W_mu40.pdf	r1	manage	14.5 K	2015-03-10 - 13:24	RicardoGoncalo	FS vs PS
png	L1J20_ClusterMaker_TotalTime_mc12_14TeV_JZ123W_mu40.png	r1	manage	20.5 K	2015-03-10 - 13:24	RicardoGoncalo	FS vs PS
eps	L1J20_focalJets_EtFSscaleRatio_mc12_14TeV_JZ123W_mu40.eps	r1	manage	14.3 K	2015-03-10 - 13:24	RicardoGoncalo	FS vs PS
pdf	L1J20_focalJets_EtFSscaleRatio_mc12_14TeV_JZ123W_mu40.pdf	r1	manage	16.4 K	2015-03-10 - 13:24	RicardoGoncalo	FS vs PS
png	L1J20_focalJets_EtFSscaleRatio_mc12_14TeV_JZ123W_mu40.png	r1	manage	19.3 K	2015-03-10 - 13:24	RicardoGoncalo	FS vs PS
eps	L1J20_focalJets_EtFSscale_mc12_14TeV_JZ123W_mu40.eps	r1	manage	12.3 K	2015-03-10 - 13:24	RicardoGoncalo	FS vs PS
pdf	L1J20_focalJets_EtFSscale_mc12_14TeV_JZ123W_mu40.pdf	r1	manage	14.8 K	2015-03-10 - 13:24	RicardoGoncalo	FS vs PS
png	L1J20_focalJets_EtFSscale_mc12_14TeV_JZ123W_mu40.png	r1	manage	19.4 K	2015-03-10 - 13:24	RicardoGoncalo	FS vs PS
eps	L1J20_tot_deltaEtvsEt_focalJets_mc12_14TeV_JZ123W_mu40.eps	r1	manage	13.3 K	2015-03-10 - 13:38	RicardoGoncalo	FS vs FS
pdf	L1J20_tot_deltaEtvsEt_focalJets_mc12_14TeV_JZ123W_mu40.pdf	r1	manage	15.7 K	2015-03-10 - 13:38	RicardoGoncalo	FS vs FS
png	L1J20_tot_deltaEtvsEt_focalJets_mc12_14TeV_JZ123W_mu40.png	r1	manage	20.8 K	2015-03-10 - 13:38	RicardoGoncalo	FS vs FS
pdf	L1SC111_ATLASprelim.pdf	r1	manage	17.9 K	2017-10-22 - 22:28	AlexMartyniuk
png	L1SC111_ATLASprelim.png	r2 r1	manage	97.8 K	2017-10-22 - 22:50	AlexMartyniuk
eps	L1_subjet_combined_prelim.eps	r1	manage	23.7 K	2018-07-05 - 18:13	WillKalderon
pdf	L1_subjet_combined_prelim.pdf	r1	manage	56.5 K	2018-07-05 - 18:13	WillKalderon
png	L1_subjet_combined_prelim.png	r1	manage	206.7 K	2018-07-05 - 18:13	WillKalderon
pdf	L1medium.pdf	r1	manage	31.4 K	2013-09-20 - 23:57	MarkSutton
png	L1medium.png	r2 r1	manage	55.8 K	2013-09-20 - 23:45	MarkSutton
pdf	L2medium.pdf	r1	manage	41.5 K	2013-09-20 - 23:57	MarkSutton
png	L2medium.png	r2 r1	manage	55.4 K	2013-09-20 - 23:45	MarkSutton
eps	WH_inclusive_L1HT_G_higgs.eps	r1	manage	19.0 K	2014-08-21 - 09:05	DavidMiller
pdf	WH_inclusive_L1HT_G_higgs.pdf	r1	manage	16.7 K	2014-08-21 - 09:05	DavidMiller
png	WH_inclusive_L1HT_G_higgs.png	r1	manage	20.1 K	2014-08-21 - 09:05	DavidMiller
eps	ZH_seed15_noise0_signal6_digitization125_gFEX_rho_1_correlation.eps	r1	manage	5767.5 K	2014-08-21 - 09:08	DavidMiller
pdf	ZH_seed15_noise0_signal6_digitization125_gFEX_rho_1_correlation.pdf	r1	manage	118.8 K	2014-08-21 - 09:08	DavidMiller
png	ZH_seed15_noise0_signal6_digitization125_gFEX_rho_1_correlation.png	r1	manage	146.4 K	2014-08-21 - 09:08	DavidMiller
eps	a4tcemsubjesFS_E_vs_phi.eps	r1	manage	41.0 K	2015-09-06 - 20:14	DavidMiller
pdf	a4tcemsubjesFS_E_vs_phi.pdf	r1	manage	46.0 K	2015-09-06 - 20:13	DavidMiller
png	a4tcemsubjesFS_E_vs_phi.png	r1	manage	148.6 K	2015-09-06 - 20:13	DavidMiller
eps	a4tcemsubjesFS_E_vs_phi_black.eps	r1	manage	40.9 K	2015-09-06 - 20:14	DavidMiller
pdf	a4tcemsubjesFS_E_vs_phi_black.pdf	r1	manage	45.9 K	2015-09-06 - 20:13	DavidMiller
png	a4tcemsubjesFS_E_vs_phi_black.png	r1	manage	148.3 K	2015-09-06 - 20:13	DavidMiller
eps	a4tcemsubjesFS_phi_vs_eta.eps	r1	manage	43.4 K	2015-09-06 - 20:14	DavidMiller
pdf	a4tcemsubjesFS_phi_vs_eta.pdf	r1	manage	55.4 K	2015-09-06 - 20:13	DavidMiller
png	a4tcemsubjesFS_phi_vs_eta.png	r1	manage	86.8 K	2015-09-06 - 20:13	DavidMiller
eps	a4tcemsubjesFS_phi_vs_eta_black.eps	r1	manage	43.3 K	2015-09-06 - 20:14	DavidMiller
pdf	a4tcemsubjesFS_phi_vs_eta_black.pdf	r1	manage	55.4 K	2015-09-06 - 20:13	DavidMiller
png	a4tcemsubjesFS_phi_vs_eta_black.png	r1	manage	87.6 K	2015-09-06 - 20:13	DavidMiller
eps	central_6J10_efficiency.eps	r1	manage	10.5 K	2012-03-19 - 12:57	MichaelBegel
png	central_6J10_efficiency.png	r1	manage	11.5 K	2012-03-19 - 12:58	MichaelBegel
pdf	eff_PT_6j60_j007_p016_JETM1_smallR_TBP_ATLASprelim.pdf	r1	manage	15.7 K	2017-10-22 - 22:28	AlexMartyniuk
png	eff_PT_6j60_j007_p016_JETM1_smallR_TBP_ATLASprelim.pdf.png	r1	manage	55.5 K	2017-10-22 - 22:37	AlexMartyniuk
png	eff_PT_6j60_j007_p016_JETM1_smallR_TBP_ATLASprelim.png	r2 r1	manage	76.1 K	2017-10-22 - 22:49	AlexMartyniuk
eps	eff_PT_6j70_data17-data18.eps	r1	manage	17.3 K	2018-05-30 - 23:23	WillKalderon
pdf	eff_PT_6j70_data17-data18.pdf	r1	manage	17.0 K	2018-05-30 - 23:23	WillKalderon
png	eff_PT_6j70_data17-data18.png	r1	manage	26.7 K	2018-05-30 - 23:23	WillKalderon
eps	eff_PT_j420_data17-data18.eps	r1	manage	14.4 K	2018-05-30 - 23:23	WillKalderon
pdf	eff_PT_j420_data17-data18.pdf	r1	manage	16.2 K	2018-05-30 - 23:23	WillKalderon
png	eff_PT_j420_data17-data18.png	r1	manage	26.2 K	2018-05-30 - 23:23	WillKalderon
pdf	eff_PT_j450_j007_p016_JETM1_smallR_TBP_ATLASprelim.pdf	r1	manage	19.0 K	2017-10-22 - 22:28	AlexMartyniuk
png	eff_PT_j450_j007_p016_JETM1_smallR_TBP_ATLASprelim.png	r2 r1	manage	106.4 K	2017-10-22 - 22:49	AlexMartyniuk
eps	eff_PT_j460_a10_data17.eps	r1	manage	14.5 K	2018-05-30 - 23:23	WillKalderon
pdf	eff_PT_j460_a10_data17.pdf	r1	manage	16.6 K	2018-05-30 - 23:23	WillKalderon
png	eff_PT_j460_a10_data17.png	r1	manage	26.0 K	2018-05-30 - 23:23	WillKalderon
eps	eff_PT_singleLargeRmass_data17_prelim.eps	r1	manage	15.5 K	2018-07-05 - 18:13	WillKalderon
pdf	eff_PT_singleLargeRmass_data17_prelim.pdf	r1	manage	60.4 K	2018-07-05 - 18:13	WillKalderon
png	eff_PT_singleLargeRmass_data17_prelim.png	r1	manage	202.9 K	2018-07-05 - 18:13	WillKalderon
eps	em15had35.eps	r1	manage	11.9 K	2013-01-09 - 19:19	DavideGerbaudo
png	em15had35.png	r1	manage	17.7 K	2013-01-09 - 19:28	DavideGerbaudo
eps	inclusive_L1HT_G_top.eps	r1	manage	21.6 K	2014-08-21 - 09:04	DavidMiller
pdf	inclusive_L1HT_G_top.pdf	r1	manage	19.4 K	2014-08-21 - 09:04	DavidMiller
png	inclusive_L1HT_G_top.png	r1	manage	20.2 K	2014-08-21 - 09:04	DavidMiller
eps	l2psSumm_6j.eps	r1	manage	10.5 K	2013-01-09 - 19:19	DavideGerbaudo
png	l2psSumm_6j.png	r1	manage	18.6 K	2013-01-09 - 19:19	DavideGerbaudo
eps	l2psSumm_6j10l1.eps	r1	manage	9.0 K	2013-01-09 - 19:19	DavideGerbaudo
png	l2psSumm_6j10l1.png	r1	manage	14.7 K	2013-01-09 - 19:19	DavideGerbaudo
eps	matched_trig_jets_pt_2d_efficientTriggerHLT_j100_binned_prelim.eps	r1	manage	17.1 K	2015-09-06 - 20:14	DavidMiller
pdf	matched_trig_jets_pt_2d_efficientTriggerHLT_j100_binned_prelim.pdf	r1	manage	45.2 K	2015-09-06 - 20:13	DavidMiller
png	matched_trig_jets_pt_2d_efficientTriggerHLT_j100_binned_prelim.png	r1	manage	20.8 K	2015-09-06 - 20:14	DavidMiller
pdf	offset-vs-eta.pdf	r1	manage	100.8 K	2013-09-20 - 23:46	MarkSutton
png	offset-vs-eta.png	r1	manage	58.6 K	2013-09-20 - 23:46	MarkSutton
pdf	offset-vs-pt.pdf	r1	manage	86.0 K	2013-09-20 - 23:45	MarkSutton
eps	one_d_eta_central.eps	r1	manage	21.0 K	2012-03-19 - 12:58	MichaelBegel
png	one_d_eta_central.png	r1	manage	30.0 K	2012-03-19 - 12:58	MichaelBegel
eps	perjet_L1_G_bysubjet.eps	r1	manage	35.9 K	2014-08-21 - 09:04	DavidMiller
pdf	perjet_L1_G_bysubjet.pdf	r1	manage	25.4 K	2014-08-21 - 09:04	DavidMiller
png	perjet_L1_G_bysubjet.png	r1	manage	21.5 K	2014-08-21 - 09:04	DavidMiller
eps	perjet_L1_bysubjet.eps	r1	manage	24.6 K	2014-08-21 - 09:04	DavidMiller
pdf	perjet_L1_bysubjet.pdf	r1	manage	21.1 K	2014-08-21 - 09:04	DavidMiller
png	perjet_L1_bysubjet.png	r1	manage	20.4 K	2014-08-21 - 09:04	DavidMiller
pdf	resolution-vs-eta.pdf	r1	manage	102.5 K	2013-09-20 - 23:46	MarkSutton
pdf	resolution-vs-pt.pdf	r1	manage	87.5 K	2013-09-20 - 23:46	MarkSutton
png	schematic.png	r1	manage	107.9 K	2012-03-19 - 12:46	MichaelBegel
ps	schematic.ps	r1	manage	369.8 K	2012-03-19 - 12:47	MichaelBegel
eps	time_fastjet_comparison.eps	r1	manage	9.0 K	2012-03-19 - 12:59	MichaelBegel
png	time_fastjet_comparison.png	r1	manage	10.1 K	2012-03-19 - 12:59	MichaelBegel
eps	time_l1_unpack_comparison.eps	r1	manage	10.0 K	2012-03-19 - 12:59	MichaelBegel
png	time_l1_unpack_comparison.png	r1	manage	10.5 K	2012-03-19 - 12:59	MichaelBegel
eps	trig_over_reco_pt_mean_efficientTriggerHLT_j100_binned_prelim.eps	r1	manage	23.2 K	2015-09-06 - 20:14	DavidMiller
pdf	trig_over_reco_pt_mean_efficientTriggerHLT_j100_binned_prelim.pdf	r1	manage	50.2 K	2015-09-06 - 20:13	DavidMiller
png	trig_over_reco_pt_mean_efficientTriggerHLT_j100_binned_prelim.png	r1	manage	20.3 K	2015-09-06 - 20:14	DavidMiller
eps	ttbar_seed15_noise0_signal6_digitization125_gFEX_rho_1_correlation.eps	r1	manage	5764.0 K	2014-08-21 - 09:08	DavidMiller
pdf	ttbar_seed15_noise0_signal6_digitization125_gFEX_rho_1_correlation.pdf	r1	manage	120.7 K	2014-08-21 - 09:08	DavidMiller
png	ttbar_seed15_noise0_signal6_digitization125_gFEX_rho_1_correlation.png	r1	manage	146.8 K	2014-08-21 - 09:08	DavidMiller

Topic revision: r38 - 2018-07-05 - WillKalderon

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