# Public Jet Trigger Plots for Collision Data

## 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 pT, 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. 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 pT, 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.

## 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 pT, with trimming parameters fcut = 0.05 and Rsub = 0.2, for 2017 data. Offline jets are selected with ABS(η) < 2.2. In red (circles) is a trigger with an ET threshold of 460 GeV, while in blue (squares) is a trigger with an ET 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 ET 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 fcut = 0.04 for trigger jets, while the standard offline selection uses fcut = 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]..

### 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 pT, with trimming parameters fcut=0.05 and Rsub=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 fcut=0.04 for trigger jets, while the standard offline selection uses fcut=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].

### 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 pT for large-radius jets with different numbers of subjets, using 2017 data. The first Level-1 trigger requires ET > 100 GeV in a Δη × Δφ window of 0.8×0.8. The second is a Level-1 topological trigger requiring Σ ET > 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 ETi > 15 GeV, Σjin i ETj 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].. Efficiencies for a Level-1 trigger requiring in a window of 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]. Efficiencies for a Level-1 topological trigger requiring 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, window of ) i satisfying , for all jet ROIs j within a radius 1.0. The threshold of 111 GeV was chosen to give equal rate to the 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].

### 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 . 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 . Together, these additional corrections allow for improved agreement between the scale of trigger and offline jets as a function of both and , and thus the trigger efficiency rises much more rapidly. [1] ATLAS-CONF-2015-002, [2] ATLAS-CONF-2015-017. 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 . 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 . Together, these additional corrections allow for improved agreement between the scale of trigger and offline jets as a function of both and , and thus the trigger efficiency rises more rapidly. Only statistical uncertainties are shown. [1] ATLAS-CONF-2015-002, [2] ATLAS-CONF-2015-017.

### 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 . 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 , and thus the trigger efficiency rises much more rapidly. [1] ATLAS-CONF-2015-002, [2] ATLAS-CONF-2015-017. 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 . 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.

## 2016 pp Data

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

 Efficiencies for HLT large-R single-jet triggers are shown as a function of the leading offline trimmed [1] jet for jets with . 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 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 threshold for an equivalent rate while retaining nearly all signal-like jets with a mass of above . 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, is used for trigger jets, while the standard offline selection uses . 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]. Efficiencies for HLT large-R di-jet triggers are shown as a function of the second leading offline trimmed [1] jet for jets with . 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 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 threshold for an equivalent rate while retaining nearly all signal-like jets with a mass of above . 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, is used for trigger jets, while the standard offline selection uses . 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].

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

 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 . 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 , and thus the trigger efficiency rises much more rapidly. The trigger with updated corrections was added for commissioning during summer 2016.

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

 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 and (HLT) and 4 jets with and (Level 1). The offline event selection is defined to mimic all-hadronic decays of a top-antitop pair, requiring 5 jets with and the 6th jets with , all jets having , 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 cuts applied in the online and offline event selection. 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 and (HLT) and 4 jets with and (Level 1). The offline event selection is defined to mimic all-hadronic decays of a top-antitop pair, requiring 5 jets with and the 6th jets with , all jets having . 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 cuts applied in the online and offline event selection.

## 2015 pp Data

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

 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 . 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 and respectively. This calibration was not applied to trigger jets during the 2015 data taking, but rather derived from HLT jets recorded during that period.

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

 Comparison of central () 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. Comparison of forward () 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. 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 from all other reconstructed offline jets with . 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. Comparison of per-event (scalar sum of jet for all central jets with ) 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. Comparison of central () 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 in , 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. Comparison of forward () 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 in , 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. 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 in , 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 from all other reconstructed offline jets with . 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.

### 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 . Each matched jet is required to have HLT and offline . 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 . Each matched jet is required to have HLT and truth . 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 . The large gain in statistics for the data scouting stream starting from 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 > 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)

 Transverse momentum of HLT jets compared to offline jets. Events are selected using the HLT_j100 trigger. HLT and offline jets are matched using . Each matched jet is required to have passed the HLT_j100 selection (HLT jet GeV) and to have offline jet GeV and . The offline 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 . Each matched jet is required to have passed the HLT_j100 selection (HLT jet GeV) and to have offline jet GeV and . The offline 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

## Phase I Upgrade Performance Plots

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

 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 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

## 2012 pp Data

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

 [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)

 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)

 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)

 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)

 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)

 #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)

 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)

 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)

 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)

 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)

 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 [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)

 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)

 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 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]

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

Responsible: Main.StevenSchramm
Subject: public

Topic attachments
I Attachment History Action Size Date Who Comment
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
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
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 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 CharlesWilliamKalderon
png 2017-07-04-HLT_largeR_multi_m.png r1 manage 271.6 K 2017-07-04 - 22:09 CharlesWilliamKalderon
pdf 2017-07-04-HLT_largeR_single_pt.pdf r1 manage 17.4 K 2017-07-04 - 22:09 CharlesWilliamKalderon
png 2017-07-04-HLT_largeR_single_pt.png r1 manage 294.1 K 2017-07-04 - 22:09 CharlesWilliamKalderon
pdf 2017-07-04-HLT_multi.pdf r1 manage 15.0 K 2017-07-04 - 22:09 CharlesWilliamKalderon
png 2017-07-04-HLT_multi.png r1 manage 227.7 K 2017-07-04 - 22:09 CharlesWilliamKalderon
pdf 2017-07-04-HLT_single.pdf r1 manage 19.3 K 2017-07-04 - 22:09 CharlesWilliamKalderon
png 2017-07-04-HLT_single.png r1 manage 294.3 K 2017-07-04 - 22:09 CharlesWilliamKalderon
pdf 2017-07-04_HLT_largeR_multi_pt.pdf r1 manage 18.7 K 2017-07-04 - 22:09 CharlesWilliamKalderon
png 2017-07-04_HLT_largeR_multi_pt.png r1 manage 304.3 K 2017-07-04 - 22:09 CharlesWilliamKalderon
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 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
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
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 CharlesWilliamKalderon
pdf L1_subjet_combined_prelim.pdf r1 manage 56.5 K 2018-07-05 - 18:13 CharlesWilliamKalderon
png L1_subjet_combined_prelim.png r1 manage 206.7 K 2018-07-05 - 18:13 CharlesWilliamKalderon
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 CharlesWilliamKalderon
pdf eff_PT_6j70_data17-data18.pdf r1 manage 17.0 K 2018-05-30 - 23:23 CharlesWilliamKalderon
png eff_PT_6j70_data17-data18.png r1 manage 26.7 K 2018-05-30 - 23:23 CharlesWilliamKalderon
eps eff_PT_j420_data17-data18.eps r1 manage 14.4 K 2018-05-30 - 23:23 CharlesWilliamKalderon
pdf eff_PT_j420_data17-data18.pdf r1 manage 16.2 K 2018-05-30 - 23:23 CharlesWilliamKalderon
png eff_PT_j420_data17-data18.png r1 manage 26.2 K 2018-05-30 - 23:23 CharlesWilliamKalderon
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 CharlesWilliamKalderon
pdf eff_PT_j460_a10_data17.pdf r1 manage 16.6 K 2018-05-30 - 23:23 CharlesWilliamKalderon
png eff_PT_j460_a10_data17.png r1 manage 26.0 K 2018-05-30 - 23:23 CharlesWilliamKalderon
eps eff_PT_singleLargeRmass_data17_prelim.eps r1 manage 15.5 K 2018-07-05 - 18:13 CharlesWilliamKalderon
pdf eff_PT_singleLargeRmass_data17_prelim.pdf r1 manage 60.4 K 2018-07-05 - 18:13 CharlesWilliamKalderon
png eff_PT_singleLargeRmass_data17_prelim.png r1 manage 202.9 K 2018-07-05 - 18:13 CharlesWilliamKalderon
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
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 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
Topic revision: r38 - 2018-07-05 - CharlesWilliamKalderon

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