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Missing Energy Trigger Public Results

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. Follow the guidelines on the trigger public results page.

13 TeV Run 3 studies

April 2021

Background rejection vs. signal efficiency curves computed. More details on how to interpret the plots can be found in arXiv:2005.09554. The background rate on the y-axis is evaluated with respect to the Run 2 L1_XE50 trigger item, and obtained from a trigger reprocessing of the Run 2 data offline. The rates are taken from a special Enhanced Bias run, where details of the weighting scheme can be found in ATL-DAQ-PUB-2016-002. The signal efficiency is evaluated using TTbar Monte Carlo samples, where the true $E_T^{\rm miss}$ from neutrinos and muons is required to be greater than 150 GeV. The curve labeled tcpufit refers to the calorimeter only algorithm using topoclusters (arXiv:1603.02934) used as baseline at the end of Run 2, while other curves correspond to algorithms incorporating tracking. The pfopufit algorithm uses the same techniques as tcpufit, but the input topological clusters are modified to use Particle Flow Objects (PFOs) using tracks (arXiv:1703.10485). The pfopufit algorithm is a variant of the tcpufit algorithm using input PFOs instead of topoclusters. As well as the improved momentum resolution of the PFOs, the vertex information provided by the charged PFOs is used to improve the categorisation of deposits into hard-scatter and pile-up. The algorithm mhtpufit modifies the tcpufit algorithm by utilizing the jets in the event, and applying the Jet Vertex Tagger (JVT) discriminant to remove pileup jets (arXiv:1510.03823). The mhtpufit algorithm uses jets passing JVT selections to define hard-scatter areas of the event. The variant mhtpufit_em uses jets at the electromagnetic scale while mhtpufit_pf uses particle flow jets. A variant of the tcpufit algorithm is then used to estimate the contribution of pileup to these jets.
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Background rejection vs. signal efficiency curves computed. More details on how to interpret the plots can be found in arXiv:2005.09554. The background rate on the y-axis is evaluated with respect to the Run 2 L1_XE50 trigger item, and obtained from a trigger reprocessing of the Run 2 data offline. The rates are taken from a special Enhanced Bias run, where details of the weighting scheme can be found in ATL-DAQ-PUB-2016-002. The signal efficiency is evaluated using TTbar Monte Carlo samples, where the true $E_T^{\rm miss}$ from neutrinos and muons is required to be greater than 150 GeV. The curve labeled tcpufit refers to the calorimeter only algorithm using topoclusters (arXiv:1603.02934) used as baseline at the end of Run 2, while other curves correspond to algorithms incorporating tracking. For the algorithm pfsum_vssk and pfsum_cssk clusters are replaced with particle flow (PF) objects (arXiv:1703.10485) using tracks. Furthermore, the algorithm pfsum_vssk subtracts a median energy by estimating the PF constituent area using Voronoi diagrams (ATLAS-CONF-2017-065), then applies a minimum threshold on the PF constituent dynamically by using the soft killer algorithm (arXiv:1407.0408). The pfsum_cssk proceeds similarly, but rather uses the constituent subtraction method (arXiv:1403.3108).

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Background rejection vs. signal efficiency curves computed. More details on how to interpret the plots can be found in arXiv:2005.09554. The background rate on the y-axis is evaluated with respect to the Run 2 L1_XE50 trigger item, and obtained from a trigger reprocessing of the Run 2 data offline. The rates are taken from a special Enhanced Bias run, where details of the weighting scheme can be found in ATL-DAQ-PUB-2016-002. The signal efficiency is evaluated using TTbar Monte Carlo samples, where the true $E_T^{\rm miss}$ from neutrinos and muons is required to be greater than 150 GeV. The curve labeled tcpufit refers to the calorimeter only algorithm using topoclusters (arXiv:1603.02934) used as baseline at the end of Run 2, while other curves correspond to algorithms incorporating tracking. The pfopufit algorithm uses the same techniques as tcpufit, but the input topological clusters are modified to use Particle Flow Objects (PFOs) using tracks (arXiv:1703.10485). The pfopufit algorithm is a variant of the tcpufit algorithm using input PFOs instead of topoclusters. As well as the improved momentum resolution of the PFOs, the vertex information provided by the charged PFOs is used to improve the categorisation of deposits into hard-scatter and pile-up. For the algorithm pfsum_vssk and pfsum_cssk clusters are replaced with particle flow (PF) objects (arXiv:1703.10485) using tracks. Furthermore, the algorithm pfsum_vssk subtracts a median energy by estimating the PF constituent area using Voronoi diagrams (ATLAS-CONF-2017-065), then applies a minimum threshold on the PF constituent dynamically by using the soft killer algorithm (arXiv:1407.0408). The pfsum_cssk proceeds similarly, but rather uses the constituent subtraction method (arXiv:1403.3108).

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Background rejection vs. signal efficiency curves computed. More details on how to interpret the plots can be found in arXiv:2005.09554. The background rate on the y-axis is evaluated with respect to the Run 2 L1_XE50 trigger item, and obtained from a trigger reprocessing of the Run 2 data offline. The rates are taken from a special Enhanced Bias run, where details of the weighting scheme can be found in ATL-DAQ-PUB-2016-002. The signal efficiency is evaluated using TTbar Monte Carlo samples, where the true $E_T^{\rm miss}$ from neutrinos and muons is required to be greater than 150 GeV. The curve labeled tcpufit refers to the calorimeter only algorithm using topoclusters (arXiv:1603.02934) used as baseline at the end of Run 2, while other curves correspond to algorithms incorporating tracking. The pfopufit algorithm uses the same techniques as tcpufit, but the input topological clusters are modified to use Particle Flow Objects (PFOs) using tracks (arXiv:1703.10485). The pfopufit algorithm is a variant of the tcpufit algorithm using input PFOs instead of topoclusters. As well as the improved momentum resolution of the PFOs, the vertex information provided by the charged PFOs is used to improve the categorisation of deposits into hard-scatter and pile-up. For the algorithm pfsum_vssk and pfsum_cssk clusters are replaced with particle flow (PF) objects (arXiv:1703.10485) using tracks. Furthermore, the algorithm pfsum_vssk subtracts a median energy by estimating the PF constituent area using Voronoi diagrams (ATLAS-CONF-2017-065), then applies a minimum threshold on the PF constituent dynamically by using the soft killer algorithm (arXiv:1407.0408). The pfsum_cssk proceeds similarly, but rather uses the constituent subtraction method (arXiv:1403.3108).

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Trigger efficiency for the $E_T^{\rm miss}$ trigger computed for various cell $E_T^{\rm miss}$ thresholds (arXiv:2005.09554) for TTbar events. Only the HLT algorithm is applied and no events are rejected at L1. The efficiency is computed with respect to the true $E_T^{\rm miss}$ in the event, where all neutrinos and muons are treated as invisible particles. The uncertainties are statistical only.

ttbar_cell_turnon_preliminary.png

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Trigger efficiency for the $E_T^{\rm miss}$ trigger computed for various cell $E_T^{\rm miss}$ thresholds (arXiv:2005.09554) for TTbar events. Only the HLT algorithm is applied and no events are rejected at L1. The efficiency is computed with respect to the true $E_T^{\rm miss}$ in the event, where all neutrinos and muons are treated as invisible particles. The uncertainties are statistical only.

ttbar_cell_turnon_L1num_preliminary.png

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Comparison of data (black) and estimate of the W and Z (red) boson events as a function of the cell algorithm MET distribution for events with L1 $E_T^{\rm miss}$ > 50 GeV. The data labeled Zero Bias and are selected using triggers without an explicit HLT requirement, and were collected in 2015-2018 with prescaled triggers. In addition, events are required to have an L1 $E_T^{\rm miss}$ value greater than 50 GeV. The cell algorithm $E_T^{\rm miss}$ distribution contribution from W and Z events is estimated by selecting the subset of the full events which pass a W or Z tag and then correcting for the simulated tagging efficiency. The tagging consists of requiring a single muon or di-muon trigger, 2 opposite charged muons in the event, and a di-muon invariant mass between 66 and 116 GeV for Z ->mumu; a single muon trigger, one muon and no electrons in the event, and a transverse mass > 50 GeV for W->mu,nu and a single electron trigger, one electron and no muons in the event, and a transverse mass > 50 GeV for $W\rightarrow e \nu$. Simulated W and Z events are used to determine the tag fraction of each decay mode as a function of cell $E_T^{\rm miss}$ for events with L1 $E_T^{\rm miss}$ > 50 GeV, and the distributions are then corrected by these efficiencies. Finally, the $Z\rightarrow \mu\mu$ mode is multiplied by 7 to account for the $Z\rightarrow \nu\nu$ contribution. The contributions from $W\rightarrow \tau \nu$ and $Z\rightarrow\tau\tau$ are not accounted for in the efficiency corrections, but leptonic tau decays may contribute to the tagged event yields in data. This plot demonstrates that the contribution for the rates from real $E_T^{\rm miss}$ in SM is much less than the contribution from QCD events due to mis-measurement.

METDIST45.png

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13 TeV Full Run-2 (data 2015-2018)

Paper TRIG-2019-01 "Performance of the missing transverse momentum triggers for the ATLAS detector during Run-2 data taking"

https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PAPERS/TRIG-2019-01/

March 2019

The combined L1 and HLT efficiency of the lowest unprescaled missing transverse energy triggers for the years 2015 to 2018 are shown as a function of the Z boson transverse momentum. The events are taken from data with a Z -> mumu selection, and the transverse momentum of the Z boson is used as a proxy for the missing transverse momentum in the event, as muons are treated as invisible objects by the triggers concerned. Depending on the data-taking period, the HLT E_T,miss was calculated by one or a combination of the algorithms "cell", "mht", or "pufit". In the "cell" algorithm, the E_T,miss is calculated as the negative of the transverse momentum vector sum of all calorimeter cells passing a two-sided noise cut. In the "mht" algorithm, the E_T,miss is calculated as the negative of the transverse momentum vector sum of all jets reconstructed by the anti-$k_t$ jet finding algorithm from calorimeter topological clusters. These jets have pileup subtraction and JES calibration applied. In the "pufit" algorithm, the E_T,miss is calculated as the negative of the transverse momentum vector sum of all calorimeter topological clusters corrected for pileup. The pileup correction is done by grouping the clusters into coarser "towers" which are then marked as pileup if their E_T falls below a pileup-dependent threshold. A fit to below-threshold towers is performed, taking into account resolutions, making the assumption that the contribution of the pileup to E_T,miss is zero. The fitted pileup E_T density is used to correct the above-threshold towers. In later years, the thresholds for these algorithms were raised to compensate for increased pileup, and therefore lower efficiencies in the turn on region were observed. High efficiency was maintained for events with E_T,miss > 200 GeV throughout all years.
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The combined L1 and HLT efficiency of the lowest unprescaled missing transverse energy triggers for the years 2015 to 2018 are shown as a function of the mean number of simultaneous interactions per proton--proton bunch crossing averaged over all bunches circulating in the LHC per lumi-block. The events are taken from data with a Z -> mumu selection. The transverse momentum of the Z boson, calculated from the two muons, is required to be at least 150 GeV, and is used as a proxy for the missing transverse momentum in the event, as muons are treated as invisible objects by the triggers concerned. Depending on the data-taking period, the HLT E_T,miss was calculated by one or a combination of the algorithms "cell", "mht", or "pufit". In the "cell" algorithm, the E_T,miss is calculated as the negative of the transverse momentum vector sum of all calorimeter cells passing a two-sided noise cut. In the "mht" algorithm, the E_T,miss is calculated as the negative of the transverse momentum vector sum of all jets reconstructed by the anti-$k_t$ jet finding algorithm from calorimeter topological clusters. These jets have pileup subtraction and JES calibration applied. In the "pufit" algorithm, the E_T,miss is calculated as the negative of the transverse momentum vector sum of all calorimeter topological clusters corrected for pileup. The pileup correction is done by grouping the clusters into coarser "towers" which are then marked as pileup if their E_T falls below a pileup-dependent threshold. A fit to below-threshold towers is performed, taking into account resolutions, making the assumption that the contribution of the pileup to E_T,miss is zero. The fitted pileup E_T density is used to correct the above-threshold towers.
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13 TeV data 2018

May 2018

The combined L1 and HLT efficiency of the missing transverse energy trigger HLT_xe110_pufit_xe70_L1XE50 (primary chain in the beginning of 2018) and HLT_xe110_pufit_xe65_L1XE50 (primary chain since May 12th) as well as the efficiency of the corresponding L1 trigger L1_XE50 are shown as a function of the Z boson transverse momentum. The events are taken from data with a Z -> mumu selection and the transverse momentum of the Z boson is used as a proxy for the missing transverse momentum in the event as muons are treated as invisible objects by the triggers concerned. The HLT E_T,miss of the “pufit” algorithm is calculated as the negative of the transverse momentum vector sum of all calorimeter topological clusters corrected for pileup. The pileup correction is done by grouping the clusters into coarser “towers” which are then marked as pileup if their E_T falls below a pileup-dependent threshold. A fit to below-threshold towers is performed, taking into account resolutions, making the assumption that the contribution of the pileup to E_T,miss is zero. The fitted pileup E_T density is used to correct the above-threshold towers. The second HLT selection in each trigger is on the “cell” algorithm. The E_T,miss of this algorithm is calculated as the negative of the transverse momentum vector sum of all calorimeter cells passing a two-sided noise cut.
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The combined L1 and HLT efficiency of the missing transverse energy trigger HLT_xe110_pufit_xe70_L1XE50 (primary chain in the beginning of 2018) and HLT_xe110_pufit_xe65_L1XE50 (primary chain since May 12th) as well as the efficiency of the corresponding L1 trigger L1_XE50 are shown as a function of the number of simultaneous interactions in a given proton–proton bunch crossing (calculated individually per bunch crossing). The events are taken from data with a Z -> mumu selection and a Z-boson transverse momentum calculated from the two muons of at least 150 GeV is required. The transverse momentum of the Z boson is used as a proxy for the missing transverse momentum in the event as muons are treated as invisible objects by the triggers concerned. The HLT E_T,miss of the “pufit” algorithm is calculated as the negative of the transverse momentum vector sum of all calorimeter topological clusters corrected for pileup. The pileup correction is done by grouping the clusters into coarser “towers” which are then marked as pileup if their E_T falls below a pileup-dependent threshold. A fit to below-threshold towers is performed, taking into account resolutions, making the assumption that the contribution of the pileup to E_T,miss is zero. The fitted pileup E_T density is used to correct the above-threshold towers. The second HLT selection in each trigger is on the “cell” algorithm. The E_T,miss of this algorithm is calculated as the negative of the transverse momentum vector sum of all calorimeter cells passing a two-sided noise cut.
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The trigger rates are compared for the E_T,miss trigger HLT_xe110_pufit_xe70_L1XE50 (primary chain in the beginning of 2018) and HLT_xe110_pufit_xe65_L1XE50 (primary chain since May 12th) as a function of the mean number of simultaneous interactions per proton–proton bunch crossing averaged over all bunches circulating in the LHC per lumi-block. The HLT E_T,miss of the “pufit” algorithm is calculated as the negative of the transverse momentum vector sum of all calorimeter topological clusters corrected for pileup. The pileup correction is done by grouping the clusters into coarser “towers” which are then marked as pileup if their E_T falls below a pileup-dependent threshold. A fit to below-threshold towers is performed, taking into account resolutions, making the assumption that the contribution of the pileup to E_T,miss is zero. The fitted pileup E_T density is used to correct the above-threshold towers. The second HLT selection in each trigger is on the “cell” algorithm. The E_T,miss of this algorithm is calculated as the negative of the transverse momentum vector sum of all calorimeter cells passing a two-sided noise cut.
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13 TeV data 2017

September 2017

The combined L1 and HLT efficiency of the missing transverse energy trigger HLT_xe110_pufit_L1XE50 as well as the efficiency of the corresponding L1 trigger L1_XE50 are shown as a function of the Z boson transverse momentum. The events are taken from data with a Z -> mumu selection and the transverse momentum of the Z boson is used as a proxy for the missing transverse momentum in the event as muons are treated as invisible objects by the triggers concerned. The HLT E_T,miss of the “pufit” algorithm is calculated as the negative of the transverse momentum vector sum of all calorimeter topological clusters corrected for pileup. The pileup correction is done by grouping the clusters into coarser “towers” which are then marked as pileup if their E_T falls below a pileup-dependent threshold. A simultaneous fit to both classes of towers is performed, taking into account resolutions, making the assumption that the contribution of the pileup to E_T,miss is zero. The fitted pileup E_T density is used to correct the above-threshold towers.
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The combined L1 and HLT efficiency of the missing transverse energy trigger HLT_xe110_pufit_L1XE50 as well as the efficiency of the corresponding L1 trigger L1_XE50 are shown as a function of the mean number of simultaneous interactions in a given proton–proton bunch crossing. The events shown are taken from data with a Z -> mumu selection and a Z-boson transverse momentum calculated from the two muons of at least 150 GeV is required. The transverse momentum of the Z boson is used as a proxy for the missing transverse momentum in the event as muons are treated as invisible objects by the triggers concerned. The HLT E_T,miss of the “pufit” algorithm is calculated as the negative of the transverse momentum vector sum of all calorimeter topological clusters corrected for pileup. The pileup correction is done by grouping the clusters into coarser “towers” which are then marked as pileup if their E_T falls below a pileup-dependent threshold. A simultaneous fit to both classes of towers is performed, taking into account resolutions, making the assumption that the contribution of the pileup to E_T,miss is zero. The fitted pileup E_T density is used to correct the above-threshold towers.
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The trigger rates are compared for the first-level E_T,miss trigger L1_XE50 for loose and tight noise suppression thresholds in the forward calorimeter (FCAL) as a function of the mean number of simultaneous interactions per proton–proton bunch crossing averaged over all bunches circulating in the LHC.
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The trigger rates are compared for the E_T,miss trigger HLT_xe110_pufit_L1XE50 as a function of the mean number of simultaneous interactions per proton–proton bunch crossing averaged over all bunches circulating in the LHC. The “pufit” E_T,miss flavour is calculated as the negative of the transverse momentum vector sum of all calorimeter topological clusters corrected for pileup. The pileup correction is done by grouping the clusters into coarser “towers” which are then marked as pileup if their E_T falls below a pileup-dependent threshold. A simultaneous fit to both classes of towers is performed, taking into account resolutions, making the assumption that the contribution of the pileup to E_T,miss is zero. The fitted pileup E_T density is used to correct the above-threshold towers.
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July 2017

The combined L1 and HLT efficiency of the missing transverse energy triggers HLT_xe110_pufit_L1XE50 and HLT_xe110_mht_L1XE50 as well as the efficiency of the corresponding L1 trigger (L1_XE50) are shown as a function of the reconstructed E_T,miss (modified to count muons as invisible). The events shown are taken from data with a W -> munu selection to provide a sample enriched in real E_T,miss . The HLT E_T,miss of the “pufit” algorithm is calculated as the negative of the transverse momentum vector sum of all calorimeter topological clusters corrected for pileup. The pileup correction is done by grouping the clusters into coarser “towers” which are then marked as pileup if their E_T falls below a pileup-dependent threshold. A simultaneous fit to both classes of towers is performed, taking into account resolutions, making the assumption that the contribution of the pileup to E_T,miss is zero. The fitted pileup E_T density is used to correct the above-threshold towers. The HLT E_T,miss of the “mht” algorithm is calculated as the negative of the transverse momentum vector sum of all jets reconstructed by the anti-k_T jet finding algorithm from calorimeter topological clusters. These jets have pileup subtraction and JES calibration applied.
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The combined L1 and HLT efficiency of the currently lowest unprescaled missing transverse energy trigger (HLT_xe110_pufit_L1XE50) as well as the efficiency of the corresponding L1 trigger (L1_XE50) are shown as a function of the mean number of simultaneous interactions in a given proton–proton bunch crossing. Events with at least 150 GeV of reconstructed E_T,miss (modified to count muons as invisible) are selected. The events shown are taken from data with a W > munu selection to provide a sample enriched in real E_T,miss. The HLT E_T,miss of the “pufit” algorithm is calculated as the negative of the transverse momentum vector sum of all calorimeter topological clusters corrected for pileup. The pileup correction is done by grouping the clusters into coarser “towers” which are then marked as pileup if their E_T falls below a pileup-dependent threshold. A simultaneous fit to both classes of towers is performed, taking into account resolutions, making the assumption that the contribution of the pileup to E_T,miss is zero. The fitted pileup E_T density is used to correct the above-threshold towers. The HLT E_T,miss of the “mht” algorithm is calculated as the negative of the transverse momentum vector sum of all jets reconstructed by the anti-k_T jet finding algorithm from calorimeter topological clusters. These jets have pileup subtraction and JES calibration applied.
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The trigger cross-section as measured by using online rate and luminosity is compared for the main trigger E_T,miss reconstruction algorithms used in 2016 (“mht”) and 2017 (“pufit”) as a function of the mean number of simultaneous interactions per proton–proton bunch crossing averaged over all bunches circulating in the LHC. The triggers HLT_xe110_mht_L1XE50 and HLT_xe110_pufit_L1XE50 are used as representative benchmarks of the 2016 and 2017 data-taking campaigns, respectively. The “pufit” E_T,miss flavour is calculated as the negative of the transverse momentum vector sum of all calorimeter topological clusters corrected for pileup. The pileup correction is done by grouping the clusters into coarser “towers” which are then marked as pileup if their E_T falls below a pileup-dependent threshold. A simultaneous fit to both classes of towers is performed, taking into account resolutions, making the assumption that the contribution of the pileup to E_T,miss is zero. The fitted pileup E_T density is used to correct the above-threshold towers. The “mht” E_T flavour is calculated as the negative of the transverse momentum vector sum of all jets reconstructed by the anti-kT jet finding algorithm from calorimeter topological clusters. These jets have pileup subtraction and JES calibration applied.
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13 TeV data 2016

Nov 2016

The trigger cross section as measured by using online rate and luminosity is shown as a function of average number of processes per LHC bunch crossing as measured online, for various missing ET triggers. The ETmiss is calculated as the negative of the transverse momentum vector sum of all jets reconstructed by the anti-kT jet finding algorithm from calorimeter topological clusters. These jets have pileup subtraction and JES calibration applied (ETmiss (mht)). The ETmiss is calculated as the negative of the transverse momentum vector sum of all calorimeter cells that aren't flagged as known bad cells and that pass noise cuts (ETmiss (cell)). The ETmiss is calculated as the negative of the transverse momentum vector sum of all calorimeter topological clusters corrected for pileup (ETmiss (pufit)). The pileup correction is done by grouping the clusters into coarser 'towers' which are then marked as pileup if their ET falls below a pileup dependent threshold. A simultaneous fit to both classes of towers is performed, taking into account resolutions, making the assumption that the contribution of the pileup to ETmiss is zero. The fitted pileup ET density is used to correct the above-threshold towers. All triggers have an L1 ETmiss requirement of 50 GeV, measured at the electromagnetic scale. metxs_vs_mu.png
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The trigger efficiency relative to the current lowest unprescaled trigger is shown for three different trigger strategies as a function of the reconstructed ETmiss (modified to count muons as invisible). The events shown are taken from data with a Z -> &mu&mu selection to provide a pure signal sample. The ETmiss is calculated as the negative of the transverse momentum vector sum of all jets reconstructed by the anti-kT jet finding algorithm from calorimeter topological clusters. These jets have pileup subtraction and JES calibration applied (ETmiss (mht)). The ETmiss is calculated as the negative of the transverse momentum vector sum of all calorimeter cells that aren't flagged as known bad cells and that pass noise cuts (ETmiss (cell)). The ETmiss is calculated as the negative of the transverse momentum vector sum of all calorimeter topological clusters corrected for pileup (ETmiss (pufit)). The pileup correction is done by grouping the clusters into coarser 'towers' which are then marked as pileup if their ET falls below a pileup dependent threshold. A simultaneous fit to both classes of towers is performed, taking into account resolutions, making the assumption that the contribution of the pileup to ETmiss is zero. The fitted pileup ET density is used to correct the above-threshold towers. All triggers have an L1 ETmiss requirement of 50 GeV, measured at the electromagnetic scale. Zmumu.png
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May 2016

Efficiency as a function of modified offline ETmiss for three different ETmiss trigger algorithms, using early pp collision data from 2016. Data from trains with both 12 and 72 bunches are used. The events have been selected using single lepton (electron or muon) triggers. The x-axis shows the offline ETmiss calculated from the sum of electrons, photons and jets, without the contributions from the muons or the track soft term. Three different ETmiss high-level trigger algorithms are shown: HLT_xe80_tc_lcw_L1XE50 calculates ETmiss based on calibrated clusters of calorimeter cells, and has a nominal threshold of 80 GeV. HLT_xe90_mht_L1XE50 calculates ETmiss based on reconstructed jets, and has a nominal threshold of 90 GeV. HLT_xe100_L1XE50 calculates ETmiss based on calorimeter cells calibrated at the electromagnetic scale, and has a nominal threshold (at the electromagnetic scale) of 100 GeV. All three algorithms are seeded by a level-1 trigger algorithm with a nominal threshold of 50 GeV which is also shown. Preliminary3_Wmunu_L1.png
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13 TeV data and simulations 2015

Performance plots made with 25ns data, especially approved for LHCC (Nov 30th 2015)

ETmiss trigger efficiency turn-on curves with respect to the ETmiss reconstructed offline without muon corrections. The dataset have been selected using the lowest unprescaled single muon trigger. Events are also required to satisfy W(mn) selections of Standard Model inclusive W cross section measurements. The different turn-on curves have been obtained for the lowest unprescaled trigger of various HLT ETmiss algorithms activated during the 25 ns runs: the cell-based (xe without postfix), jet-based (mht), and topocluster-based (tc) algorithms. Uncertainties are statistical only. Preliminary3_Wmunu_L1.png
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ETmiss trigger efficiency turn-on curves with respect to the ETmiss reconstructed offline without muon corrections. The dataset have been selected using the lowest unprescaled single muon trigger. Events are also required to satisfy W(mn) selections of Standard Model inclusive W cross section measurements. The different turn-on curves have been obtained for the L1 ETmiss and for the HLT ETmiss algorithm activated during the 25 ns runs: the cell-based (xe without postfix), jet-based (mht), and topocluster-based algorithms, with (tc_PS) and without (tc) a pile-up subtraction scheme. The thresholds for the different algorithms correspond to equal trigger rate. Uncertainties are statistical only. Preliminary_Wmunu_L1_er.png
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ETmiss trigger efficiency turn-on curves with respect to the ETmiss reconstructed offline without muon corrections. The dataset have been selected using the lowest unprescaled single muon trigger. Events are also required to satisfy Z(mm) selections of Standard Model inclusive W cross section measurements. The different turn-on curves have been obtained for the L1 ETmiss and for the HLT ETmiss algorithm activated during the 25 ns runs: the cell-based (xe without postfix), jet-based (mht), and topocluster-based algorithms, with (tc_PS) and without (tc) a pile-up subtraction scheme. The thresholds for the different algorithms correspond to equal trigger rate. Uncertainties are statistical only. Preliminary_Zmumu_L1_er.png
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Plots for the poster ATL-COM-DAQ-2015-112 presented at the Lepton-Photon Conference, Ljubljana 17-22 August 2015

Missing transverse momentum distributions reconstructed online using different algorithms: a 2-sided 2-sigma noise suppression cell-based algorithm (cell), a topocluster-based algorithm with no further corrections (topocl), an eta-ring pile-up subtraction (topoclPS), or a pile-up fit procedure (topoclPUC), and an algorithm based on the sum of jet momenta (mht). The three different topocluster-based algorithms overlay each other for most bins. These plots show MET for events collected with the full ATLAS trigger menu. MET distributions are event topology dependent, but this figure allows a qualitative comparison of the different algorithms. All_TRIGxe_MET_data2015_periodC.png
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Scalar sum of transverse momentum distributions reconstructed online using different algorithms: a 2-sided 2-sigma noise suppression cell-based algorithm (cell), a topocluster-based algorithm with no further corrections (topocl), an eta-ring pile-up subtraction (topoclPS), or a pile-up fit procedure (topoclPUC), and an algorithm based on the sum of jet momenta (mht).The three different topocluster-based algorithms overlay each other completely. These plots show MET for events collected with the full ATLAS trigger menu. MET distributions are event topology dependent, but this figure allows a qualitative comparison of the different algorithms. All_TRIGxe_SUMET_data2015_periodC.png
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Resolution of the missing transverse momentum reconstructed at the trigger level as a function of the offline Sum ET reference for different algorithms: a 2-sided 2-sigma noise suppression cell-based algorithm (cell), a topocluster-based algorithm with no further corrections (topocl), an eta-ring pile-up subtraction (topoclPS), or a pile-up fit procedure (topoclPUC), and an algorithm based on the sum of jet momenta (mht). The resolution is obtained by a fit of a gaussian to the x-component of the ETmiss obtained for each bin of the SumET reference. The plotted statistical error bars are smaller than the size of the marker point. The three different topocluster-based algorithms overlay each other for most bins. These plots show MET for events collected with the full ATLAS trigger menu. MET distributions are event topology dependent, but this figure allows a qualitative comparison of the different algorithms. All_SumETResolution_Errors_data2015_periodC.png
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Missing transverse momentum trigger efficiency turn-on curves for a threshold of 35 GeV as a function of offline reconstructed ETmiss reference for different algorithms: a 2-sided 2-sigma noise suppression cell-based algorithm (cell), a topocluster-based algorithm with no further corrections (topocl), an eta-ring pile-up subtraction (topoclPS), or a pile-up fit procedure (topoclPUC), and an algorithm based on the sum of jet momenta (mht). Error bars are statistical binomial errors only. The three different topocluster-based algorithms overlay each other for most bins.These plots show MET for events collected with the full ATLAS trigger menu. MET distributions are event topology dependent, but this figure allows a qualitative comparison of the different algorithms. All_Offline_PXE35_data2015_periodC.png
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Missing transverse momentum trigger efficiency turn-on curves for a threshold of 50 GeV as a function of offline reconstructed ETmiss reference for different algorithms: a 2-sided 2-sigma noise suppression cell-based algorithm (cell), a topocluster-based algorithm with no further corrections (topocl), an eta-ring pile-up subtraction (topoclPS), or a pile-up fit procedure (topoclPUC), and an algorithm based on the sum of jet momenta (mht). Error bars are statistical binomial errors only. The three different topocluster-based algorithms overlay each other for most bins. These plots show MET for events collected with the full ATLAS trigger menu. MET distributions are event topology dependent, but this figure allows a qualitative comparison of the different algorithms. All_Offline_PXE50_data2015_periodC.png
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Plots for the poster ATL-COM-DAQ-2015-105 presented at the EPS-HEP Conference, Vienna 22-29 July 2015

ATL-DAQ-SLIDE-2015-495 https://cds.cern.ch/record/2047023

The look-up-table of the new proposed topological algorithm for L1 is shown: a Kalman filter is used to calculate a correction weight to the Level 1 ETmiss (based on the sum of towers of cells) .The weight is a function of L1 proto-jet pT and pseudo rapidity, that are built from energy depositions in specific regions of interest. The values of the weights are obtained by analyzing simulated events with real ETmiss with severe pile-up by minimizing the difference of the true missing transverse energy in the event and the correction sum E⃗Tmiss L1 − Σ i wi ⋅ p⃗Tjet i. Energy contributions from the forward region are weighted down to subtract pile-up, whereas central jets are weighted up to apply an ad-hoc calibration. The correction is applied at Level 1 using the new topological processor that can produce such a corrected ETmiss in real-time. The weights in the look- up-table have only a subheading dependence on the underlying physics sample as the main correction comes from the energy depositions from pile-up collisions. oldLUT.png
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Turn-on curve of the corrected versus the original L1 ETmiss for ZH → νν bb events simulated at 13 TeV with an average pile-up of 23 is shown. Both algorithms are kept at thresholds that correspond to the same total trigger rate as estimated from simulated events without any real ETmiss at similar conditions. effcurve_ZHnubmc15_oldLUT.png
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Turn-on curve of the corrected versus the default L1 ETmiss for ttbar events simulated at 13 TeV with an average pile-up of 23 is shown. One or both top- quarks decay via a semileptonic transition and produce real ETmiss in the event. Both algorithms are kept at thresholds that correspond to the same total trigger rate as estimated from simulated events without any real ETmiss at similar conditions. effcurve_ttbarmc15_oldLUT.png
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2012 data and simulations at 8 TeV

ATL-DAQ-PUB-2018-001 "Performance of the ATLAS global transverse-momentum triggers at √s = 8 TeV"

https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PUBNOTES/ATL-DAQ-PUB-2018-001/

ATL-DAQ-PUB-2017-002 "Analytical description of missing transverse-momentum trigger rates in ATLAS with √s = 7 and 8 TeV data"

http://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PUBNOTES/ATL-DAQ-PUB-2017-002/

Plots for the poster ATL-COM-DAQ-2015-105 presented at the EPS-HEP Conference, Vienna 22-29 July 2015

ATL-DAQ-SLIDE-2015-495 https://cds.cern.ch/record/2047023

The missing transverse momentum of events collected in 2012 with a random trigger on crossing bunches as determined with the default offline algorithm versus the value obtained with the Level 1 trigger tower algorithm. The white stripes parallel to the y-axis are a consequence of the 1 GeV resolution on the x and y components added in quadrature to obtain the L1 missing transverse momentum. data12_MinBias_h_L2_XE_vs_MET_RefFinal_scatterplot_v3.png
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The missing transverse momentum of events collected in 2012 with a random trigger on crossing bunches as determined with the default offline algorithm versus the value obtained with the Level 2 algorithm. The Level 2 algorithm is based on energy sums from the front-end electronics boards (FEBs) that was modified to provide just a sum over all (up to and mostly 128) cells in each board. This is necessary as the entire calorimeter cannot be read out in its full granularity at the rates the Level 2 system has to sustain. data12_MinBias_h_EF_feb_XE_vs_MET_RefFinal_scatterplot_v3.png
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The missing transverse momentum of events collected in 2012 with a random trigger on crossing bunches as determined with the default offline algorithm versus the value obtained with the EF-level cell-sum algorithm. This algorithm sums the energy content using the full granularity of the calorimeter of about 188000 cells above a specified noise threshold. data12_MinBias_h_EF_XE_vs_MET_RefFinal_scatterplot_v3.png
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The missing transverse momentum of events collected in 2012 with a random trigger on crossing bunches as determined with the default offline algorithm versus the value obtained with the EF-level cluster algorithm not including correction for the hadronic energy scale. This algorithm clusters up the energy content using of the about 188000 calorimeter cells using a topological algorithm. data12_MinBias_h_EF_topocl_EM_XE_vs_MET_RefFinal_scatterplot_v3.png
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The missing transverse momentum of events collected in 2012 with a random trigger on crossing bunches as determined with the default offline algorithm versus the value obtained with the EF-level cluster algorithm including correction for the hadronic energy scale. This algorithm clusters up the energy content using of the about 188000 calorimeter cells using a topological algorithm and applies a local weight calibration depending on the cluster properties to each cell. data12_MinBias_h_EF_topocl_XE_vs_MET_RefFinal_scatterplot_v3.png
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Performance of the ATLAS transverse energy triggers at √s = 8 TeV (June 11, 2012)

The resolution of the x component of missing transversal energy for 2012 data are compared with the corresponding 2011 algorithm: in blue the EF - L2 and in red the EF - L1 residual are shown, where the latter corresponds to the L2 resolution used in the 2011 MET L2 calculation. mex_ef_l2_l1_fit.png
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The resolution of the x component of missing transversal energy for 2012 data for EF is compared with the offline values: in blue the EF topological cluster calculation - Offline (RefFinal) and in red the EF cell based calculation - Offline (RefFinal) residual are shown. mex_ef_l2_l1_fit.png
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The improvement in the turn-on curve for simulated pp → ZH → νν bb events (using Pythia and mH = 120 GeV) using either the lowest unprescaled trigger chain in 2011 (L1 XE50 → L2 xe55 noM → xe60 verytight noMu) or in 2012 (L1 XE40 BGRP7 → L2 xe45T → xe80T tclcw loose) are shown. mex_ef_l2_l1_fit.png
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Comparison of 2011 and 2012 triggers (November 7, 2012)

Figure 3 in https://cds.cern.ch/record/1492192?ln=en compares simulated SUSY efficiency for 2011 and 2012 MET triggers.

2011 data and simulations at 7 TeV

Selected plots below from the conf note on performance of the ATLAS transverse energy triggers at √s = 7 TeV were approved before the note. The full note can be found at https://cds.cern.ch/record/1647616.

Level 1 ETMISS distribution for candidate W → μ ν events compared with expectations from simulation. Events are selected using a muon trigger with transverse momentum threshold of 11 GeV at L1 and 18 GeV at L2 and EF, and are required to have an offline transverse mass between 40 and 95 GeV. The transverse momentum of muons is not included in this determination of ETMISS. Since the sums are over the full calorimeter, various small effects give rise to mismatch between data and simulation. These include hot cells in data (which are later removed offline), imprecise modeling of bunch trains, pulse shapes and noise suppression, and high energy tails not fully reproduced by PYTHIA 6. L1_MET_Wmunu_comp_2011_perfnote.png
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Level 1 ΣET distribution for candidate W → μ ν events compared with expectations from simulation. Events are selected using a muon trigger with transverse momentum threshold of 11 GeV at L1 and 18 GeV at L2 and EF, and are required to have an offline transverse mass between 40 and 95 GeV. The transverse momentum of muons is not included in this determination of ΣET. Since the sums are over the full calorimeter, various small effects give rise to mismatch between data and simulation. These include hot cells in data (which are later removed offline), imprecise modeling of bunch trains, pulse shapes and noise suppression, and high energy tails not fully reproduced by PYTHIA 6. L1_SET_Wmunu_comp_2011_perfnote.png
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Event Filter Level ETMISS distribution for candidate W → μ ν events compared with expectations from simulation. Events are selected using a muon trigger with transverse momentum threshold of 11 GeV at L1 and 18 GeV at L2 and EF, and are required to have an offline transverse mass between 40 and 95 GeV. The transverse momentum of muons is not included in this determination of ETMISS. Since the sums are over the full calorimeter, various small effects give rise to mismatch between data and simulation. These include hot cells in data (which are later removed offline), imprecise modeling of bunch trains, pulse shapes and noise suppression, and high energy tails not fully reproduced by PYTHIA 6. EF_MET_Wmunu_comp_2011_perfnote.png
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Event Filter Level ΣET distribution for candidate W → μ ν events compared with expectations from simulation. Events are selected using a muon trigger with transverse momentum threshold of 11 GeV at L1 and 18 GeV at L2 and EF, and are required to have an offline transverse mass between 40 and 95 GeV. The transverse momentum of muons is not included in this determination of ΣET. Since the sums are over the full calorimeter, various small effects give rise to mismatch between data and simulation. These include hot cells in data (which are later removed offline), imprecise modeling of bunch trains, pulse shapes and noise suppression, and high energy tails not fully reproduced by PYTHIA 6. EF_SET_Wmunu_comp_2011_perfnote.png
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N.B. The eight random trigger figures below initially included a caption in the figure that these were from 281 inverse nb. This integrated luminosity was found to be in error, and, in fact, a luminosity quote for these events doesn't really make sense. So the figures were replaced on 10 Oct. 2013 with ones not stating the luminosity. Reference to the 281 inverse nanobarns in the captions to the plots of EF MET and EF XS versus mu were removed on 14 October 2013 (A. Mincer)

Trigger Level 1 distributions of the x-component of the missing momentum (Ex) as determined from the sum over the full set of calorimeter trigger towers for events triggered on random bunch crossings. These are shown for two different calorimeter ΣET ranges, and are compared with Gaussian fits to the distributions. The results from these fits to the peak region data points (with statistical errors as shown in the figure) are used to determine the event-by-event ETMISS expected from calorimeter energy measurement fluctuations. L1_MEx_Slice_20.png
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Trigger Level 1 distributions of the x-component of the missing momentum (Ex) as determined from the sum over the full set of calorimeter trigger towers for events triggered on random bunch crossings. These are shown for two different calorimeter ΣET ranges, and are compared with Gaussian fits to the distributions. The results from these fits to the peak region data points (with statistical errors as shown in the figure) are used to determine the event-by-event ETMISS expected from calorimeter energy measurement fluctuations. L1_MEx_Slice_70.png
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Trigger Event Filter level distributions of the x-component of the missing momentum (Ex) as determined from the sum over the full set of calorimeter cells for events triggered on random bunch crossings. These are shown for two different calorimeter ΣET ranges, and are compared with Gaussian fits to the distributions. The results from these fits to the peak region data points (with statistical errors as shown in the figure) are used to determine the event-by-event ETMISS expected from calorimeter energy measurement fluctuations. EF_MEx_Slice_20.png
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Trigger Event Filter level distributions of the x-component of the missing momentum (Ex) as determined from the sum over the full set of calorimeter cells for events triggered on random bunch crossings. These are shown for two different calorimeter ΣET ranges, and are compared with Gaussian fits to the distributions. The results from these fits to the peak region data points (with statistical errors as shown in the figure) are used to determine the event-by-event ETMISS expected from calorimeter energy measurement fluctuations. EF_MEx_Slice_70.png
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Standard deviation of the x-component of the missing momentum (Ex) as determined from the sum over the full set of calorimeter L1 trigger towers for events triggered on random bunch crossings. A linear fit to the standard deviation as a function of the square root of the total calorimeter ΣET is used to determine the event-by-eventETMISS expected from calorimeter energy measurement fluctuations. The error bars are the statistical errors on the standard deviation determined from a Gaussian fit to the individual Ex distributions for each ΣET bin. The non-linearity at Level 1 means that fewer events than predicted by the line will fluctuate above any given Ex value. L1_ExSig_sqrt_SumEt.png
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Standard deviation of the y-component of the missing momentum (Ey) as determined from the sum over the full set of calorimeter L1 trigger towers for events triggered on random bunch crossings. A linear fit to the standard deviation as a function of the square root of the total calorimeter ΣET is used to determine the event-by-eventETMISS expected from calorimeter energy measurement fluctuations. The error bars are the statistical errors on the standard deviation determined from a Gaussian fit to the individual Ey distributions for each ΣET bin. The non-linearity at Level 1 means that fewer events than predicted by the line will fluctuate above any given Ey value. L1_EySig_sqrt_SumEt.png
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Standard deviation of the x-component of the missing momentum (Ex) as determined from the sum over the full set of calorimeter cells at Event Filter level for events triggered on random bunch crossings. A linear fit to the standard deviation as a function of the square root of the total calorimeter ΣET is used to determine the event-by-eventETMISS expected from calorimeter energy measurement fluctuations. The error bars are the statistical errors on the standard deviation determined from a Gaussian fit to the individual Ex distributions for each ΣET bin. EF_ExSig_sqrt_SumEt.png
png pdf eps
Standard deviation of the y-component of the missing momentum (Ey) as determined from the sum over the full set of calorimeter cells at Event Filter level for events triggered on random bunch crossings. A linear fit to the standard deviation as a function of the square root of the total calorimeter ΣET is used to determine the event-by-eventETMISS expected from calorimeter energy measurement fluctuations. The error bars are the statistical errors on the standard deviation determined from a Gaussian fit to the individual Ey distributions for each ΣET bin. EF_EySig_sqrt_SumEt.png
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Event Filter Level ETMISS distributions for various values of the average number of interactions per bunch crossing μ for a random trigger on colliding bunches. A strong dependence on μ is visible. MET_mu.png
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Rates per bunch crossing (corrected for prescales used) of ETMISS triggers as a function of the number of interactions per bunch crossing μ. The figure on the left shows the rates of ETMISS triggers with thresholds of 20, 30 and 40 GeV for data recorded from 14 April to 30 October 2011. The discontinuities in rates seen in this figure are due to low thresholds being sensitive to changes in beam structure and small detector variations such as noisy cells. As seen from the large μ dependence in this figure, the rates for these thresholds are also very sensitive to pileup. The figure on the right shows the rates of ETMISS triggers with thresholds of 60, 70, 80 and 90 GeV for data recorded from 13 July to 30 October 2011. The roughly linear dependence on μ implies little pileup sensitivity. Every dot is an average over about 8 minutes of data. The earlier period data uses different zero suppression, but was included in the left plot because the 30 GeV threshold trigger was turned off afterwards. EF_xe20_xe40_noMu_rates.png
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Rates per bunch crossing (corrected for prescales used) of ETMISS triggers as a function of the number of interactions per bunch crossing μ. The figure on the left shows the rates of ETMISS triggers with thresholds of 20, 30 and 40 GeV for data recorded from 14 April to 30 October 2011. The discontinuities in rates seen in this figure are due to low thresholds being sensitive to changes in beam structure and small detector variations such as noisy cells. As seen from the large μ dependence in this figure, the rates for these thresholds are also very sensitive to pileup. The figure on the right shows the rates of ETMISS triggers with thresholds of 60, 70, 80 and 90 GeV for data recorded from 13 July to 30 October 2011. The roughly linear dependence on μ implies little pileup sensitivity. Every dot is an average over about 8 minutes of data. The earlier period data uses different zero suppression, but was included in the left plot because the 30 GeV threshold trigger was turned off afterwards. EF_xe60_xe90_noMu_rates.png
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The Event Filter level ETMISS significance (EF XS) distributions for various values of the average number of interactions per bunch crossing μ for a random trigger on colliding bunches. XS is defined as ETMISS/resolution, where the ETMISS resolution is determined for each event from ΣET in that event. Events identified offline as having calorimeter noise bursts or badly measured jets were removed from the data samples used. XS is used to select events with low ETMISS that are unlikely to have been the result of measurement fluctuations. Unlike the case for ETMISS, The XS distribution and therefore the XS trigger rate is not strongly μ dependent. XS_mu_cleaned.png
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Comparison of measured and simulated combined all-level trigger efficiency for various ETMISS triggers for W → μ ν events. Events are selected using a muon trigger with transverse momentum threshold of 11 GeV at L1 and 18 GeV at L2 and EF, and are required to have an offline transverse mass between 40 and 95 GeV. The trigger thresholds in GeV at Level 1 and Event Filter level are indicated in the figures. The transverse momentum of muons is not included in these determinations of ETMISS. Because it involves a sum over the full calorimeter, the efficiency behavior may vary significantly for different event samples. EF_xe20_MET_turnon_Wmunu.png
png pdf eps
Comparison of measured and simulated combined all-level trigger efficiency for various ETMISS triggers for W → μ ν events. Events are selected using a muon trigger with transverse momentum threshold of 11 GeV at L1 and 18 GeV at L2 and EF, and are required to have an offline transverse mass between 40 and 95 GeV. The trigger thresholds in GeV at Level 1 and Event Filter level are indicated in the figures. The transverse momentum of muons is not included in these determinations of ETMISS. Because it involves a sum over the full calorimeter, the efficiency behavior may vary significantly for different event samples. EF_xe30_MET_turnon_Wmunu.png
png pdf eps
Comparison of measured and simulated combined all-level trigger efficiency for various ETMISS triggers for W → μ ν events. Events are selected using a muon trigger with transverse momentum threshold of 11 GeV at L1 and 18 GeV at L2 and EF, and are required to have an offline transverse mass between 40 and 95 GeV. The trigger thresholds in GeV at Level 1 and Event Filter level are indicated in the figures. The transverse momentum of muons is not included in these determinations of ETMISS. Because it involves a sum over the full calorimeter, the efficiency behavior may vary significantly for different event samples. EF_xe40_MET_turnon_Wmunu.png
png pdf eps
Comparison of measured and simulated combined all-level trigger efficiency for various ETMISS triggers for W → μ ν events. Events are selected using a muon trigger with transverse momentum threshold of 11 GeV at L1 and 18 GeV at L2 and EF, and are required to have an offline transverse mass between 40 and 95 GeV. The trigger thresholds in GeV at Level 1 and Event Filter level are indicated in the figures. The transverse momentum of muons is not included in these determinations of ETMISS. Because it involves a sum over the full calorimeter, the efficiency behavior may vary significantly for different event samples. EF_xe50_MET_turnon_Wmunu.png
png pdf eps
Comparison of measured and simulated combined all-level trigger efficiency for various ETMISS triggers for W → μ ν events. Events are selected using a muon trigger with transverse momentum threshold of 11 GeV at L1 and 18 GeV at L2 and EF, and are required to have an offline transverse mass between 40 and 95 GeV. The trigger thresholds in GeV at Level 1 and Event Filter level are indicated in the figures. The transverse momentum of muons is not included in these determinations of ETMISS. Because it involves a sum over the full calorimeter, the efficiency behavior may vary significantly for different event samples. EF_xe60_MET_turnon_Wmunu.png
png pdf eps
Comparison of measured and simulated combined all-level trigger efficiency for various ETMISS triggers for W → μ ν events. Events are selected using a muon trigger with transverse momentum threshold of 11 GeV at L1 and 18 GeV at L2 and EF, and are required to have an offline transverse mass between 40 and 95 GeV. The trigger thresholds in GeV at Level 1 and Event Filter level are indicated in the figures. The transverse momentum of muons is not included in these determinations of ETMISS. Because it involves a sum over the full calorimeter, the efficiency behavior may vary significantly for different event samples. EF_xe70_MET_turnon_Wmunu.png
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Development of XS trigger

From https://cds.cern.ch/record/1345767 (April 19, 2011)

Distributions of Level 1 ETMISS with varying number of primary vertices. L1METdistribVSvertices.png
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Distributions of Level 1 XS with varying number of primary vertices. L1XSdistribVSvertices.png
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Relative rate estimates for the Level 1 ETMISS trigger as a function of pileup.. L1XErateVSvertices.png
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Relative rate estimates for the Level 1 XS trigger as a function of pileup.. L1XSrateVSvertices.png
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Level 1 ETMISS versus the square root of the total calorimeter ΣET for minbias 7 TeV data versus simulated W → τ ν events. Wtaunu_L2_xe_vs_sqrset.png
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Event Filter level ETMISS versus the square root of the total calorimeter ΣET for minbias 7 TeV data versus simulated W → τ ν events. Wtaunu_EF_xe_vs_sqrset.png
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2010 data and simulations at 7 TeV

Performance of the ATLAS transverse energy triggers with initial LHC runs at √s = 7 TeV ATLAS-CONF-2010-072 (May 18, 2011)

* http://cdsweb.cern.ch/record/1351836

Comparison of ETMISS shapes between data-taking periods, measured at the L1. The spectra become harder for increasing mean number of collisions per bunch crossing μ. Minimum bias events have been used, and the distributions are normalized to the same area. turnon_efxestack2V2.png
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Comparison of ETMISS shapes between data-taking periods, measured at EF. The spectra become harder for increasing mean number of collisions per bunch crossing μ. Minimum bias events have been used, and the distributions are normalized to the same area. turnon_efxestack2V2.png
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Comparison of ΣET shapes between data-taking periods, measured at the L1. The spectra become harder for increasing mean number of collisions per bunch crossing μ. Minimum bias events have been used, and the distributions are normalized to the same area. turnon_efxestack2V2.png
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Comparison of ΣET shapes between data-taking periods, measured at the EF. The spectra become harder for increasing mean number of collisions per bunch crossing μ. Minimum bias events have been used, and the distributions are normalized to the same area. turnon_efxestack2V2.png
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ETMISS distributions measured at the L1 for minimum bias events with a single reconstructed primary vertex. The distributions are normalized to the same area. turnon_efxestack2V2.png
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ETMISS distributions measured at the EF for minimum bias events with a single reconstructed primary vertex. The distributions are normalized to the same area. turnon_efxestack2V2.png
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ΣET distributions measured at the L1 for minimum bias events with a single reconstructed primary vertex. The distributions are normalized to the same area. turnon_efxestack2V2.png
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ΣET distributions measured at the EF for minimum bias events with a single reconstructed primary vertex. The distributions are normalized to the same area. turnon_efxestack2V2.png
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Distributions of ETMISS computed at L1 for all events (black dots) and for the subset obtained by rejecting events with multiple primary vertices (green squares), compared to simulated minimum bias events which do not include pile-up effects (red circles). turnon_efxestack2V2.png
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Distributions of ETMISS computed at EF for all events (black dots) and for the subset obtained by rejecting events with multiple primary vertices (green squares), compared to simulated minimum bias events which do not include pile-up effects (red circles). turnon_efxestack2V2.png
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Distributions of ΣET computed at L1 for all events (black dots) and for the subset obtained by rejecting events with multiple primary vertices (green squares), compared to simulated minimum bias events which do not include pile-up effects (red circles). turnon_efxestack2V2.png
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Distributions of ΣET computed at EF for all events (black dots) and for the subset obtained by rejecting events with multiple primary vertices (green squares), compared to simulated minimum bias events which do not include pile-up effects (red circles). turnon_efxestack2V2.png
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Correlation between L1 and EF ETMISS measurements for events triggered by the electron trigger. turnon_efxestack2V2.png
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Correlation between L1 and MET_Topo ETMISS measurements for events triggered by the electron trigger. turnon_efxestack2V2.png
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Correlation between EF and MET_Topo ETMISS measurements for events triggered by the electron trigger. turnon_efxestack2V2.png
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Correlation between EF and MET_Topo ETMISS measurements for events triggered by the electron trigger which also triggered the softest L1 ETMISS threshold (L1 XE10, a threshold of 10 GeV). turnon_efxestack2V2.png
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Correlation between L1 and EF ΣET measurements for events triggered by the electron trigger. turnon_efxestack2V2.png
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Correlation between L1 and MET_Topo ΣET measurements for events triggered by the electron trigger. turnon_efxestack2V2.png
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Correlation between EF and MET_Topo ΣET measurements for events triggered by the electron trigger. turnon_efxestack2V2.png
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Correlation between EF and MET_Topo ΣET measurements for events triggered by the electron trigger which also triggered L1 TE50 (a 50 GeV ΣET threshold at L1). turnon_efxestack2V2.png
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L1 ΣET correlation with the value of ΣET computed offline for heavy-ion collision events. turnon_efxestack2V2.png
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EF ΣET correlation with the value of ΣET computed offline for heavy-ion collision events. turnon_efxestack2V2.png
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Efficiency of the L1 ETMISS threshold at 20 GeV as a function of MET_Topo ETMISS for W→eν candidates. turnon_efxestack2V2.png
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Efficiency of the L1 ETMISS threshold at 30 GeV as a function of MET_Topo ETMISS for W→eν candidates. turnon_efxestack2V2.png
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Efficiency of the L1 ETMISS threshold at 20 GeV as a function of MET_Topo ETMISS for W→μν candidates. turnon_efxestack2V2.png
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Efficiency of the L1 ETMISS threshold at 30 GeV as a function of MET_Topo ETMISS for W→μν candidates. turnon_efxestack2V2.png
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Efficiency of the EF-only ETMISS threshold at 30 GeV as a function of MET_Topo ETMISS for W→eν candidates. turnon_efxestack2V2.png
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Efficiency of the EF-only ETMISS threshold at 40 GeV as a function of MET_Topo ETMISS for W→eν candidates. turnon_efxestack2V2.png
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Efficiency of the EF-only ETMISS threshold at 30 GeV as a function of MET_Topo ETMISS for W→μν candidates. turnon_efxestack2V2.png
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Efficiency of the EF-only ETMISS threshold at 40 GeV as a function of MET_Topo ETMISS for W→μν candidates. turnon_efxestack2V2.png
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Efficiency of the ETMISS trigger with (L1, EF) thresholds at (20, 30) GeV as a function of MET_Topo ETMISS for W→eν candidates. turnon_efxestack2V2.png
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Efficiency of the ETMISS trigger with (L1, EF) thresholds at (30, 40) GeV as a function of MET_Topo ETMISS for W→eν candidates. turnon_efxestack2V2.png
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Efficiency of the ETMISS trigger with (L1, EF) thresholds at (20, 30) GeV as a function of MET_Topo ETMISS for W→μν candidates. turnon_efxestack2V2.png
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Efficiency of the ETMISS trigger with (L1, EF) thresholds at (30, 40) GeV as a function of MET_Topo ETMISS for W→μν candidates. turnon_efxestack2V2.png
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Efficiency of the EF-only ΣET threshold at 200 GeV as a function of MET_Topo ΣET for W→eν candidates. turnon_efxestack2V2.png
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Efficiency of the EF-only ΣET threshold at 300 GeV as a function of MET_Topo ΣET for W→eν candidates. turnon_efxestack2V2.png
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Efficiency of the EF-only ΣET threshold at 200 GeV as a function of MET_Topo ΣET for W→μν candidates. turnon_efxestack2V2.png
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Efficiency of the EF-only ΣET threshold at 300 GeV as a function of MET_Topo ΣET for W→μν candidates. turnon_efxestack2V2.png
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Efficiency of the ETMISS trigger with thresholds at 20 GeV (L1) and 30 GeV (EF) for W→eν candidates for different data-taking periods. turnon_efxestack2V2.png
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Efficiency of the ETMISS trigger with thresholds at 20 GeV (L1) and 30 GeV (EF) for W→μν candidates for different data-taking periods. turnon_efxestack2V2.png
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Efficiency of the xe30_noMu ETMISS trigger chain for W→eν candidates. Several suspicious runs are plotted separately, together with the efficiency over all runs. turnon_efxestack2V2.png
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Efficiency of the xe40_noMu ETMISS trigger chain for W→eν candidates. Several suspicious runs are plotted separately, together with the efficiency over all runs. turnon_efxestack2V2.png
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Major updates:
-- JoergStelzer - 12-Jun-2011

Responsible: JoergStelzer
Subject: public

Attachments Attachments
Topic attachments
I Attachment History Action Size Date Who Comment
PDFpdf 2016-05-16-UpdatedTurnOns.pdf r1 manage 20.3 K 2016-05-24 - 16:41 AlanBarr MET trigger turn-ons May 2016
PNGpng 2016-05-16-UpdatedTurnOns.png r4 r3 r2 r1 manage 81.8 K 2016-05-24 - 16:46 AlanBarr MET trigger turn-ons May 2016
Unknown file formateps 2018May_Efficiency_Zmumu.eps r1 manage 18.6 K 2018-05-27 - 21:01 KenjiHamano 2018 May Efficiency Plot
PDFpdf 2018May_Efficiency_Zmumu.pdf r1 manage 18.4 K 2018-05-27 - 21:01 KenjiHamano 2018 May Efficiency Plot
PNGpng 2018May_Efficiency_Zmumu.png r1 manage 52.1 K 2018-05-27 - 21:01 KenjiHamano 2018 May Efficiency Plot
Unknown file formateps 2018May_Rate_HLT.eps r1 manage 23.7 K 2018-05-27 - 18:14 KenjiHamano 2018 May Rate plot
PDFpdf 2018May_Rate_HLT.pdf r1 manage 30.8 K 2018-05-27 - 18:14 KenjiHamano 2018 May Rate plot
PNGpng 2018May_Rate_HLT.png r1 manage 15.9 K 2018-05-27 - 18:14 KenjiHamano 2018 May Rate plot
Unknown file formateps 2018May_Stability_Zmumu.eps r1 manage 18.5 K 2018-05-27 - 21:01 KenjiHamano 2018 May Efficiency Plot
PDFpdf 2018May_Stability_Zmumu.pdf r1 manage 17.2 K 2018-05-27 - 21:01 KenjiHamano 2018 May Efficiency Plot
PNGpng 2018May_Stability_Zmumu.png r1 manage 48.4 K 2018-05-27 - 21:01 KenjiHamano 2018 May Efficiency Plot
Unknown file formateps All_Offline_PXE35_data2015_periodC.eps r1 manage 29.3 K 2015-08-13 - 14:42 AntoniaStrubig turn-on xe35, all algos
PDFpdf All_Offline_PXE35_data2015_periodC.pdf r1 manage 22.3 K 2015-08-13 - 14:42 AntoniaStrubig turn-on xe35, all algos
PNGpng All_Offline_PXE35_data2015_periodC.png r1 manage 26.3 K 2015-08-13 - 14:42 AntoniaStrubig turn-on xe35, all algos
Unknown file formateps All_Offline_PXE50_data2015_periodC.eps r1 manage 29.7 K 2015-08-13 - 14:45 AntoniaStrubig turn-on xe50, all algos
PDFpdf All_Offline_PXE50_data2015_periodC.pdf r1 manage 22.7 K 2015-08-13 - 14:45 AntoniaStrubig turn-on xe50, all algos
PNGpng All_Offline_PXE50_data2015_periodC.png r1 manage 26.4 K 2015-08-13 - 14:45 AntoniaStrubig turn-on xe50, all algos
Unknown file formateps All_SumETResolution_Errors_data2015_periodC.eps r1 manage 10.2 K 2015-08-13 - 14:40 AntoniaStrubig sumEt resolution all algos
PDFpdf All_SumETResolution_Errors_data2015_periodC.pdf r1 manage 15.2 K 2015-08-13 - 14:40 AntoniaStrubig sumEt resolution all algos
PNGpng All_SumETResolution_Errors_data2015_periodC.png r1 manage 19.6 K 2015-08-13 - 14:40 AntoniaStrubig sumEt resolution all algos
Unknown file formateps All_TRIGxe_MET_data2015_periodC.eps r1 manage 14.0 K 2015-08-13 - 14:34 AntoniaStrubig Data 2015 period C, MET trigger algorithms distros
PDFpdf All_TRIGxe_MET_data2015_periodC.pdf r1 manage 15.2 K 2015-08-13 - 14:34 AntoniaStrubig Data 2015 period C, MET trigger algorithms distros
PNGpng All_TRIGxe_MET_data2015_periodC.png r1 manage 15.9 K 2015-08-13 - 14:34 AntoniaStrubig Data 2015 period C, MET trigger algorithms distros
Unknown file formateps All_TRIGxe_SUMET_data2015_periodC.eps r1 manage 15.8 K 2015-08-13 - 14:40 AntoniaStrubig Data 2015 period C, SumMET trigger algorithms distros
PDFpdf All_TRIGxe_SUMET_data2015_periodC.pdf r1 manage 15.8 K 2015-08-13 - 14:40 AntoniaStrubig Data 2015 period C, SumMET trigger algorithms distros
PNGpng All_TRIGxe_SUMET_data2015_periodC.png r1 manage 18.0 K 2015-08-13 - 14:40 AntoniaStrubig Data 2015 period C, SumMET trigger algorithms distros
Unknown file formateps EF_ExSig_sqrt_SumEt.eps r2 r1 manage 14.6 K 2013-10-10 - 21:12 AllenMincer EF Ex sigma vs sqrt(SumEt)
PDFpdf EF_ExSig_sqrt_SumEt.pdf r2 r1 manage 17.8 K 2013-10-10 - 21:18 AllenMincer EF Ex sigma vs sqrt(SumEt)
PNGpng EF_ExSig_sqrt_SumEt.png r2 r1 manage 18.9 K 2013-10-10 - 21:18 AllenMincer EF Ex sigma vs sqrt(SumEt)
Unknown file formateps EF_EySig_sqrt_SumEt.eps r2 r1 manage 14.6 K 2013-10-10 - 21:22 AllenMincer EF Ey sigma vs sqrt(SumEt)
PDFpdf EF_EySig_sqrt_SumEt.pdf r2 r1 manage 17.8 K 2013-10-10 - 21:23 AllenMincer EF Ey sigma vs sqrt(SumEt)
PNGpng EF_EySig_sqrt_SumEt.png r2 r1 manage 18.7 K 2013-10-10 - 21:23 AllenMincer EF Ey sigma vs sqrt(SumEt)
Unknown file formateps EF_MET_Wmunu_comp_2011_perfnote.eps r1 manage 12.6 K 2013-04-25 - 18:02 AllenMincer EF MET distribution for W mu nu events
PDFpdf EF_MET_Wmunu_comp_2011_perfnote.pdf r1 manage 16.5 K 2013-04-25 - 18:03 AllenMincer EF MET distribution for W mu nu events
PNGpng EF_MET_Wmunu_comp_2011_perfnote.png r1 manage 17.9 K 2013-04-25 - 18:03 AllenMincer EF MET distribution for W mu nu events
Unknown file formateps EF_MEx_Slice_20.eps r2 r1 manage 17.8 K 2013-10-10 - 21:30 AllenMincer EF METx for one SumEt slice
PDFpdf EF_MEx_Slice_20.pdf r2 r1 manage 17.0 K 2013-10-10 - 21:30 AllenMincer EF METx for one SumEt slice
PNGpng EF_MEx_Slice_20.png r2 r1 manage 19.1 K 2013-10-10 - 21:31 AllenMincer EF METx for one SumEt slice
Unknown file formateps EF_MEx_Slice_70.eps r2 r1 manage 18.4 K 2013-10-10 - 21:31 AllenMincer EF METx for another SumEt slice
PDFpdf EF_MEx_Slice_70.pdf r2 r1 manage 18.6 K 2013-10-10 - 21:33 AllenMincer EF METx for another SumEt slice
PNGpng EF_MEx_Slice_70.png r2 r1 manage 21.9 K 2013-10-10 - 21:33 AllenMincer EF METx for another SumEt slice
Unknown file formateps EF_SET_Wmunu_comp_2011_perfnote.eps r1 manage 13.8 K 2013-04-25 - 18:01 AllenMincer EF SumEt distribution for W mu nu events
PDFpdf EF_SET_Wmunu_comp_2011_perfnote.pdf r1 manage 17.4 K 2013-04-25 - 18:01 AllenMincer EF SumEt distribution for W mu nu events
PNGpng EF_SET_Wmunu_comp_2011_perfnote.png r1 manage 19.3 K 2013-04-25 - 18:02 AllenMincer EF SumEt distribution for W mu nu events
Unknown file formateps EF_xe20_MET_turnon_Wmunu.eps r1 manage 14.6 K 2013-04-24 - 13:57 AllenMincer W mu nu MET xe20 turn-on curve
PDFpdf EF_xe20_MET_turnon_Wmunu.pdf r1 manage 17.3 K 2013-04-24 - 13:57 AllenMincer W mu nu MET xe20 turn-on curve
PNGpng EF_xe20_MET_turnon_Wmunu.png r1 manage 21.0 K 2013-04-24 - 13:57 AllenMincer W mu nu MET xe20 turn-on curve
Unknown file formateps EF_xe20_xe40_noMu_rates.eps r1 manage 92.5 K 2013-04-22 - 15:59 AllenMincer XE20_noMu to xe40_noMu 2011 trigger rates
PDFpdf EF_xe20_xe40_noMu_rates.pdf r1 manage 225.5 K 2013-04-22 - 16:07 AllenMincer EF_xe20_noMu to xe40_noMU 2011 rates
PNGpng EF_xe20_xe40_noMu_rates.png r1 manage 47.4 K 2013-04-22 - 16:07 AllenMincer EF_xe20_noMu to xe40_noMU 2011 rates
Unknown file formateps EF_xe30_MET_turnon_Wmunu.eps r1 manage 14.6 K 2013-04-24 - 13:58 AllenMincer W mu nu MET xe30 turn-on curve
PDFpdf EF_xe30_MET_turnon_Wmunu.pdf r1 manage 17.3 K 2013-04-24 - 13:58 AllenMincer W mu nu MET xe30 turn-on curve
PNGpng EF_xe30_MET_turnon_Wmunu.png r1 manage 21.1 K 2013-04-24 - 13:59 AllenMincer W mu nu MET xe30 turn-on curve
Unknown file formateps EF_xe40_MET_turnon_Wmunu.eps r1 manage 14.6 K 2013-04-24 - 13:59 AllenMincer W mu nu MET xe40 turn-on curve
PDFpdf EF_xe40_MET_turnon_Wmunu.pdf r1 manage 17.3 K 2013-04-24 - 13:59 AllenMincer W mu nu MET xe40 turn-on curve
PNGpng EF_xe40_MET_turnon_Wmunu.png r1 manage 21.0 K 2013-04-24 - 13:59 AllenMincer W mu nu MET xe40 turn-on curve
Unknown file formateps EF_xe50_MET_turnon_Wmunu.eps r1 manage 14.5 K 2013-04-24 - 14:00 AllenMincer W mu nu MET xe50 turn-on curve
PDFpdf EF_xe50_MET_turnon_Wmunu.pdf r1 manage 17.3 K 2013-04-24 - 14:00 AllenMincer W mu nu MET xe50 turn-on curve
PNGpng EF_xe50_MET_turnon_Wmunu.png r1 manage 20.7 K 2013-04-24 - 14:01 AllenMincer W mu nu MET xe50 turn-on curve
Unknown file formateps EF_xe60_MET_turnon_Wmunu.eps r1 manage 14.5 K 2013-04-24 - 14:01 AllenMincer W mu nu MET xe60 turn-on curve
PDFpdf EF_xe60_MET_turnon_Wmunu.pdf r1 manage 17.2 K 2013-04-24 - 14:02 AllenMincer W mu nu MET xe60 turn-on curve
PNGpng EF_xe60_MET_turnon_Wmunu.png r1 manage 21.0 K 2013-04-24 - 14:02 AllenMincer W mu nu MET xe60 turn-on curve
Unknown file formateps EF_xe60_xe90_noMu_rates.eps r1 manage 84.0 K 2013-04-22 - 16:08 AllenMincer EF_xe60_noMu to xe90_noMU 2011 rates
PDFpdf EF_xe60_xe90_noMu_rates.pdf r1 manage 208.2 K 2013-04-22 - 16:08 AllenMincer EF_xe60_noMu to xe90_noMU 2011 rates
PNGpng EF_xe60_xe90_noMu_rates.png r1 manage 58.5 K 2013-04-22 - 16:08 AllenMincer EF_xe60_noMu to xe90_noMU 2011 rates
Unknown file formateps EF_xe70_MET_turnon_Wmunu.eps r1 manage 14.5 K 2013-04-24 - 14:02 AllenMincer W mu nu MET xe70 turn-on curve
PDFpdf EF_xe70_MET_turnon_Wmunu.pdf r1 manage 17.2 K 2013-04-24 - 14:03 AllenMincer W mu nu MET xe70 turn-on curve
PNGpng EF_xe70_MET_turnon_Wmunu.png r1 manage 20.3 K 2013-04-24 - 14:03 AllenMincer W mu nu MET xe70 turn-on curve
Unknown file formateps HLT_v2.eps r1 manage 24.8 K 2017-09-13 - 13:02 MoritzBackes  
PDFpdf HLT_v2.pdf r1 manage 20.7 K 2017-09-13 - 13:02 MoritzBackes  
PNGpng HLT_v2.png r1 manage 11.9 K 2017-09-13 - 13:02 MoritzBackes  
PDFpdf L1METdistribVSvertices.pdf r1 manage 1977.7 K 2014-01-15 - 22:07 AllenMincer L1 MET distributions for various number of vertices
PNGpng L1METdistribVSvertices.png r1 manage 88.2 K 2014-01-15 - 22:08 AllenMincer L1 MET distributions for various number of vertices
PDFpdf L1XErateVSvertices.pdf r1 manage 1976.8 K 2014-01-15 - 22:10 AllenMincer L1 MET relative rates for various number of vertices
PNGpng L1XErateVSvertices.png r1 manage 103.0 K 2014-01-15 - 22:10 AllenMincer L1 MET relative rates for various number of vertices
PDFpdf L1XSdistribVSvertices.pdf r1 manage 1976.7 K 2014-01-15 - 22:08 AllenMincer L1 XS distributions for various number of vertices
PNGpng L1XSdistribVSvertices.png r1 manage 88.9 K 2014-01-15 - 22:09 AllenMincer L1 XS distributions for various number of vertices
PDFpdf L1XSrateVSvertices.pdf r1 manage 1976.7 K 2014-01-15 - 22:11 AllenMincer L1 XS relative rates for various number of vertices
PNGpng L1XSrateVSvertices.png r1 manage 89.0 K 2014-01-15 - 22:11 AllenMincer L1 XS relative rates for various number of vertices
Unknown file formateps L1_ExSig_sqrt_SumEt.eps r2 r1 manage 14.4 K 2013-10-10 - 21:24 AllenMincer L1 Ex sigma vs sqrt(SumEt)
PDFpdf L1_ExSig_sqrt_SumEt.pdf r2 r1 manage 17.2 K 2013-10-10 - 21:24 AllenMincer L1 Ex sigma vs sqrt(SumEt)
PNGpng L1_ExSig_sqrt_SumEt.png r2 r1 manage 19.9 K 2013-10-10 - 21:25 AllenMincer L1 Ex sigma vs sqrt(SumEt)
Unknown file formateps L1_EySig_sqrt_SumEt.eps r2 r1 manage 14.4 K 2013-10-10 - 21:25 AllenMincer L1 Ey sigma vs sqrt(SumEt)
PDFpdf L1_EySig_sqrt_SumEt.pdf r2 r1 manage 17.2 K 2013-10-10 - 21:26 AllenMincer L1 Ey sigma vs sqrt(SumEt)
PNGpng L1_EySig_sqrt_SumEt.png r2 r1 manage 19.5 K 2013-10-10 - 21:26 AllenMincer L1 Ey sigma vs sqrt(SumEt)
Unknown file formateps L1_MET_Wmunu_comp_2011_perfnote.eps r1 manage 12.9 K 2013-04-25 - 17:55 AllenMincer L1 MET distribution for W mu nu events
PDFpdf L1_MET_Wmunu_comp_2011_perfnote.pdf r1 manage 16.5 K 2013-04-25 - 17:56 AllenMincer L1 MET distribution for W mu nu events
PNGpng L1_MET_Wmunu_comp_2011_perfnote.png r1 manage 17.7 K 2013-04-25 - 17:56 AllenMincer L1 MET distribution for W mu nu events
Unknown file formateps L1_MEx_Slice_20.eps r2 r1 manage 18.1 K 2013-10-10 - 21:27 AllenMincer L1 METx for one SumEt slice
PDFpdf L1_MEx_Slice_20.pdf r2 r1 manage 17.6 K 2013-10-10 - 21:27 AllenMincer L1 METx for one SumEt slice
PNGpng L1_MEx_Slice_20.png r2 r1 manage 20.2 K 2013-10-10 - 21:28 AllenMincer L1 METx for one SumEt slice
Unknown file formateps L1_MEx_Slice_70.eps r2 r1 manage 18.8 K 2013-10-10 - 21:28 AllenMincer L1 METx for another SumEt slice
PDFpdf L1_MEx_Slice_70.pdf r2 r1 manage 18.4 K 2013-10-10 - 21:29 AllenMincer L1 METx for another SumEt slice
PNGpng L1_MEx_Slice_70.png r2 r1 manage 22.1 K 2013-10-10 - 21:29 AllenMincer L1 METx for another SumEt slice
Unknown file formateps L1_SET_Wmunu_comp_2011_perfnote.eps r1 manage 14.4 K 2013-04-25 - 18:00 AllenMincer L1 SumEt distribution for W mu nu events
PDFpdf L1_SET_Wmunu_comp_2011_perfnote.pdf r1 manage 17.6 K 2013-04-25 - 18:00 AllenMincer L1 SumEt distribution for W mu nu events
PNGpng L1_SET_Wmunu_comp_2011_perfnote.png r1 manage 20.2 K 2013-04-25 - 18:00 AllenMincer L1 SumEt distribution for W mu nu events
Unknown file formateps L1_v3.eps r1 manage 30.7 K 2017-09-13 - 13:02 MoritzBackes  
PDFpdf L1_v3.pdf r1 manage 48.4 K 2017-09-13 - 13:02 MoritzBackes  
PNGpng L1_v3.png r1 manage 13.6 K 2017-09-13 - 13:02 MoritzBackes  
PDFpdf METDIST45.pdf r1 manage 15.4 K 2021-04-16 - 19:39 BenCarlson  
PNGpng METDIST45.png r1 manage 77.4 K 2021-04-16 - 19:39 BenCarlson  
Unknown file formateps MET_mu.eps r1 manage 17.2 K 2013-04-22 - 23:03 AllenMincer EF met distributions for different mu in 2011
PDFpdf MET_mu.pdf r1 manage 18.9 K 2013-04-22 - 23:01 AllenMincer EF met distributions for different mu in 2011
PNGpng MET_mu.png r1 manage 31.1 K 2013-04-22 - 23:02 AllenMincer EF met distributions for different mu in 2011
Unknown file formateps Preliminary3_Wmunu_L1.eps r1 manage 18.6 K 2015-11-30 - 22:27 PierreHuguesBeauchemin  
PDFpdf Preliminary3_Wmunu_L1.pdf r1 manage 19.0 K 2015-11-30 - 20:36 PierreHuguesBeauchemin  
PNGpng Preliminary3_Wmunu_L1.png r1 manage 15.9 K 2015-11-30 - 22:27 PierreHuguesBeauchemin  
Unknown file formateps Preliminary_Wmunu_L1_er.eps r1 manage 21.1 K 2015-11-30 - 22:27 PierreHuguesBeauchemin  
PDFpdf Preliminary_Wmunu_L1_er.pdf r1 manage 20.7 K 2015-11-30 - 20:36 PierreHuguesBeauchemin  
PNGpng Preliminary_Wmunu_L1_er.png r1 manage 19.1 K 2015-11-30 - 22:27 PierreHuguesBeauchemin  
Unknown file formateps Preliminary_Zmumu_L1_er.eps r1 manage 26.0 K 2015-11-30 - 22:27 PierreHuguesBeauchemin  
PDFpdf Preliminary_Zmumu_L1_er.pdf r1 manage 21.8 K 2015-11-30 - 20:36 PierreHuguesBeauchemin  
PNGpng Preliminary_Zmumu_L1_er.png r1 manage 19.0 K 2015-11-30 - 22:27 PierreHuguesBeauchemin  
Unknown file formateps Run2_Eff_AvgMu.eps r1 manage 15.8 K 2019-03-18 - 15:21 GabrielEmmanuelGallardo MET Trigger efficiencies on full Run 2 data
PDFpdf Run2_Eff_AvgMu.pdf r1 manage 15.6 K 2019-03-18 - 15:21 GabrielEmmanuelGallardo MET Trigger efficiencies on full Run 2 data
PNGpng Run2_Eff_AvgMu.png r1 manage 14.4 K 2019-03-18 - 15:21 GabrielEmmanuelGallardo MET Trigger efficiencies on full Run 2 data
Unknown file formateps Run2_Eff_Zpt.eps r1 manage 21.3 K 2019-03-18 - 15:21 GabrielEmmanuelGallardo MET Trigger efficiencies on full Run 2 data
PDFpdf Run2_Eff_Zpt.pdf r1 manage 19.8 K 2019-03-18 - 15:21 GabrielEmmanuelGallardo MET Trigger efficiencies on full Run 2 data
PNGpng Run2_Eff_Zpt.png r1 manage 17.7 K 2019-03-18 - 15:21 GabrielEmmanuelGallardo MET Trigger efficiencies on full Run 2 data
PDFpdf Wtaunu_EF_xe_vs_sqrset.pdf r1 manage 39.6 K 2014-01-10 - 20:27 AllenMincer EF MET vs sqrt(sumet) 2010 minbias data and W tau nu simulation
PNGpng Wtaunu_EF_xe_vs_sqrset.png r1 manage 37.2 K 2014-01-10 - 20:27 AllenMincer EF MET vs sqrt(sumet) 2010 minbias data and W tau nu simulation
PDFpdf Wtaunu_L2_xe_vs_sqrset.pdf r1 manage 39.4 K 2014-01-10 - 20:25 AllenMincer L1 MET vs sqrt(sumet) 2010 minbias data and W tau nu simulation
PNGpng Wtaunu_L2_xe_vs_sqrset.png r1 manage 38.5 K 2014-01-10 - 20:26 AllenMincer L1 MET vs sqrt(sumet) 2010 minbias data and W tau nu simulation
Unknown file formateps XS_mu_cleaned.eps r1 manage 17.7 K 2013-04-22 - 23:10 AllenMincer EF XS distributions for various mu values in 2011
PDFpdf XS_mu_cleaned.pdf r1 manage 19.0 K 2013-04-22 - 23:10 AllenMincer EF XS distributions for various mu values in 2011
PNGpng XS_mu_cleaned.png r1 manage 26.9 K 2013-04-22 - 23:11 AllenMincer EF XS distributions for various mu values in 2011
Unknown file formateps ZH_nunubb_atlas.eps r1 manage 19.9 K 2012-06-11 - 19:23 FlorianBernlochner MET 2012 Performance Plots
PDFpdf ZH_nunubb_atlas.pdf r1 manage 48.9 K 2012-06-11 - 19:23 FlorianBernlochner MET 2012 Performance Plots
PNGpng ZH_nunubb_atlas.png r1 manage 66.1 K 2012-06-11 - 19:23 FlorianBernlochner MET 2012 Performance Plots
Unknown file formateps ZmumuEff_L1_XE50_Z_pT.eps r1 manage 15.7 K 2017-09-13 - 12:58 MoritzBackes September 2017 efficiency plots
PDFpdf ZmumuEff_L1_XE50_Z_pT.pdf r1 manage 16.9 K 2017-09-13 - 12:58 MoritzBackes September 2017 efficiency plots
PNGpng ZmumuEff_L1_XE50_Z_pT.png r1 manage 14.1 K 2017-09-13 - 12:58 MoritzBackes September 2017 efficiency plots
Unknown file formateps ZmumuEff_L1_XE50_Z_pT_pu.eps r1 manage 12.8 K 2017-09-13 - 12:58 MoritzBackes September 2017 efficiency plots
PDFpdf ZmumuEff_L1_XE50_Z_pT_pu.pdf r1 manage 14.9 K 2017-09-13 - 12:58 MoritzBackes September 2017 efficiency plots
PNGpng ZmumuEff_L1_XE50_Z_pT_pu.png r1 manage 11.7 K 2017-09-13 - 12:58 MoritzBackes September 2017 efficiency plots
Unknown file formateps data12_MinBias_h_EF_XE_vs_MET_RefFinal_scatterplot_v3.eps r1 manage 186.9 K 2015-07-24 - 20:56 AllenMincer EF Cell MET versus RefFinal MET
PDFpdf data12_MinBias_h_EF_XE_vs_MET_RefFinal_scatterplot_v3.pdf r1 manage 45.0 K 2015-07-24 - 20:56 AllenMincer EF Cell MET versus RefFinal MET
PNGpng data12_MinBias_h_EF_XE_vs_MET_RefFinal_scatterplot_v3.png r1 manage 15.1 K 2015-07-24 - 20:56 AllenMincer EF Cell MET versus RefFinal MET
Unknown file formateps data12_MinBias_h_EF_feb_XE_vs_MET_RefFinal_scatterplot_v3.eps r1 manage 154.1 K 2015-07-24 - 20:38 AllenMincer L2 FEB vs RefFinal MET
PDFpdf data12_MinBias_h_EF_feb_XE_vs_MET_RefFinal_scatterplot_v3.pdf r1 manage 40.3 K 2015-07-24 - 20:38 AllenMincer L2 FEB versus RefFinal MET
PNGpng data12_MinBias_h_EF_feb_XE_vs_MET_RefFinal_scatterplot_v3.png r1 manage 16.0 K 2015-07-24 - 20:38 AllenMincer L2 FEB versus RefFinal MET
Unknown file formateps data12_MinBias_h_EF_topocl_EM_XE_vs_MET_RefFinal_scatterplot_v3.eps r1 manage 205.8 K 2015-07-24 - 20:57 AllenMincer EF topocl EM scale versus RefFinal MET
PDFpdf data12_MinBias_h_EF_topocl_EM_XE_vs_MET_RefFinal_scatterplot_v3.pdf r1 manage 49.1 K 2015-07-24 - 20:57 AllenMincer EF topocl EM scale versus RefFinal MET
PNGpng data12_MinBias_h_EF_topocl_EM_XE_vs_MET_RefFinal_scatterplot_v3.png r1 manage 14.3 K 2015-07-24 - 20:57 AllenMincer EF topocl EM scale versus RefFinal MET
Unknown file formateps data12_MinBias_h_EF_topocl_XE_vs_MET_RefFinal_scatterplot_v3.eps r1 manage 210.7 K 2015-07-24 - 20:58 AllenMincer EF hadcal topo cluster versus RefFinal MET
PDFpdf data12_MinBias_h_EF_topocl_XE_vs_MET_RefFinal_scatterplot_v3.pdf r1 manage 50.1 K 2015-07-24 - 20:58 AllenMincer EF hadcal topo cluster versus RefFinal MET
PNGpng data12_MinBias_h_EF_topocl_XE_vs_MET_RefFinal_scatterplot_v3.png r1 manage 16.6 K 2015-07-24 - 20:58 AllenMincer EF hadcal topo cluster versus RefFinal MET
Unknown file formateps data12_MinBias_h_L2_XE_vs_MET_RefFinal_scatterplot_v3.eps r1 manage 159.7 K 2015-07-24 - 20:31 AllenMincer L1 MET vs RefFinal MET
PDFpdf data12_MinBias_h_L2_XE_vs_MET_RefFinal_scatterplot_v3.pdf r1 manage 40.3 K 2015-07-24 - 20:31 AllenMincer L1 MET vs RefFinal MET
PNGpng data12_MinBias_h_L2_XE_vs_MET_RefFinal_scatterplot_v3.png r1 manage 14.8 K 2015-07-24 - 20:31 AllenMincer L1 MET vs RefFinal MET
Unknown file formateps dataWmunuEff_inTimePileup.eps r1 manage 12.1 K 2017-07-03 - 22:19 MoritzBackes 2017 performance plots for EPS
PDFpdf dataWmunuEff_inTimePileup.pdf r1 manage 15.5 K 2017-07-03 - 22:19 MoritzBackes 2017 performance plots for EPS
PNGpng dataWmunuEff_inTimePileup.png r1 manage 13.8 K 2017-07-03 - 22:19 MoritzBackes 2017 performance plots for EPS
Unknown file formateps dataWmunuEff_offlineMETNoMu.eps r1 manage 16.0 K 2017-07-03 - 22:19 MoritzBackes 2017 performance plots for EPS
PDFpdf dataWmunuEff_offlineMETNoMu.pdf r1 manage 18.5 K 2017-07-03 - 22:19 MoritzBackes 2017 performance plots for EPS
PNGpng dataWmunuEff_offlineMETNoMu.png r1 manage 16.4 K 2017-07-03 - 22:19 MoritzBackes 2017 performance plots for EPS
PDFpdf eff_1_style_preliminary.pdf r1 manage 15.2 K 2021-04-16 - 19:38 BenCarlson  
PNGpng eff_1_style_preliminary.png r1 manage 138.7 K 2021-04-16 - 19:38 BenCarlson  
PDFpdf eff_2_style_preliminary.pdf r1 manage 14.9 K 2021-04-16 - 19:38 BenCarlson  
PNGpng eff_2_style_preliminary.png r1 manage 129.9 K 2021-04-16 - 19:38 BenCarlson  
PDFpdf eff_3_style_preliminary.pdf r1 manage 14.8 K 2021-04-16 - 19:38 BenCarlson  
PNGpng eff_3_style_preliminary.png r1 manage 123.6 K 2021-04-16 - 19:38 BenCarlson  
PDFpdf eff_4_style_preliminary.pdf r1 manage 14.9 K 2021-04-16 - 19:38 BenCarlson  
PNGpng eff_4_style_preliminary.png r1 manage 124.9 K 2021-04-16 - 19:38 BenCarlson  
Unknown file formateps effcurve_ZHnubmc15_oldLUT.eps r1 manage 14.4 K 2015-07-24 - 21:05 AllenMincer L1 ZH nu nu b b simulated efficiency
PDFpdf effcurve_ZHnubmc15_oldLUT.pdf r1 manage 16.6 K 2015-07-24 - 21:05 AllenMincer L1 ZH nu nu b b simulated efficiency
PNGpng effcurve_ZHnubmc15_oldLUT.png r1 manage 19.1 K 2015-07-24 - 21:05 AllenMincer L1 ZH nu nu b b simulated efficiency
Unknown file formateps effcurve_ttbarmc15_oldLUT.eps r1 manage 11.8 K 2015-07-24 - 21:06 AllenMincer L1 KF t tbar simulated efficiency
PDFpdf effcurve_ttbarmc15_oldLUT.pdf r1 manage 15.6 K 2015-07-24 - 21:06 AllenMincer L1 KF t tbar simulated efficiency
PNGpng effcurve_ttbarmc15_oldLUT.png r1 manage 18.3 K 2015-07-24 - 21:06 AllenMincer L1 KF t tbar simulated efficiency
Unknown file formateps etxs_vs_mu_2017.eps r1 manage 12.6 K 2017-07-03 - 22:19 MoritzBackes 2017 performance plots for EPS
PDFpdf etxs_vs_mu_2017.pdf r1 manage 21.8 K 2017-07-03 - 22:19 MoritzBackes 2017 performance plots for EPS
PNGpng etxs_vs_mu_2017.png r1 manage 15.0 K 2017-07-03 - 22:19 MoritzBackes 2017 performance plots for EPS
Unknown file formateps mex_ef_l2_l1_fit.eps r1 manage 13.4 K 2012-06-11 - 19:23 FlorianBernlochner MET 2012 Performance Plots
PDFpdf mex_ef_l2_l1_fit.pdf r1 manage 17.5 K 2012-06-11 - 19:23 FlorianBernlochner MET 2012 Performance Plots
PNGpng mex_ef_l2_l1_fit.png r1 manage 91.1 K 2012-06-11 - 19:23 FlorianBernlochner MET 2012 Performance Plots
Unknown file formateps mex_tcl_ef_rf_fit.eps r1 manage 15.3 K 2012-06-11 - 19:23 FlorianBernlochner MET 2012 Performance Plots
PDFpdf mex_tcl_ef_rf_fit.pdf r1 manage 18.4 K 2012-06-11 - 19:23 FlorianBernlochner MET 2012 Performance Plots
PNGpng mex_tcl_ef_rf_fit.png r1 manage 105.5 K 2012-06-11 - 19:23 FlorianBernlochner MET 2012 Performance Plots
Unknown file formateps oldLUT.eps r1 manage 20.6 K 2015-07-24 - 21:03 AllenMincer L1 KF lookup table
PDFpdf oldLUT.pdf r1 manage 15.4 K 2015-07-24 - 21:03 AllenMincer L1 KF lookup table
PNGpng oldLUT.png r1 manage 34.5 K 2015-07-24 - 21:03 AllenMincer L1 KF lookup table
PDFpdf ttbar_cell_turnon_L1num_preliminary.pdf r1 manage 17.9 K 2021-04-16 - 19:39 BenCarlson  
PNGpng ttbar_cell_turnon_L1num_preliminary.png r1 manage 88.7 K 2021-04-16 - 19:39 BenCarlson  
PDFpdf ttbar_cell_turnon_preliminary.pdf r1 manage 17.8 K 2021-04-16 - 19:39 BenCarlson  
PNGpng ttbar_cell_turnon_preliminary.png r1 manage 84.7 K 2021-04-16 - 19:39 BenCarlson  
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Topic revision: r52 - 2021-05-21 - BenCarlson
 
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