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.
The signal efficiency for signal events for 2018 and 2022 data are compared. The data are collected with a muon trigger, where the muons transverse momentum is used as a proxy for . More details can be found in JHEP 08 (2020) 80. Only the L1 efficiency for 2022 data is shown, as the curves agree within a few percent when compared to 2018 data. The pfopufit algorithm uses the same techniques as tcpufit, but the input topological clusters are modified to use Particle Flow Objects (PFOs) using tracks (Eur. Phys. J. C 77 (2017) 466). 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 hardscatter and pileup. The curve labeled tcpufit refers to the calorimeter only algorithm using topoclusters (Eur. Phys. J. C 77 (2017) 490) used as baseline at the end of Run 2, where the statistical uncertainties are negligible. All the other curves correspond to algorithms incorporating tracking.  png pdf jpg 
The trigger rate for the primary \met trigger vs. for data collected in 2022, where the peak luminosity at the highest value of \m shown corresponds to . For the highest values of , the rates are within around 5% of Run 2 rate values. More details on how to interpret the plots can be found in JHEP 08 (2020) 80. 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 hardscatter and pileup. The curve labeled tcpufit refers to the calorimeter only algorithm using topoclusters (Eur. Phys. J. C 77 (2017) 490) used as baseline at the end of Run 2, where the statistical uncertainties are negligible. All the other curves correspond to algorithms incorporating tracking.  png pdf jpg 

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 yaxis 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 ATLDAQPUB2016002. The signal efficiency is evaluated using TTbar Monte Carlo samples, where the true 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 hardscatter and pileup. 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 hardscatter 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.  png pdf 
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 yaxis 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 ATLDAQPUB2016002. The signal efficiency is evaluated using TTbar Monte Carlo samples, where the true 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 (ATLASCONF2017065), 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). 

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 yaxis 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 ATLDAQPUB2016002. The signal efficiency is evaluated using TTbar Monte Carlo samples, where the true 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 hardscatter and pileup. 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 (ATLASCONF2017065), 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).  
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 yaxis 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 ATLDAQPUB2016002. The signal efficiency is evaluated using TTbar Monte Carlo samples, where the true 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 hardscatter and pileup. 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 (ATLASCONF2017065), 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). 

Trigger efficiency for the trigger computed for various cell 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 in the event, where all neutrinos and muons are treated as invisible particles. The uncertainties are statistical only.  
Trigger efficiency for the trigger computed for various cell 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 in the event, where all neutrinos and muons are treated as invisible particles. The uncertainties are statistical only.  
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 > 50 GeV. The data labeled Zero Bias and are selected using triggers without an explicit HLT requirement, and were collected in 20152018 with prescaled triggers. In addition, events are required to have an L1 value greater than 50 GeV. The cell algorithm 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 dimuon trigger, 2 opposite charged muons in the event, and a dimuon 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 . Simulated W and Z events are used to determine the tag fraction of each decay mode as a function of cell for events with L1 > 50 GeV, and the distributions are then corrected by these efficiencies. Finally, the mode is multiplied by 7 to account for the contribution. The contributions from and 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 in SM is much less than the contribution from QCD events due to mismeasurement. 
https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PAPERS/TRIG201901/
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 datataking 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 twosided 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 pileupdependent threshold. A fit to belowthreshold 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 abovethreshold 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.  png eps pdf 
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 protonproton bunch crossing averaged over all bunches circulating in the LHC per lumiblock. 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 datataking 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 twosided 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 pileupdependent threshold. A fit to belowthreshold 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 abovethreshold towers.  png eps pdf 
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 pileupdependent threshold. A fit to belowthreshold 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 abovethreshold 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 twosided noise cut. 
png eps pdf 
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 Zboson 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 pileupdependent threshold. A fit to belowthreshold 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 abovethreshold 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 twosided noise cut.  png eps pdf 
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 lumiblock. 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 pileupdependent threshold. A fit to belowthreshold 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 abovethreshold 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 twosided noise cut.  png eps pdf 
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 pileupdependent 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 abovethreshold towers.  png eps pdf 
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 Zboson 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 pileupdependent 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 abovethreshold towers.  png eps pdf 
The trigger rates are compared for the firstlevel 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.  png eps pdf 
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 pileupdependent 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 abovethreshold towers.  png eps pdf 
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 pileupdependent 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 abovethreshold 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 antik_T jet finding algorithm from calorimeter topological clusters. These jets have pileup subtraction and JES calibration applied.  png eps pdf 
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 pileupdependent 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 abovethreshold 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 antik_T jet finding algorithm from calorimeter topological clusters. These jets have pileup subtraction and JES calibration applied.  png eps pdf 
The trigger crosssection 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 datataking 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 pileupdependent 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 abovethreshold towers. The “mht” E_T flavour is calculated as the negative of the transverse momentum vector sum of all jets reconstructed by the antikT jet finding algorithm from calorimeter topological clusters. These jets have pileup subtraction and JES calibration applied.  png eps pdf 
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 antikT 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 abovethreshold towers. All triggers have an L1 ETmiss requirement of 50 GeV, measured at the electromagnetic scale.  eps pdf 
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 antikT 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 abovethreshold towers. All triggers have an L1 ETmiss requirement of 50 GeV, measured at the electromagnetic scale.  eps pdf 
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 xaxis 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 highlevel 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 level1 trigger algorithm with a nominal threshold of 50 GeV which is also shown.  pdf png 
ETmiss trigger efficiency turnon 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 turnon curves have been obtained for the lowest unprescaled trigger of various HLT ETmiss algorithms activated during the 25 ns runs: the cellbased (xe without postfix), jetbased (mht), and topoclusterbased (tc) algorithms. Uncertainties are statistical only. 
png pdf eps 
ETmiss trigger efficiency turnon 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 turnon curves have been obtained for the L1 ETmiss and for the HLT ETmiss algorithm activated during the 25 ns runs: the cellbased (xe without postfix), jetbased (mht), and topoclusterbased algorithms, with (tc_PS) and without (tc) a pileup subtraction scheme. The thresholds for the different algorithms correspond to equal trigger rate. Uncertainties are statistical only.  png pdf eps 
ETmiss trigger efficiency turnon 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 turnon curves have been obtained for the L1 ETmiss and for the HLT ETmiss algorithm activated during the 25 ns runs: the cellbased (xe without postfix), jetbased (mht), and topoclusterbased algorithms, with (tc_PS) and without (tc) a pileup subtraction scheme. The thresholds for the different algorithms correspond to equal trigger rate. Uncertainties are statistical only.  png pdf eps 
Missing transverse momentum distributions reconstructed online using different algorithms: a 2sided 2sigma noise suppression cellbased algorithm (cell), a topoclusterbased algorithm with no further corrections (topocl), an etaring pileup subtraction (topoclPS), or a pileup fit procedure (topoclPUC), and an algorithm based on the sum of jet momenta (mht). The three different topoclusterbased 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. 
png pdf eps 
Scalar sum of transverse momentum distributions reconstructed online using different algorithms: a 2sided 2sigma noise suppression cellbased algorithm (cell), a topoclusterbased algorithm with no further corrections (topocl), an etaring pileup subtraction (topoclPS), or a pileup fit procedure (topoclPUC), and an algorithm based on the sum of jet momenta (mht).The three different topoclusterbased 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. 
png pdf eps 
Resolution of the missing transverse momentum reconstructed at the trigger level as a function of the offline Sum ET reference for different algorithms: a 2sided 2sigma noise suppression cellbased algorithm (cell), a topoclusterbased algorithm with no further corrections (topocl), an etaring pileup subtraction (topoclPS), or a pileup 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 xcomponent 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 topoclusterbased 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. 
png pdf eps 
Missing transverse momentum trigger efficiency turnon curves for a threshold of 35 GeV as a function of offline reconstructed ETmiss reference for different algorithms: a 2sided 2sigma noise suppression cellbased algorithm (cell), a topoclusterbased algorithm with no further corrections (topocl), an etaring pileup subtraction (topoclPS), or a pileup fit procedure (topoclPUC), and an algorithm based on the sum of jet momenta (mht). Error bars are statistical binomial errors only. The three different topoclusterbased 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.  png pdf eps 
Missing transverse momentum trigger efficiency turnon curves for a threshold of 50 GeV as a function of offline reconstructed ETmiss reference for different algorithms: a 2sided 2sigma noise suppression cellbased algorithm (cell), a topoclusterbased algorithm with no further corrections (topocl), an etaring pileup subtraction (topoclPS), or a pileup fit procedure (topoclPUC), and an algorithm based on the sum of jet momenta (mht). Error bars are statistical binomial errors only. The three different topoclusterbased 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.  png pdf eps 
ATLDAQSLIDE2015495 https://cds.cern.ch/record/2047023
The lookuptable of the new proposed topological algorithm for L1 is shown: a Kalman filter is used to calculate a correction weight to the Level 1 E_{T}^{miss} (based on the sum of towers of cells) .The weight is a function of L1 protojet 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 E_{T}^{miss} with severe pileup by minimizing the difference of the true missing transverse energy in the event and the correction sum E⃗_{T}^{miss L1} − Σ _{ i } w_{i} ⋅ p⃗_{T}^{jet i}. Energy contributions from the forward region are weighted down to subtract pileup, whereas central jets are weighted up to apply an adhoc calibration. The correction is applied at Level 1 using the new topological processor that can produce such a corrected E_{T}^{miss} in realtime. The weights in the look uptable have only a subheading dependence on the underlying physics sample as the main correction comes from the energy depositions from pileup collisions. 
png pdf eps 
Turnon curve of the corrected versus the original L1 E_{T}^{miss} for ZH → νν bb events simulated at 13 TeV with an average pileup 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 E_{T}^{miss} at similar conditions.  png pdf eps 
Turnon curve of the corrected versus the default L1 E_{T}^{miss} for ttbar events simulated at 13 TeV with an average pileup of 23 is shown. One or both top quarks decay via a semileptonic transition and produce real E_{T}^{miss} 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 E_{T}^{miss} at similar conditions.  png pdf eps 
https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PUBNOTES/ATLDAQPUB2018001/
http://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PUBNOTES/ATLDAQPUB2017002/
ATLDAQSLIDE2015495 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 yaxis are a consequence of the 1 GeV resolution on the x and y components added in quadrature to obtain the L1 missing transverse momentum. 
png pdf eps 
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 frontend 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. 
png pdf eps 
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 EFlevel cellsum algorithm. This algorithm sums the energy content using the full granularity of the calorimeter of about 188000 cells above a specified noise threshold. 
png pdf eps 
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 EFlevel 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.  png pdf eps 
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 EFlevel 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.  png pdf eps 
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. 
png pdf eps 
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.  png pdf eps 
The improvement in the turnon 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.  png pdf eps 
Figure 3 in https://cds.cern.ch/record/1492192?ln=en compares simulated SUSY efficiency for 2011 and 2012 MET triggers.
Level 1 E_{T}^{MISS} 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 E_{T}^{MISS}. 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. 
png pdf eps 
Level 1 ΣE_{T} 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 ΣE_{T}. 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. 
png pdf eps 
Event Filter Level E_{T}^{MISS} 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 E_{T}^{MISS}. 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.  png pdf eps 
Event Filter Level ΣE_{T} 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 ΣE_{T}. 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.  png pdf eps 
Trigger Level 1 distributions of the xcomponent of the missing momentum ( 
png pdf eps 
Trigger Level 1 distributions of the xcomponent of the missing momentum ( 
png pdf eps 
Trigger Event Filter level distributions of the xcomponent of the missing momentum ( 
png pdf eps 
Trigger Event Filter level distributions of the xcomponent of the missing momentum ( 
png pdf eps 
Standard deviation of the xcomponent of the missing momentum ( 
png pdf eps 
Standard deviation of the ycomponent of the missing momentum ( 
png pdf eps 
Standard deviation of the xcomponent of the missing momentum (  png pdf eps 
Standard deviation of the ycomponent of the missing momentum (  png pdf eps 
Event Filter Level E_{T}^{MISS} 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. 
png pdf eps 
Rates per bunch crossing (corrected for prescales used) of E_{T}^{MISS} triggers as a function of the number of interactions per bunch crossing μ. The figure on the left shows the rates of E_{T}^{MISS} 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. 
eps pdf png 
Rates per bunch crossing (corrected for prescales used) of E_{T}^{MISS} triggers as a function of the number of interactions per bunch crossing μ. The figure on the left shows the rates of E_{T}^{MISS} 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 E_{T}^{MISS} 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. 
eps pdf png 
The Event Filter level E_{T}^{MISS} 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 E_{T}^{MISS}/resolution, where the E_{T}^{MISS} resolution is determined for each event from ΣE_{T} 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 E_{T}^{MISS} that are unlikely to have been the result of measurement fluctuations. Unlike the case for E_{T}^{MISS}, The XS distribution and therefore the XS trigger rate is not strongly μ dependent. 
png pdf eps 
Comparison of measured and simulated combined alllevel trigger efficiency for various E_{T}^{MISS} 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 E_{T}^{MISS}. Because it involves a sum over the full calorimeter, the efficiency behavior may vary significantly for different event samples. 
png pdf eps 
Comparison of measured and simulated combined alllevel trigger efficiency for various E_{T}^{MISS} 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 E_{T}^{MISS}. Because it involves a sum over the full calorimeter, the efficiency behavior may vary significantly for different event samples. 
png pdf eps 
Comparison of measured and simulated combined alllevel trigger efficiency for various E_{T}^{MISS} 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 E_{T}^{MISS}. Because it involves a sum over the full calorimeter, the efficiency behavior may vary significantly for different event samples. 
png pdf eps 
Comparison of measured and simulated combined alllevel trigger efficiency for various E_{T}^{MISS} 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 E_{T}^{MISS}. Because it involves a sum over the full calorimeter, the efficiency behavior may vary significantly for different event samples. 
png pdf eps 
Comparison of measured and simulated combined alllevel trigger efficiency for various E_{T}^{MISS} 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 E_{T}^{MISS}. Because it involves a sum over the full calorimeter, the efficiency behavior may vary significantly for different event samples.  png pdf eps 
Comparison of measured and simulated combined alllevel trigger efficiency for various E_{T}^{MISS} 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 E_{T}^{MISS}. Because it involves a sum over the full calorimeter, the efficiency behavior may vary significantly for different event samples.  png pdf eps 
Distributions of Level 1 E_{T}^{MISS} with varying number of primary vertices. 
png pdf 
Distributions of Level 1 XS with varying number of primary vertices. 
png pdf 
Relative rate estimates for the Level 1 E_{T}^{MISS} trigger as a function of pileup.. 
png pdf 
Relative rate estimates for the Level 1 XS trigger as a function of pileup.. 
png pdf 
Level 1 E_{T}^{MISS} versus the square root of the total calorimeter ΣE_{T} for minbias 7 TeV data versus simulated W → τ ν events.  png pdf 
Event Filter level E_{T}^{MISS} versus the square root of the total calorimeter ΣE_{T} for minbias 7 TeV data versus simulated W → τ ν events.  png pdf 
* http://cdsweb.cern.ch/record/1351836
Comparison of E_{T}^{MISS} shapes between datataking 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. 
png eps 
Comparison of E_{T}^{MISS} shapes between datataking 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. 
png eps 
Comparison of ΣE_{T} shapes between datataking 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. 
png eps 
Comparison of ΣE_{T} shapes between datataking 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. 
png eps 
E_{T}^{MISS} distributions measured at the L1 for minimum bias events with a single reconstructed primary vertex. The distributions are normalized to the same area. 
png eps 
E_{T}^{MISS} distributions measured at the EF for minimum bias events with a single reconstructed primary vertex. The distributions are normalized to the same area. 
png eps 
ΣE_{T} distributions measured at the L1 for minimum bias events with a single reconstructed primary vertex. The distributions are normalized to the same area. 
png eps 
ΣE_{T} distributions measured at the EF for minimum bias events with a single reconstructed primary vertex. The distributions are normalized to the same area. 
png eps 
Distributions of E_{T}^{MISS} 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 pileup effects (red circles). 
png eps 
Distributions of E_{T}^{MISS} 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 pileup effects (red circles). 
png eps 
Distributions of ΣE_{T} 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 pileup effects (red circles). 
png eps 
Distributions of ΣE_{T} 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 pileup effects (red circles). 
png eps 
Correlation between L1 and EF E_{T}^{MISS} measurements for events triggered by the electron trigger. 
png eps 
Correlation between L1 and MET_Topo E_{T}^{MISS} measurements for events triggered by the electron trigger. 
png eps 
Correlation between EF and MET_Topo E_{T}^{MISS} measurements for events triggered by the electron trigger. 
png eps 
Correlation between EF and MET_Topo E_{T}^{MISS} measurements for events triggered by the electron trigger which also triggered the softest L1 E_{T}^{MISS} threshold (L1 XE10, a threshold of 10 GeV). 
png eps 
Correlation between L1 and EF ΣE_{T} measurements for events triggered by the electron trigger. 
png eps 
Correlation between L1 and MET_Topo ΣE_{T} measurements for events triggered by the electron trigger. 
png eps 
Correlation between EF and MET_Topo ΣE_{T} measurements for events triggered by the electron trigger. 
png eps 
Correlation between EF and MET_Topo ΣE_{T} measurements for events triggered by the electron trigger which also triggered L1 TE50 (a 50 GeV ΣE_{T} threshold at L1). 
png eps 
L1 ΣE_{T} correlation with the value of ΣE_{T} computed offline for heavyion collision events. 
png eps 
EF ΣE_{T} correlation with the value of ΣE_{T} computed offline for heavyion collision events. 
png eps 
Efficiency of the L1 E_{T}^{MISS} threshold at 20 GeV as a function of MET_Topo ETMISS for W→eν candidates. 
png eps 
Efficiency of the L1 E_{T}^{MISS} threshold at 30 GeV as a function of MET_Topo ETMISS for W→eν candidates. 
png eps 
Efficiency of the L1 E_{T}^{MISS} threshold at 20 GeV as a function of MET_Topo ETMISS for W→μν candidates. 
png eps 
Efficiency of the L1 E_{T}^{MISS} threshold at 30 GeV as a function of MET_Topo ETMISS for W→μν candidates. 
png eps 
Efficiency of the EFonly E_{T}^{MISS} threshold at 30 GeV as a function of MET_Topo ETMISS for W→eν candidates. 
png eps 
Efficiency of the EFonly E_{T}^{MISS} threshold at 40 GeV as a function of MET_Topo ETMISS for W→eν candidates. 
png eps 
Efficiency of the EFonly E_{T}^{MISS} threshold at 30 GeV as a function of MET_Topo ETMISS for W→μν candidates. 
png eps 
Efficiency of the EFonly E_{T}^{MISS} threshold at 40 GeV as a function of MET_Topo ETMISS for W→μν candidates. 
png eps 
Efficiency of the E_{T}^{MISS} trigger with (L1, EF) thresholds at (20, 30) GeV as a function of MET_Topo ETMISS for W→eν candidates. 
png eps 
Efficiency of the E_{T}^{MISS} trigger with (L1, EF) thresholds at (30, 40) GeV as a function of MET_Topo ETMISS for W→eν candidates. 
png eps 
Efficiency of the E_{T}^{MISS} trigger with (L1, EF) thresholds at (20, 30) GeV as a function of MET_Topo ETMISS for W→μν candidates. 
png eps 
Efficiency of the E_{T}^{MISS} trigger with (L1, EF) thresholds at (30, 40) GeV as a function of MET_Topo ETMISS for W→μν candidates. 
png eps 
Efficiency of the EFonly ΣE_{T} threshold at 200 GeV as a function of MET_Topo ΣE_{T} for W→eν candidates. 
png eps 
Efficiency of the EFonly ΣE_{T} threshold at 300 GeV as a function of MET_Topo ΣE_{T} for W→eν candidates. 
png eps 
Efficiency of the EFonly ΣE_{T} threshold at 200 GeV as a function of MET_Topo ΣE_{T} for W→μν candidates. 
png eps 
Efficiency of the EFonly ΣE_{T} threshold at 300 GeV as a function of MET_Topo ΣE_{T} for W→μν candidates. 
png eps 
Efficiency of the E_{T}^{MISS} trigger with thresholds at 20 GeV (L1) and 30 GeV (EF) for W→eν candidates for different datataking periods. 
png eps 
Efficiency of the E_{T}^{MISS} trigger with thresholds at 20 GeV (L1) and 30 GeV (EF) for W→μν candidates for different datataking periods. 
png eps 
Efficiency of the xe30_noMu E_{T}^{MISS} trigger chain for W→eν candidates. Several suspicious runs are plotted separately, together with the efficiency over all runs.  png eps 
Efficiency of the xe40_noMu E_{T}^{MISS} trigger chain for W→eν candidates. Several suspicious runs are plotted separately, together with the efficiency over all runs.  png eps 
Major updates:
 JoergStelzer  12Jun2011
Responsible: JoergStelzer
Subject: public
I  Attachment  History  Action  Size  Date  Who  Comment 

20160516UpdatedTurnOns.pdf  r1  manage  20.3 K  20160524  16:41  AlanBarr  MET trigger turnons May 2016  
png  20160516UpdatedTurnOns.png  r4 r3 r2 r1  manage  81.8 K  20160524  16:46  AlanBarr  MET trigger turnons May 2016 
eps  2018May_Efficiency_Zmumu.eps  r1  manage  18.6 K  20180527  21:01  KenjiHamano  2018 May Efficiency Plot 
2018May_Efficiency_Zmumu.pdf  r1  manage  18.4 K  20180527  21:01  KenjiHamano  2018 May Efficiency Plot  
png  2018May_Efficiency_Zmumu.png  r1  manage  52.1 K  20180527  21:01  KenjiHamano  2018 May Efficiency Plot 
eps  2018May_Rate_HLT.eps  r1  manage  23.7 K  20180527  18:14  KenjiHamano  2018 May Rate plot 
2018May_Rate_HLT.pdf  r1  manage  30.8 K  20180527  18:14  KenjiHamano  2018 May Rate plot  
png  2018May_Rate_HLT.png  r1  manage  15.9 K  20180527  18:14  KenjiHamano  2018 May Rate plot 
eps  2018May_Stability_Zmumu.eps  r1  manage  18.5 K  20180527  21:01  KenjiHamano  2018 May Efficiency Plot 
2018May_Stability_Zmumu.pdf  r1  manage  17.2 K  20180527  21:01  KenjiHamano  2018 May Efficiency Plot  
png  2018May_Stability_Zmumu.png  r1  manage  48.4 K  20180527  21:01  KenjiHamano  2018 May Efficiency Plot 
eps  All_Offline_PXE35_data2015_periodC.eps  r1  manage  29.3 K  20150813  14:42  AntoniaStrubig  turnon xe35, all algos 
All_Offline_PXE35_data2015_periodC.pdf  r1  manage  22.3 K  20150813  14:42  AntoniaStrubig  turnon xe35, all algos  
png  All_Offline_PXE35_data2015_periodC.png  r1  manage  26.3 K  20150813  14:42  AntoniaStrubig  turnon xe35, all algos 
eps  All_Offline_PXE50_data2015_periodC.eps  r1  manage  29.7 K  20150813  14:45  AntoniaStrubig  turnon xe50, all algos 
All_Offline_PXE50_data2015_periodC.pdf  r1  manage  22.7 K  20150813  14:45  AntoniaStrubig  turnon xe50, all algos  
png  All_Offline_PXE50_data2015_periodC.png  r1  manage  26.4 K  20150813  14:45  AntoniaStrubig  turnon xe50, all algos 
eps  All_SumETResolution_Errors_data2015_periodC.eps  r1  manage  10.2 K  20150813  14:40  AntoniaStrubig  sumEt resolution all algos 
All_SumETResolution_Errors_data2015_periodC.pdf  r1  manage  15.2 K  20150813  14:40  AntoniaStrubig  sumEt resolution all algos  
png  All_SumETResolution_Errors_data2015_periodC.png  r1  manage  19.6 K  20150813  14:40  AntoniaStrubig  sumEt resolution all algos 
eps  All_TRIGxe_MET_data2015_periodC.eps  r1  manage  14.0 K  20150813  14:34  AntoniaStrubig  Data 2015 period C, MET trigger algorithms distros 
All_TRIGxe_MET_data2015_periodC.pdf  r1  manage  15.2 K  20150813  14:34  AntoniaStrubig  Data 2015 period C, MET trigger algorithms distros  
png  All_TRIGxe_MET_data2015_periodC.png  r1  manage  15.9 K  20150813  14:34  AntoniaStrubig  Data 2015 period C, MET trigger algorithms distros 
eps  All_TRIGxe_SUMET_data2015_periodC.eps  r1  manage  15.8 K  20150813  14:40  AntoniaStrubig  Data 2015 period C, SumMET trigger algorithms distros 
All_TRIGxe_SUMET_data2015_periodC.pdf  r1  manage  15.8 K  20150813  14:40  AntoniaStrubig  Data 2015 period C, SumMET trigger algorithms distros  
png  All_TRIGxe_SUMET_data2015_periodC.png  r1  manage  18.0 K  20150813  14:40  AntoniaStrubig  Data 2015 period C, SumMET trigger algorithms distros 
jpg  Data2018Vs2022_PFOPufit.jpg  r1  manage  338.0 K  20220929  23:21  BenCarlson  
Data2018Vs2022_PFOPufit.pdf  r1  manage  23.8 K  20220929  23:21  BenCarlson  
png  Data2018Vs2022_PFOPufit.png  r1  manage  306.2 K  20220929  23:21  BenCarlson  
eps  EF_ExSig_sqrt_SumEt.eps  r2 r1  manage  14.6 K  20131010  21:12  AllenMincer  EF Ex sigma vs sqrt(SumEt) 
EF_ExSig_sqrt_SumEt.pdf  r2 r1  manage  17.8 K  20131010  21:18  AllenMincer  EF Ex sigma vs sqrt(SumEt)  
png  EF_ExSig_sqrt_SumEt.png  r2 r1  manage  18.9 K  20131010  21:18  AllenMincer  EF Ex sigma vs sqrt(SumEt) 
eps  EF_EySig_sqrt_SumEt.eps  r2 r1  manage  14.6 K  20131010  21:22  AllenMincer  EF Ey sigma vs sqrt(SumEt) 
EF_EySig_sqrt_SumEt.pdf  r2 r1  manage  17.8 K  20131010  21:23  AllenMincer  EF Ey sigma vs sqrt(SumEt)  
png  EF_EySig_sqrt_SumEt.png  r2 r1  manage  18.7 K  20131010  21:23  AllenMincer  EF Ey sigma vs sqrt(SumEt) 
eps  EF_MET_Wmunu_comp_2011_perfnote.eps  r1  manage  12.6 K  20130425  18:02  AllenMincer  EF MET distribution for W mu nu events 
EF_MET_Wmunu_comp_2011_perfnote.pdf  r1  manage  16.5 K  20130425  18:03  AllenMincer  EF MET distribution for W mu nu events  
png  EF_MET_Wmunu_comp_2011_perfnote.png  r1  manage  17.9 K  20130425  18:03  AllenMincer  EF MET distribution for W mu nu events 
eps  EF_MEx_Slice_20.eps  r2 r1  manage  17.8 K  20131010  21:30  AllenMincer  EF METx for one SumEt slice 
EF_MEx_Slice_20.pdf  r2 r1  manage  17.0 K  20131010  21:30  AllenMincer  EF METx for one SumEt slice  
png  EF_MEx_Slice_20.png  r2 r1  manage  19.1 K  20131010  21:31  AllenMincer  EF METx for one SumEt slice 
eps  EF_MEx_Slice_70.eps  r2 r1  manage  18.4 K  20131010  21:31  AllenMincer  EF METx for another SumEt slice 
EF_MEx_Slice_70.pdf  r2 r1  manage  18.6 K  20131010  21:33  AllenMincer  EF METx for another SumEt slice  
png  EF_MEx_Slice_70.png  r2 r1  manage  21.9 K  20131010  21:33  AllenMincer  EF METx for another SumEt slice 
eps  EF_SET_Wmunu_comp_2011_perfnote.eps  r1  manage  13.8 K  20130425  18:01  AllenMincer  EF SumEt distribution for W mu nu events 
EF_SET_Wmunu_comp_2011_perfnote.pdf  r1  manage  17.4 K  20130425  18:01  AllenMincer  EF SumEt distribution for W mu nu events  
png  EF_SET_Wmunu_comp_2011_perfnote.png  r1  manage  19.3 K  20130425  18:02  AllenMincer  EF SumEt distribution for W mu nu events 
eps  EF_xe20_MET_turnon_Wmunu.eps  r1  manage  14.6 K  20130424  13:57  AllenMincer  W mu nu MET xe20 turnon curve 
EF_xe20_MET_turnon_Wmunu.pdf  r1  manage  17.3 K  20130424  13:57  AllenMincer  W mu nu MET xe20 turnon curve  
png  EF_xe20_MET_turnon_Wmunu.png  r1  manage  21.0 K  20130424  13:57  AllenMincer  W mu nu MET xe20 turnon curve 
eps  EF_xe20_xe40_noMu_rates.eps  r1  manage  92.5 K  20130422  15:59  AllenMincer  XE20_noMu to xe40_noMu 2011 trigger rates 
EF_xe20_xe40_noMu_rates.pdf  r1  manage  225.5 K  20130422  16:07  AllenMincer  EF_xe20_noMu to xe40_noMU 2011 rates  
png  EF_xe20_xe40_noMu_rates.png  r1  manage  47.4 K  20130422  16:07  AllenMincer  EF_xe20_noMu to xe40_noMU 2011 rates 
eps  EF_xe30_MET_turnon_Wmunu.eps  r1  manage  14.6 K  20130424  13:58  AllenMincer  W mu nu MET xe30 turnon curve 
EF_xe30_MET_turnon_Wmunu.pdf  r1  manage  17.3 K  20130424  13:58  AllenMincer  W mu nu MET xe30 turnon curve  
png  EF_xe30_MET_turnon_Wmunu.png  r1  manage  21.1 K  20130424  13:59  AllenMincer  W mu nu MET xe30 turnon curve 
eps  EF_xe40_MET_turnon_Wmunu.eps  r1  manage  14.6 K  20130424  13:59  AllenMincer  W mu nu MET xe40 turnon curve 
EF_xe40_MET_turnon_Wmunu.pdf  r1  manage  17.3 K  20130424  13:59  AllenMincer  W mu nu MET xe40 turnon curve  
png  EF_xe40_MET_turnon_Wmunu.png  r1  manage  21.0 K  20130424  13:59  AllenMincer  W mu nu MET xe40 turnon curve 
eps  EF_xe50_MET_turnon_Wmunu.eps  r1  manage  14.5 K  20130424  14:00  AllenMincer  W mu nu MET xe50 turnon curve 
EF_xe50_MET_turnon_Wmunu.pdf  r1  manage  17.3 K  20130424  14:00  AllenMincer  W mu nu MET xe50 turnon curve  
png  EF_xe50_MET_turnon_Wmunu.png  r1  manage  20.7 K  20130424  14:01  AllenMincer  W mu nu MET xe50 turnon curve 
eps  EF_xe60_MET_turnon_Wmunu.eps  r1  manage  14.5 K  20130424  14:01  AllenMincer  W mu nu MET xe60 turnon curve 
EF_xe60_MET_turnon_Wmunu.pdf  r1  manage  17.2 K  20130424  14:02  AllenMincer  W mu nu MET xe60 turnon curve  
png  EF_xe60_MET_turnon_Wmunu.png  r1  manage  21.0 K  20130424  14:02  AllenMincer  W mu nu MET xe60 turnon curve 
eps  EF_xe60_xe90_noMu_rates.eps  r1  manage  84.0 K  20130422  16:08  AllenMincer  EF_xe60_noMu to xe90_noMU 2011 rates 
EF_xe60_xe90_noMu_rates.pdf  r1  manage  208.2 K  20130422  16:08  AllenMincer  EF_xe60_noMu to xe90_noMU 2011 rates  
png  EF_xe60_xe90_noMu_rates.png  r1  manage  58.5 K  20130422  16:08  AllenMincer  EF_xe60_noMu to xe90_noMU 2011 rates 
eps  EF_xe70_MET_turnon_Wmunu.eps  r1  manage  14.5 K  20130424  14:02  AllenMincer  W mu nu MET xe70 turnon curve 
EF_xe70_MET_turnon_Wmunu.pdf  r1  manage  17.2 K  20130424  14:03  AllenMincer  W mu nu MET xe70 turnon curve  
png  EF_xe70_MET_turnon_Wmunu.png  r1  manage  20.3 K  20130424  14:03  AllenMincer  W mu nu MET xe70 turnon curve 
jpg  HLTRate_8143,8144_unscaled.jpg  r1  manage  301.3 K  20220929  23:21  BenCarlson  
HLTRate_8143,8144_unscaled.pdf  r1  manage  28.7 K  20220929  23:21  BenCarlson  
png  HLTRate_8143,8144_unscaled.png  r1  manage  271.8 K  20220929  23:21  BenCarlson  
jpg  HLTRate_8143_8144_unscaled.jpg  r1  manage  301.3 K  20220929  23:52  BenCarlson  
HLTRate_8143_8144_unscaled.pdf  r1  manage  28.7 K  20220929  23:52  BenCarlson  
png  HLTRate_8143_8144_unscaled.png  r1  manage  271.8 K  20220929  23:52  BenCarlson  
eps  HLT_v2.eps  r1  manage  24.8 K  20170913  13:02  MoritzBackes  
HLT_v2.pdf  r1  manage  20.7 K  20170913  13:02  MoritzBackes  
png  HLT_v2.png  r1  manage  11.9 K  20170913  13:02  MoritzBackes  
L1METdistribVSvertices.pdf  r1  manage  1977.7 K  20140115  22:07  AllenMincer  L1 MET distributions for various number of vertices  
png  L1METdistribVSvertices.png  r1  manage  88.2 K  20140115  22:08  AllenMincer  L1 MET distributions for various number of vertices 
L1XErateVSvertices.pdf  r1  manage  1976.8 K  20140115  22:10  AllenMincer  L1 MET relative rates for various number of vertices  
png  L1XErateVSvertices.png  r1  manage  103.0 K  20140115  22:10  AllenMincer  L1 MET relative rates for various number of vertices 
L1XSdistribVSvertices.pdf  r1  manage  1976.7 K  20140115  22:08  AllenMincer  L1 XS distributions for various number of vertices  
png  L1XSdistribVSvertices.png  r1  manage  88.9 K  20140115  22:09  AllenMincer  L1 XS distributions for various number of vertices 
L1XSrateVSvertices.pdf  r1  manage  1976.7 K  20140115  22:11  AllenMincer  L1 XS relative rates for various number of vertices  
png  L1XSrateVSvertices.png  r1  manage  89.0 K  20140115  22:11  AllenMincer  L1 XS relative rates for various number of vertices 
eps  L1_ExSig_sqrt_SumEt.eps  r2 r1  manage  14.4 K  20131010  21:24  AllenMincer  L1 Ex sigma vs sqrt(SumEt) 
L1_ExSig_sqrt_SumEt.pdf  r2 r1  manage  17.2 K  20131010  21:24  AllenMincer  L1 Ex sigma vs sqrt(SumEt)  
png  L1_ExSig_sqrt_SumEt.png  r2 r1  manage  19.9 K  20131010  21:25  AllenMincer  L1 Ex sigma vs sqrt(SumEt) 
eps  L1_EySig_sqrt_SumEt.eps  r2 r1  manage  14.4 K  20131010  21:25  AllenMincer  L1 Ey sigma vs sqrt(SumEt) 
L1_EySig_sqrt_SumEt.pdf  r2 r1  manage  17.2 K  20131010  21:26  AllenMincer  L1 Ey sigma vs sqrt(SumEt)  
png  L1_EySig_sqrt_SumEt.png  r2 r1  manage  19.5 K  20131010  21:26  AllenMincer  L1 Ey sigma vs sqrt(SumEt) 
eps  L1_MET_Wmunu_comp_2011_perfnote.eps  r1  manage  12.9 K  20130425  17:55  AllenMincer  L1 MET distribution for W mu nu events 
L1_MET_Wmunu_comp_2011_perfnote.pdf  r1  manage  16.5 K  20130425  17:56  AllenMincer  L1 MET distribution for W mu nu events  
png  L1_MET_Wmunu_comp_2011_perfnote.png  r1  manage  17.7 K  20130425  17:56  AllenMincer  L1 MET distribution for W mu nu events 
eps  L1_MEx_Slice_20.eps  r2 r1  manage  18.1 K  20131010  21:27  AllenMincer  L1 METx for one SumEt slice 
L1_MEx_Slice_20.pdf  r2 r1  manage  17.6 K  20131010  21:27  AllenMincer  L1 METx for one SumEt slice  
png  L1_MEx_Slice_20.png  r2 r1  manage  20.2 K  20131010  21:28  AllenMincer  L1 METx for one SumEt slice 
eps  L1_MEx_Slice_70.eps  r2 r1  manage  18.8 K  20131010  21:28  AllenMincer  L1 METx for another SumEt slice 
L1_MEx_Slice_70.pdf  r2 r1  manage  18.4 K  20131010  21:29  AllenMincer  L1 METx for another SumEt slice  
png  L1_MEx_Slice_70.png  r2 r1  manage  22.1 K  20131010  21:29  AllenMincer  L1 METx for another SumEt slice 
eps  L1_SET_Wmunu_comp_2011_perfnote.eps  r1  manage  14.4 K  20130425  18:00  AllenMincer  L1 SumEt distribution for W mu nu events 
L1_SET_Wmunu_comp_2011_perfnote.pdf  r1  manage  17.6 K  20130425  18:00  AllenMincer  L1 SumEt distribution for W mu nu events  
png  L1_SET_Wmunu_comp_2011_perfnote.png  r1  manage  20.2 K  20130425  18:00  AllenMincer  L1 SumEt distribution for W mu nu events 
eps  L1_v3.eps  r1  manage  30.7 K  20170913  13:02  MoritzBackes  
L1_v3.pdf  r1  manage  48.4 K  20170913  13:02  MoritzBackes  
png  L1_v3.png  r1  manage  13.6 K  20170913  13:02  MoritzBackes  
METDIST45.pdf  r1  manage  15.4 K  20210416  19:39  BenCarlson  
png  METDIST45.png  r1  manage  77.4 K  20210416  19:39  BenCarlson  
eps  MET_mu.eps  r1  manage  17.2 K  20130422  23:03  AllenMincer  EF met distributions for different mu in 2011 
MET_mu.pdf  r1  manage  18.9 K  20130422  23:01  AllenMincer  EF met distributions for different mu in 2011  
png  MET_mu.png  r1  manage  31.1 K  20130422  23:02  AllenMincer  EF met distributions for different mu in 2011 
eps  Preliminary3_Wmunu_L1.eps  r1  manage  18.6 K  20151130  22:27  PierreHuguesBeauchemin  
Preliminary3_Wmunu_L1.pdf  r1  manage  19.0 K  20151130  20:36  PierreHuguesBeauchemin  
png  Preliminary3_Wmunu_L1.png  r1  manage  15.9 K  20151130  22:27  PierreHuguesBeauchemin  
eps  Preliminary_Wmunu_L1_er.eps  r1  manage  21.1 K  20151130  22:27  PierreHuguesBeauchemin  
Preliminary_Wmunu_L1_er.pdf  r1  manage  20.7 K  20151130  20:36  PierreHuguesBeauchemin  
png  Preliminary_Wmunu_L1_er.png  r1  manage  19.1 K  20151130  22:27  PierreHuguesBeauchemin  
eps  Preliminary_Zmumu_L1_er.eps  r1  manage  26.0 K  20151130  22:27  PierreHuguesBeauchemin  
Preliminary_Zmumu_L1_er.pdf  r1  manage  21.8 K  20151130  20:36  PierreHuguesBeauchemin  
png  Preliminary_Zmumu_L1_er.png  r1  manage  19.0 K  20151130  22:27  PierreHuguesBeauchemin  
eps  Run2_Eff_AvgMu.eps  r1  manage  15.8 K  20190318  15:21  GabrielGallardo  MET Trigger efficiencies on full Run 2 data 
Run2_Eff_AvgMu.pdf  r1  manage  15.6 K  20190318  15:21  GabrielGallardo  MET Trigger efficiencies on full Run 2 data  
png  Run2_Eff_AvgMu.png  r1  manage  14.4 K  20190318  15:21  GabrielGallardo  MET Trigger efficiencies on full Run 2 data 
eps  Run2_Eff_Zpt.eps  r1  manage  21.3 K  20190318  15:21  GabrielGallardo  MET Trigger efficiencies on full Run 2 data 
Run2_Eff_Zpt.pdf  r1  manage  19.8 K  20190318  15:21  GabrielGallardo  MET Trigger efficiencies on full Run 2 data  
png  Run2_Eff_Zpt.png  r1  manage  17.7 K  20190318  15:21  GabrielGallardo  MET Trigger efficiencies on full Run 2 data 
Wtaunu_EF_xe_vs_sqrset.pdf  r1  manage  39.6 K  20140110  20:27  AllenMincer  EF MET vs sqrt(sumet) 2010 minbias data and W tau nu simulation  
png  Wtaunu_EF_xe_vs_sqrset.png  r1  manage  37.2 K  20140110  20:27  AllenMincer  EF MET vs sqrt(sumet) 2010 minbias data and W tau nu simulation 
Wtaunu_L2_xe_vs_sqrset.pdf  r1  manage  39.4 K  20140110  20:25  AllenMincer  L1 MET vs sqrt(sumet) 2010 minbias data and W tau nu simulation  
png  Wtaunu_L2_xe_vs_sqrset.png  r1  manage  38.5 K  20140110  20:26  AllenMincer  L1 MET vs sqrt(sumet) 2010 minbias data and W tau nu simulation 
eps  XS_mu_cleaned.eps  r1  manage  17.7 K  20130422  23:10  AllenMincer  EF XS distributions for various mu values in 2011 
XS_mu_cleaned.pdf  r1  manage  19.0 K  20130422  23:10  AllenMincer  EF XS distributions for various mu values in 2011  
png  XS_mu_cleaned.png  r1  manage  26.9 K  20130422  23:11  AllenMincer  EF XS distributions for various mu values in 2011 
eps  ZH_nunubb_atlas.eps  r1  manage  19.9 K  20120611  19:23  FlorianBernlochner  MET 2012 Performance Plots 
ZH_nunubb_atlas.pdf  r1  manage  48.9 K  20120611  19:23  FlorianBernlochner  MET 2012 Performance Plots  
png  ZH_nunubb_atlas.png  r1  manage  66.1 K  20120611  19:23  FlorianBernlochner  MET 2012 Performance Plots 
eps  ZmumuEff_L1_XE50_Z_pT.eps  r1  manage  15.7 K  20170913  12:58  MoritzBackes  September 2017 efficiency plots 
ZmumuEff_L1_XE50_Z_pT.pdf  r1  manage  16.9 K  20170913  12:58  MoritzBackes  September 2017 efficiency plots  
png  ZmumuEff_L1_XE50_Z_pT.png  r1  manage  14.1 K  20170913  12:58  MoritzBackes  September 2017 efficiency plots 
eps  ZmumuEff_L1_XE50_Z_pT_pu.eps  r1  manage  12.8 K  20170913  12:58  MoritzBackes  September 2017 efficiency plots 
ZmumuEff_L1_XE50_Z_pT_pu.pdf  r1  manage  14.9 K  20170913  12:58  MoritzBackes  September 2017 efficiency plots  
png  ZmumuEff_L1_XE50_Z_pT_pu.png  r1  manage  11.7 K  20170913  12:58  MoritzBackes  September 2017 efficiency plots 
eps  data12_MinBias_h_EF_XE_vs_MET_RefFinal_scatterplot_v3.eps  r1  manage  186.9 K  20150724  20:56  AllenMincer  EF Cell MET versus RefFinal MET 
data12_MinBias_h_EF_XE_vs_MET_RefFinal_scatterplot_v3.pdf  r1  manage  45.0 K  20150724  20:56  AllenMincer  EF Cell MET versus RefFinal MET  
png  data12_MinBias_h_EF_XE_vs_MET_RefFinal_scatterplot_v3.png  r1  manage  15.1 K  20150724  20:56  AllenMincer  EF Cell MET versus RefFinal MET 
eps  data12_MinBias_h_EF_feb_XE_vs_MET_RefFinal_scatterplot_v3.eps  r1  manage  154.1 K  20150724  20:38  AllenMincer  L2 FEB vs RefFinal MET 
data12_MinBias_h_EF_feb_XE_vs_MET_RefFinal_scatterplot_v3.pdf  r1  manage  40.3 K  20150724  20:38  AllenMincer  L2 FEB versus RefFinal MET  
png  data12_MinBias_h_EF_feb_XE_vs_MET_RefFinal_scatterplot_v3.png  r1  manage  16.0 K  20150724  20:38  AllenMincer  L2 FEB versus RefFinal MET 
eps  data12_MinBias_h_EF_topocl_EM_XE_vs_MET_RefFinal_scatterplot_v3.eps  r1  manage  205.8 K  20150724  20:57  AllenMincer  EF topocl EM scale versus RefFinal MET 
data12_MinBias_h_EF_topocl_EM_XE_vs_MET_RefFinal_scatterplot_v3.pdf  r1  manage  49.1 K  20150724  20:57  AllenMincer  EF topocl EM scale versus RefFinal MET  
png  data12_MinBias_h_EF_topocl_EM_XE_vs_MET_RefFinal_scatterplot_v3.png  r1  manage  14.3 K  20150724  20:57  AllenMincer  EF topocl EM scale versus RefFinal MET 
eps  data12_MinBias_h_EF_topocl_XE_vs_MET_RefFinal_scatterplot_v3.eps  r1  manage  210.7 K  20150724  20:58  AllenMincer  EF hadcal topo cluster versus RefFinal MET 
data12_MinBias_h_EF_topocl_XE_vs_MET_RefFinal_scatterplot_v3.pdf  r1  manage  50.1 K  20150724  20:58  AllenMincer  EF hadcal topo cluster versus RefFinal MET  
png  data12_MinBias_h_EF_topocl_XE_vs_MET_RefFinal_scatterplot_v3.png  r1  manage  16.6 K  20150724  20:58  AllenMincer  EF hadcal topo cluster versus RefFinal MET 
eps  data12_MinBias_h_L2_XE_vs_MET_RefFinal_scatterplot_v3.eps  r1  manage  159.7 K  20150724  20:31  AllenMincer  L1 MET vs RefFinal MET 
data12_MinBias_h_L2_XE_vs_MET_RefFinal_scatterplot_v3.pdf  r1  manage  40.3 K  20150724  20:31  AllenMincer  L1 MET vs RefFinal MET  
png  data12_MinBias_h_L2_XE_vs_MET_RefFinal_scatterplot_v3.png  r1  manage  14.8 K  20150724  20:31  AllenMincer  L1 MET vs RefFinal MET 
eps  dataWmunuEff_inTimePileup.eps  r1  manage  12.1 K  20170703  22:19  MoritzBackes  2017 performance plots for EPS 
dataWmunuEff_inTimePileup.pdf  r1  manage  15.5 K  20170703  22:19  MoritzBackes  2017 performance plots for EPS  
png  dataWmunuEff_inTimePileup.png  r1  manage  13.8 K  20170703  22:19  MoritzBackes  2017 performance plots for EPS 
eps  dataWmunuEff_offlineMETNoMu.eps  r1  manage  16.0 K  20170703  22:19  MoritzBackes  2017 performance plots for EPS 
dataWmunuEff_offlineMETNoMu.pdf  r1  manage  18.5 K  20170703  22:19  MoritzBackes  2017 performance plots for EPS  
png  dataWmunuEff_offlineMETNoMu.png  r1  manage  16.4 K  20170703  22:19  MoritzBackes  2017 performance plots for EPS 
eff_1_style_preliminary.pdf  r1  manage  15.2 K  20210416  19:38  BenCarlson  
png  eff_1_style_preliminary.png  r1  manage  138.7 K  20210416  19:38  BenCarlson  
eff_2_style_preliminary.pdf  r1  manage  14.9 K  20210416  19:38  BenCarlson  
png  eff_2_style_preliminary.png  r1  manage  129.9 K  20210416  19:38  BenCarlson  
eff_3_style_preliminary.pdf  r1  manage  14.8 K  20210416  19:38  BenCarlson  
png  eff_3_style_preliminary.png  r1  manage  123.6 K  20210416  19:38  BenCarlson  
eff_4_style_preliminary.pdf  r1  manage  14.9 K  20210416  19:38  BenCarlson  
png  eff_4_style_preliminary.png  r1  manage  124.9 K  20210416  19:38  BenCarlson  
eps  effcurve_ZHnubmc15_oldLUT.eps  r1  manage  14.4 K  20150724  21:05  AllenMincer  L1 ZH nu nu b b simulated efficiency 
effcurve_ZHnubmc15_oldLUT.pdf  r1  manage  16.6 K  20150724  21:05  AllenMincer  L1 ZH nu nu b b simulated efficiency  
png  effcurve_ZHnubmc15_oldLUT.png  r1  manage  19.1 K  20150724  21:05  AllenMincer  L1 ZH nu nu b b simulated efficiency 
eps  effcurve_ttbarmc15_oldLUT.eps  r1  manage  11.8 K  20150724  21:06  AllenMincer  L1 KF t tbar simulated efficiency 
effcurve_ttbarmc15_oldLUT.pdf  r1  manage  15.6 K  20150724  21:06  AllenMincer  L1 KF t tbar simulated efficiency  
png  effcurve_ttbarmc15_oldLUT.png  r1  manage  18.3 K  20150724  21:06  AllenMincer  L1 KF t tbar simulated efficiency 
eps  etxs_vs_mu_2017.eps  r1  manage  12.6 K  20170703  22:19  MoritzBackes  2017 performance plots for EPS 
etxs_vs_mu_2017.pdf  r1  manage  21.8 K  20170703  22:19  MoritzBackes  2017 performance plots for EPS  
png  etxs_vs_mu_2017.png  r1  manage  15.0 K  20170703  22:19  MoritzBackes  2017 performance plots for EPS 
eps  mex_ef_l2_l1_fit.eps  r1  manage  13.4 K  20120611  19:23  FlorianBernlochner  MET 2012 Performance Plots 
mex_ef_l2_l1_fit.pdf  r1  manage  17.5 K  20120611  19:23  FlorianBernlochner  MET 2012 Performance Plots  
png  mex_ef_l2_l1_fit.png  r1  manage  91.1 K  20120611  19:23  FlorianBernlochner  MET 2012 Performance Plots 
eps  mex_tcl_ef_rf_fit.eps  r1  manage  15.3 K  20120611  19:23  FlorianBernlochner  MET 2012 Performance Plots 
mex_tcl_ef_rf_fit.pdf  r1  manage  18.4 K  20120611  19:23  FlorianBernlochner  MET 2012 Performance Plots  
png  mex_tcl_ef_rf_fit.png  r1  manage  105.5 K  20120611  19:23  FlorianBernlochner  MET 2012 Performance Plots 
eps  oldLUT.eps  r1  manage  20.6 K  20150724  21:03  AllenMincer  L1 KF lookup table 
oldLUT.pdf  r1  manage  15.4 K  20150724  21:03  AllenMincer  L1 KF lookup table  
png  oldLUT.png  r1  manage  34.5 K  20150724  21:03  AllenMincer  L1 KF lookup table 
ttbar_cell_turnon_L1num_preliminary.pdf  r1  manage  17.9 K  20210416  19:39  BenCarlson  
png  ttbar_cell_turnon_L1num_preliminary.png  r1  manage  88.7 K  20210416  19:39  BenCarlson  
ttbar_cell_turnon_preliminary.pdf  r1  manage  17.8 K  20210416  19:39  BenCarlson  
png  ttbar_cell_turnon_preliminary.png  r1  manage  84.7 K  20210416  19:39  BenCarlson 