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 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. | 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 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. | 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 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. | 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 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. | png eps pdf |
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. | 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 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. | 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 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. | 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 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. | png eps pdf |
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. | 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 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. | 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 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. | 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 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. | pdf png |
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. |
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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. |
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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. | png pdf eps |
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. |
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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. |
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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. |
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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. |
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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. | png pdf eps |
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 E_{T}^{miss} (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 E_{T}^{miss} with severe pile-up 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 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 E_{T}^{miss} 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. |
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Turn-on curve of the corrected versus the original L1 E_{T}^{miss} 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 E_{T}^{miss} at similar conditions. |
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Turn-on curve of the corrected versus the default L1 E_{T}^{miss} 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 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/ATL-DAQ-PUB-2018-001/
http://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PUBNOTES/ATL-DAQ-PUB-2017-002/
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. |
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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. |
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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. |
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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. |
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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. | 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. |
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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. |
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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. | 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. |
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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. |
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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. |
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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 x-component of the missing momentum ( |
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Trigger Level 1 distributions of the x-component of the missing momentum ( |
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Trigger Event Filter level distributions of the x-component of the missing momentum ( |
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Trigger Event Filter level distributions of the x-component of the missing momentum ( |
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Standard deviation of the x-component of the missing momentum ( |
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Standard deviation of the y-component of the missing momentum ( |
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Standard deviation of the x-component of the missing momentum ( |
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Standard deviation of the y-component 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. |
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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. |
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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 all-level 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 all-level 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 all-level 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 all-level 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 all-level 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 all-level 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 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. |
png eps |
Comparison of E_{T}^{MISS} 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. |
png eps |
Comparison of ΣE_{T} 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. |
png eps |
Comparison of ΣE_{T} 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. |
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 pile-up 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 pile-up 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 pile-up 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 pile-up 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 heavy-ion collision events. |
png eps |
EF ΣE_{T} correlation with the value of ΣE_{T} computed offline for heavy-ion 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 EF-only E_{T}^{MISS} threshold at 30 GeV as a function of MET_Topo ETMISS for W→eν candidates. |
png eps |
Efficiency of the EF-only E_{T}^{MISS} threshold at 40 GeV as a function of MET_Topo ETMISS for W→eν candidates. |
png eps |
Efficiency of the EF-only E_{T}^{MISS} threshold at 30 GeV as a function of MET_Topo ETMISS for W→μν candidates. |
png eps |
Efficiency of the EF-only 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 EF-only ΣE_{T} threshold at 200 GeV as a function of MET_Topo ΣE_{T} for W→eν candidates. |
png eps |
Efficiency of the EF-only ΣE_{T} threshold at 300 GeV as a function of MET_Topo ΣE_{T} for W→eν candidates. |
png eps |
Efficiency of the EF-only ΣE_{T} threshold at 200 GeV as a function of MET_Topo ΣE_{T} for W→μν candidates. |
png eps |
Efficiency of the EF-only 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 data-taking 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 data-taking 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 - 12-Jun-2011
Responsible: JoergStelzer
Subject: public
I | Attachment | History | Action | Size | Date | Who | Comment |
---|---|---|---|---|---|---|---|
2016-05-16-UpdatedTurnOns.pdf | r1 | manage | 20.3 K | 2016-05-24 - 16:41 | AlanBarr | MET trigger turn-ons May 2016 | |
png | 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 |
eps | 2018May_Efficiency_Zmumu.eps | r1 | manage | 18.6 K | 2018-05-27 - 21:01 | KenjiHamano | 2018 May Efficiency Plot |
2018May_Efficiency_Zmumu.pdf | r1 | manage | 18.4 K | 2018-05-27 - 21:01 | KenjiHamano | 2018 May Efficiency Plot | |
png | 2018May_Efficiency_Zmumu.png | r1 | manage | 52.1 K | 2018-05-27 - 21:01 | KenjiHamano | 2018 May Efficiency Plot |
eps | 2018May_Rate_HLT.eps | r1 | manage | 23.7 K | 2018-05-27 - 18:14 | KenjiHamano | 2018 May Rate plot |
2018May_Rate_HLT.pdf | r1 | manage | 30.8 K | 2018-05-27 - 18:14 | KenjiHamano | 2018 May Rate plot | |
png | 2018May_Rate_HLT.png | r1 | manage | 15.9 K | 2018-05-27 - 18:14 | KenjiHamano | 2018 May Rate plot |
eps | 2018May_Stability_Zmumu.eps | r1 | manage | 18.5 K | 2018-05-27 - 21:01 | KenjiHamano | 2018 May Efficiency Plot |
2018May_Stability_Zmumu.pdf | r1 | manage | 17.2 K | 2018-05-27 - 21:01 | KenjiHamano | 2018 May Efficiency Plot | |
png | 2018May_Stability_Zmumu.png | r1 | manage | 48.4 K | 2018-05-27 - 21:01 | KenjiHamano | 2018 May Efficiency Plot |
eps | All_Offline_PXE35_data2015_periodC.eps | r1 | manage | 29.3 K | 2015-08-13 - 14:42 | AntoniaStrubig | turn-on xe35, all algos |
All_Offline_PXE35_data2015_periodC.pdf | r1 | manage | 22.3 K | 2015-08-13 - 14:42 | AntoniaStrubig | turn-on xe35, all algos | |
png | All_Offline_PXE35_data2015_periodC.png | r1 | manage | 26.3 K | 2015-08-13 - 14:42 | AntoniaStrubig | turn-on xe35, all algos |
eps | All_Offline_PXE50_data2015_periodC.eps | r1 | manage | 29.7 K | 2015-08-13 - 14:45 | AntoniaStrubig | turn-on xe50, all algos |
All_Offline_PXE50_data2015_periodC.pdf | r1 | manage | 22.7 K | 2015-08-13 - 14:45 | AntoniaStrubig | turn-on xe50, all algos | |
png | All_Offline_PXE50_data2015_periodC.png | r1 | manage | 26.4 K | 2015-08-13 - 14:45 | AntoniaStrubig | turn-on xe50, all algos |
eps | All_SumETResolution_Errors_data2015_periodC.eps | r1 | manage | 10.2 K | 2015-08-13 - 14:40 | AntoniaStrubig | sumEt resolution all algos |
All_SumETResolution_Errors_data2015_periodC.pdf | r1 | manage | 15.2 K | 2015-08-13 - 14:40 | AntoniaStrubig | sumEt resolution all algos | |
png | All_SumETResolution_Errors_data2015_periodC.png | r1 | manage | 19.6 K | 2015-08-13 - 14:40 | AntoniaStrubig | sumEt resolution all algos |
eps | 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 |
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 | |
png | 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 |
eps | 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 |
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 | |
png | 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 |
eps | EF_ExSig_sqrt_SumEt.eps | r2 r1 | manage | 14.6 K | 2013-10-10 - 21:12 | AllenMincer | EF Ex sigma vs sqrt(SumEt) |
EF_ExSig_sqrt_SumEt.pdf | r2 r1 | manage | 17.8 K | 2013-10-10 - 21:18 | AllenMincer | EF Ex sigma vs sqrt(SumEt) | |
png | EF_ExSig_sqrt_SumEt.png | r2 r1 | manage | 18.9 K | 2013-10-10 - 21:18 | AllenMincer | EF Ex sigma vs sqrt(SumEt) |
eps | EF_EySig_sqrt_SumEt.eps | r2 r1 | manage | 14.6 K | 2013-10-10 - 21:22 | AllenMincer | EF Ey sigma vs sqrt(SumEt) |
EF_EySig_sqrt_SumEt.pdf | r2 r1 | manage | 17.8 K | 2013-10-10 - 21:23 | AllenMincer | EF Ey sigma vs sqrt(SumEt) | |
png | EF_EySig_sqrt_SumEt.png | r2 r1 | manage | 18.7 K | 2013-10-10 - 21:23 | AllenMincer | EF Ey sigma vs sqrt(SumEt) |
eps | 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 |
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 | |
png | 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 |
eps | EF_MEx_Slice_20.eps | r2 r1 | manage | 17.8 K | 2013-10-10 - 21:30 | AllenMincer | EF METx for one SumEt slice |
EF_MEx_Slice_20.pdf | r2 r1 | manage | 17.0 K | 2013-10-10 - 21:30 | AllenMincer | EF METx for one SumEt slice | |
png | EF_MEx_Slice_20.png | r2 r1 | manage | 19.1 K | 2013-10-10 - 21:31 | AllenMincer | EF METx for one SumEt slice |
eps | EF_MEx_Slice_70.eps | r2 r1 | manage | 18.4 K | 2013-10-10 - 21:31 | AllenMincer | EF METx for another SumEt slice |
EF_MEx_Slice_70.pdf | r2 r1 | manage | 18.6 K | 2013-10-10 - 21:33 | AllenMincer | EF METx for another SumEt slice | |
png | EF_MEx_Slice_70.png | r2 r1 | manage | 21.9 K | 2013-10-10 - 21:33 | AllenMincer | EF METx for another SumEt slice |
eps | 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 |
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 | |
png | 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 |
eps | 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 |
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 | |
png | 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 |
eps | 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 |
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 | |
png | 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 |
eps | 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 |
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 | |
png | 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 |
eps | 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 |
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 | |
png | 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 |
eps | 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 |
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 | |
png | 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 |
eps | 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 |
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 | |
png | 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 |
eps | 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 |
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 | |
png | 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 |
eps | 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 |
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 | |
png | 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 |
eps | HLT_v2.eps | r1 | manage | 24.8 K | 2017-09-13 - 13:02 | MoritzBackes | |
HLT_v2.pdf | r1 | manage | 20.7 K | 2017-09-13 - 13:02 | MoritzBackes | ||
png | HLT_v2.png | r1 | manage | 11.9 K | 2017-09-13 - 13:02 | MoritzBackes | |
L1METdistribVSvertices.pdf | r1 | manage | 1977.7 K | 2014-01-15 - 22:07 | AllenMincer | L1 MET distributions for various number of vertices | |
png | L1METdistribVSvertices.png | r1 | manage | 88.2 K | 2014-01-15 - 22:08 | AllenMincer | L1 MET distributions for various number of vertices |
L1XErateVSvertices.pdf | r1 | manage | 1976.8 K | 2014-01-15 - 22:10 | AllenMincer | L1 MET relative rates for various number of vertices | |
png | L1XErateVSvertices.png | r1 | manage | 103.0 K | 2014-01-15 - 22:10 | AllenMincer | L1 MET relative rates for various number of vertices |
L1XSdistribVSvertices.pdf | r1 | manage | 1976.7 K | 2014-01-15 - 22:08 | AllenMincer | L1 XS distributions for various number of vertices | |
png | L1XSdistribVSvertices.png | r1 | manage | 88.9 K | 2014-01-15 - 22:09 | AllenMincer | L1 XS distributions for various number of vertices |
L1XSrateVSvertices.pdf | r1 | manage | 1976.7 K | 2014-01-15 - 22:11 | AllenMincer | L1 XS relative rates for various number of vertices | |
png | L1XSrateVSvertices.png | r1 | manage | 89.0 K | 2014-01-15 - 22:11 | AllenMincer | L1 XS relative rates for various number of vertices |
eps | L1_ExSig_sqrt_SumEt.eps | r2 r1 | manage | 14.4 K | 2013-10-10 - 21:24 | AllenMincer | L1 Ex sigma vs sqrt(SumEt) |
L1_ExSig_sqrt_SumEt.pdf | r2 r1 | manage | 17.2 K | 2013-10-10 - 21:24 | AllenMincer | L1 Ex sigma vs sqrt(SumEt) | |
png | L1_ExSig_sqrt_SumEt.png | r2 r1 | manage | 19.9 K | 2013-10-10 - 21:25 | AllenMincer | L1 Ex sigma vs sqrt(SumEt) |
eps | L1_EySig_sqrt_SumEt.eps | r2 r1 | manage | 14.4 K | 2013-10-10 - 21:25 | AllenMincer | L1 Ey sigma vs sqrt(SumEt) |
L1_EySig_sqrt_SumEt.pdf | r2 r1 | manage | 17.2 K | 2013-10-10 - 21:26 | AllenMincer | L1 Ey sigma vs sqrt(SumEt) | |
png | L1_EySig_sqrt_SumEt.png | r2 r1 | manage | 19.5 K | 2013-10-10 - 21:26 | AllenMincer | L1 Ey sigma vs sqrt(SumEt) |
eps | 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 |
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 | |
png | 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 |
eps | L1_MEx_Slice_20.eps | r2 r1 | manage | 18.1 K | 2013-10-10 - 21:27 | AllenMincer | L1 METx for one SumEt slice |
L1_MEx_Slice_20.pdf | r2 r1 | manage | 17.6 K | 2013-10-10 - 21:27 | AllenMincer | L1 METx for one SumEt slice | |
png | L1_MEx_Slice_20.png | r2 r1 | manage | 20.2 K | 2013-10-10 - 21:28 | AllenMincer | L1 METx for one SumEt slice |
eps | L1_MEx_Slice_70.eps | r2 r1 | manage | 18.8 K | 2013-10-10 - 21:28 | AllenMincer | L1 METx for another SumEt slice |
L1_MEx_Slice_70.pdf | r2 r1 | manage | 18.4 K | 2013-10-10 - 21:29 | AllenMincer | L1 METx for another SumEt slice | |
png | L1_MEx_Slice_70.png | r2 r1 | manage | 22.1 K | 2013-10-10 - 21:29 | AllenMincer | L1 METx for another SumEt slice |
eps | 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 |
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 | |
png | 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 |
eps | L1_v3.eps | r1 | manage | 30.7 K | 2017-09-13 - 13:02 | MoritzBackes | |
L1_v3.pdf | r1 | manage | 48.4 K | 2017-09-13 - 13:02 | MoritzBackes | ||
png | L1_v3.png | r1 | manage | 13.6 K | 2017-09-13 - 13:02 | MoritzBackes | |
eps | MET_mu.eps | r1 | manage | 17.2 K | 2013-04-22 - 23:03 | AllenMincer | EF met distributions for different mu in 2011 |
MET_mu.pdf | r1 | manage | 18.9 K | 2013-04-22 - 23:01 | AllenMincer | EF met distributions for different mu in 2011 | |
png | MET_mu.png | r1 | manage | 31.1 K | 2013-04-22 - 23:02 | AllenMincer | EF met distributions for different mu in 2011 |
eps | Preliminary3_Wmunu_L1.eps | r1 | manage | 18.6 K | 2015-11-30 - 22:27 | PierreHuguesBeauchemin | |
Preliminary3_Wmunu_L1.pdf | r1 | manage | 19.0 K | 2015-11-30 - 20:36 | PierreHuguesBeauchemin | ||
png | Preliminary3_Wmunu_L1.png | r1 | manage | 15.9 K | 2015-11-30 - 22:27 | PierreHuguesBeauchemin | |
eps | Preliminary_Wmunu_L1_er.eps | r1 | manage | 21.1 K | 2015-11-30 - 22:27 | PierreHuguesBeauchemin | |
Preliminary_Wmunu_L1_er.pdf | r1 | manage | 20.7 K | 2015-11-30 - 20:36 | PierreHuguesBeauchemin | ||
png | Preliminary_Wmunu_L1_er.png | r1 | manage | 19.1 K | 2015-11-30 - 22:27 | PierreHuguesBeauchemin | |
eps | Preliminary_Zmumu_L1_er.eps | r1 | manage | 26.0 K | 2015-11-30 - 22:27 | PierreHuguesBeauchemin | |
Preliminary_Zmumu_L1_er.pdf | r1 | manage | 21.8 K | 2015-11-30 - 20:36 | PierreHuguesBeauchemin | ||
png | Preliminary_Zmumu_L1_er.png | r1 | manage | 19.0 K | 2015-11-30 - 22:27 | PierreHuguesBeauchemin | |
eps | Run2_Eff_AvgMu.eps | r1 | manage | 15.8 K | 2019-03-18 - 15:21 | GabrielEmmanuelGallardo | MET Trigger efficiencies on full Run 2 data |
Run2_Eff_AvgMu.pdf | r1 | manage | 15.6 K | 2019-03-18 - 15:21 | GabrielEmmanuelGallardo | MET Trigger efficiencies on full Run 2 data | |
png | Run2_Eff_AvgMu.png | r1 | manage | 14.4 K | 2019-03-18 - 15:21 | GabrielEmmanuelGallardo | MET Trigger efficiencies on full Run 2 data |
eps | Run2_Eff_Zpt.eps | r1 | manage | 21.3 K | 2019-03-18 - 15:21 | GabrielEmmanuelGallardo | MET Trigger efficiencies on full Run 2 data |
Run2_Eff_Zpt.pdf | r1 | manage | 19.8 K | 2019-03-18 - 15:21 | GabrielEmmanuelGallardo | MET Trigger efficiencies on full Run 2 data | |
png | Run2_Eff_Zpt.png | r1 | manage | 17.7 K | 2019-03-18 - 15:21 | GabrielEmmanuelGallardo | MET Trigger efficiencies on full Run 2 data |
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 | |
png | 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 |
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 | |
png | 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 |
eps | XS_mu_cleaned.eps | r1 | manage | 17.7 K | 2013-04-22 - 23:10 | AllenMincer | EF XS distributions for various mu values in 2011 |
XS_mu_cleaned.pdf | r1 | manage | 19.0 K | 2013-04-22 - 23:10 | AllenMincer | EF XS distributions for various mu values in 2011 | |
png | XS_mu_cleaned.png | r1 | manage | 26.9 K | 2013-04-22 - 23:11 | AllenMincer | EF XS distributions for various mu values in 2011 |
eps | ZH_nunubb_atlas.eps | r1 | manage | 19.9 K | 2012-06-11 - 19:23 | FlorianBernlochner | MET 2012 Performance Plots |
ZH_nunubb_atlas.pdf | r1 | manage | 48.9 K | 2012-06-11 - 19:23 | FlorianBernlochner | MET 2012 Performance Plots | |
png | ZH_nunubb_atlas.png | r1 | manage | 66.1 K | 2012-06-11 - 19:23 | FlorianBernlochner | MET 2012 Performance Plots |
eps | ZmumuEff_L1_XE50_Z_pT.eps | r1 | manage | 15.7 K | 2017-09-13 - 12:58 | MoritzBackes | September 2017 efficiency plots |
ZmumuEff_L1_XE50_Z_pT.pdf | r1 | manage | 16.9 K | 2017-09-13 - 12:58 | MoritzBackes | September 2017 efficiency plots | |
png | ZmumuEff_L1_XE50_Z_pT.png | r1 | manage | 14.1 K | 2017-09-13 - 12:58 | MoritzBackes | September 2017 efficiency plots |
eps | ZmumuEff_L1_XE50_Z_pT_pu.eps | r1 | manage | 12.8 K | 2017-09-13 - 12:58 | MoritzBackes | September 2017 efficiency plots |
ZmumuEff_L1_XE50_Z_pT_pu.pdf | r1 | manage | 14.9 K | 2017-09-13 - 12:58 | MoritzBackes | September 2017 efficiency plots | |
png | ZmumuEff_L1_XE50_Z_pT_pu.png | r1 | manage | 11.7 K | 2017-09-13 - 12:58 | MoritzBackes | September 2017 efficiency plots |
eps | 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 |
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 | |
png | 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 |
eps | 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 |
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 | |
png | 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 |
eps | 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 |
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 | |
png | 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 |
eps | 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 |
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 | |
png | 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 |
eps | 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 |
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 | |
png | 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 |
eps | dataWmunuEff_inTimePileup.eps | r1 | manage | 12.1 K | 2017-07-03 - 22:19 | MoritzBackes | 2017 performance plots for EPS |
dataWmunuEff_inTimePileup.pdf | r1 | manage | 15.5 K | 2017-07-03 - 22:19 | MoritzBackes | 2017 performance plots for EPS | |
png | dataWmunuEff_inTimePileup.png | r1 | manage | 13.8 K | 2017-07-03 - 22:19 | MoritzBackes | 2017 performance plots for EPS |
eps | dataWmunuEff_offlineMETNoMu.eps | r1 | manage | 16.0 K | 2017-07-03 - 22:19 | MoritzBackes | 2017 performance plots for EPS |
dataWmunuEff_offlineMETNoMu.pdf | r1 | manage | 18.5 K | 2017-07-03 - 22:19 | MoritzBackes | 2017 performance plots for EPS | |
png | dataWmunuEff_offlineMETNoMu.png | r1 | manage | 16.4 K | 2017-07-03 - 22:19 | MoritzBackes | 2017 performance plots for EPS |
eps | effcurve_ZHnubmc15_oldLUT.eps | r1 | manage | 14.4 K | 2015-07-24 - 21:05 | AllenMincer | L1 ZH nu nu b b simulated efficiency |
effcurve_ZHnubmc15_oldLUT.pdf | r1 | manage | 16.6 K | 2015-07-24 - 21:05 | AllenMincer | L1 ZH nu nu b b simulated efficiency | |
png | effcurve_ZHnubmc15_oldLUT.png | r1 | manage | 19.1 K | 2015-07-24 - 21:05 | AllenMincer | L1 ZH nu nu b b simulated efficiency |
eps | effcurve_ttbarmc15_oldLUT.eps | r1 | manage | 11.8 K | 2015-07-24 - 21:06 | AllenMincer | L1 KF t tbar simulated efficiency |
effcurve_ttbarmc15_oldLUT.pdf | r1 | manage | 15.6 K | 2015-07-24 - 21:06 | AllenMincer | L1 KF t tbar simulated efficiency | |
png | effcurve_ttbarmc15_oldLUT.png | r1 | manage | 18.3 K | 2015-07-24 - 21:06 | AllenMincer | L1 KF t tbar simulated efficiency |
eps | etxs_vs_mu_2017.eps | r1 | manage | 12.6 K | 2017-07-03 - 22:19 | MoritzBackes | 2017 performance plots for EPS |
etxs_vs_mu_2017.pdf | r1 | manage | 21.8 K | 2017-07-03 - 22:19 | MoritzBackes | 2017 performance plots for EPS | |
png | etxs_vs_mu_2017.png | r1 | manage | 15.0 K | 2017-07-03 - 22:19 | MoritzBackes | 2017 performance plots for EPS |
eps | mex_ef_l2_l1_fit.eps | r1 | manage | 13.4 K | 2012-06-11 - 19:23 | FlorianBernlochner | MET 2012 Performance Plots |
mex_ef_l2_l1_fit.pdf | r1 | manage | 17.5 K | 2012-06-11 - 19:23 | FlorianBernlochner | MET 2012 Performance Plots | |
png | mex_ef_l2_l1_fit.png | r1 | manage | 91.1 K | 2012-06-11 - 19:23 | FlorianBernlochner | MET 2012 Performance Plots |
eps | mex_tcl_ef_rf_fit.eps | r1 | manage | 15.3 K | 2012-06-11 - 19:23 | FlorianBernlochner | MET 2012 Performance Plots |
mex_tcl_ef_rf_fit.pdf | r1 | manage | 18.4 K | 2012-06-11 - 19:23 | FlorianBernlochner | MET 2012 Performance Plots | |
png | mex_tcl_ef_rf_fit.png | r1 | manage | 105.5 K | 2012-06-11 - 19:23 | FlorianBernlochner | MET 2012 Performance Plots |
eps | oldLUT.eps | r1 | manage | 20.6 K | 2015-07-24 - 21:03 | AllenMincer | L1 KF lookup table |
oldLUT.pdf | r1 | manage | 15.4 K | 2015-07-24 - 21:03 | AllenMincer | L1 KF lookup table | |
png | oldLUT.png | r1 | manage | 34.5 K | 2015-07-24 - 21:03 | AllenMincer | L1 KF lookup table |