# Introduction

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

# 2022 Trigger performance plots from 900 GeV collisions

## ATL-COM-DAQ-2022-055 Inner Detector Trigger MinBias tracking in 900 GeV collsions from 2022

 The distribution of offline track transverse momentum pT , for tracks found in events reconstructed passing either of three minimum bias triggers which select on hits in the minimum bias trigger scintillator (MBTS) system and requiring different track selection from 900 GeV collisions collected at the LHC in 2022: in the single track trigger, only the presence of at least one track reconstructed online with transverse momentum greater than 100 MeV is required; for the 4 GeV and 6 GeV triggers, at least one of these tracks reconstructed in the trigger must have transverse momentum larger than 4 GeV or 6 GeV respectively. As such, for the 4 GeV and 6 GeV triggers, the selection will be biased by the online selection for tracks with pT above the respective threshold. This leads to the corresponding thresholds seen around 4 GeV and 6 GeV. Below the thresholds, the tracks will be unbiased. For the selection of offline tracks, only tracks with pT > 500 MeV have been selected. png pdf The distribution of track transverse momentum, pT , for trigger tracks found in LHC collisions at 900 GeV from 2022 for events passing either of three minimum bias triggers which run the trigger tracking. The triggers select on hits in the minimum bias trigger scintillator (MBTS) system and require different track selection: In the single track trigger, only the presence of at least one track reconstructed in the trigger with transverse momentum greater than 100 MeV is required; for the 4 GeV and 6 GeV triggers, at least one of these trigger tracks must have transverse momentum larger than 4 GeV or 6 GeV respectively leading to the corresponding thresholds seen at 4 GeV and 6 GeV. The ID minimum bias trigger reconstructs tracks for the ATLAS high level trigger using hits from the silicon detectors -- the Pixel detector and the Semiconductor tracker. png pdf The efficiency for the reconstruction of tracks from the ATLAS inner detector (ID) trigger for LHC collisions at 900 GeV from 2022. The efficiency is shown for the tracks reconstructed in the trigger as a function of the corresponding transverse momentum, pT , of the tracks reconstructed offline. The efficiency is shown for three minimum bias triggers which run the trigger tracking. The triggers select on hits in the minimum bias trigger scintillator (MBTS) system and require different track selection: in the single track trigger, only the presence of at least one track reconstructed in the trigger with transverse momentum greater than 100 MeV is required; for the 4 GeV and 6 GeV triggers, at least one of these trigger tracks must have transverse momentum larger than 4 GeV or 6 GeV respectively. As such the trigger efficiency is somewhat biased for tracks above the 4 GeV and 6 GeV thresholds from the respective triggers which are enhanced in the samples, however, each sample is largely dominated by tracks from lower transverse momentum, for which the samples are unbiased. The ID minimum bias trigger reconstructs tracks for the ATLAS high level trigger using hits from the silicon detectors -- the Pixel detector and the Semiconductor tracker. png pdf The efficiency for the reconstruction of tracks from the ATLAS inner detector (ID) for LHC collisions at 900 GeV from 2022. The efficiency is shown for the tracks reconstructed in the trigger as a function of the corresponding track pseudorapidity, $\eta$, of the tracks reconstructed offline. The efficiency is shown for three minimum bias triggers which run the trigger tracking. The triggers select on hits in the minimum bias trigger scintillator (MBTS) system and require different track selection: in the single track trigger, only the presence of at least one track reconstructed in the trigger with transverse momentum greater than 100 MeV is required; for the 4 GeV and 6 GeV triggers, at least one of these trigger tracks must have transverse momentum larger than 4 GeV or 6 GeV respectively. As such the trigger efficiency is somewhat biased for tracks above the 4 GeV and 6 GeV thresholds from the respective triggers which are enhanced in the sample, however, the samples are largely dominated by tracks from lower momentum, for which the samples are unbiased. The ID minimum bias trigger reconstructs tracks for the ATLAS high level trigger using hits from the silicon detectors -- the Pixel detector and the Semiconductor tracker. png pdf The efficiency for the reconstruction of tracks from the ATLAS inner detector (ID) trigger for LHC collisions at 900 GeV from 2022. The efficiency is shown for the tracks reconstructed in the trigger as a function of the corresponding z-position at the beam line, of the tracks reconstructed offline. The efficiency is shown for three minimum bias triggers which run the trigger tracking. The triggers select on hits in the minimum bias trigger scintillator (MBTS) system and require different track selection: in the single track trigger, only the presence of at least one track reconstructed in the trigger with transverse momentum greater than 100 MeV is required; for the 4 GeV and 6 GeV triggers, at least one of these trigger tracks must have transverse momentum larger than 4 GeV or 6 GeV respectively. As such the trigger efficiency is somewhat biased for tracks above the 4 GeV and 6 GeV thresholds from the respective triggers which are enhanced in the samples, however, each sample is largely dominated by tracks from lower transverse momentum, for which the samples are unbiased. The ID minimum bias trigger reconstructs tracks for the ATLAS high level trigger using hits from the silicon detectors -- the Pixel detector and the Semiconductor tracker. png pdf The efficiency for the reconstruction of tracks from the ATLAS inner detector (ID) trigger for LHC collisions at 900 GeV from 2022. The efficiency is shown for the tracks reconstructed in the trigger as a function of the corresponding transverse impact parameter, d0 , measured with respect to the beam line of the tracks reconstructed offline. The efficiency is shown for three minimum bias triggers which run the trigger tracking. The triggers select on hits in the minimum bias trigger scintillator (MBTS) system and require different track selection: In the single track trigger, only the presence of at least one track reconstructed in the trigger with transverse momentum greater than 100 MeV is required; for the 4 GeV and 6 GeV triggers, at least one of these trigger tracks must have transverse momentum larger than 4 GeV or 6 GeV respectively. As such the trigger efficiency is somewhat biased for tracks above the 4 GeV and 6 GeV thresholds from the respective triggers which are enhanced in the samples, however, each sample is largely dominated by tracks from lower transverse momentum, for which the samples are unbiased. The ID minimum bias trigger reconstructs tracks for the ATLAS high level trigger using hits from the silicon detectors -- the Pixel detector and the Semiconductor tracker. png pdf The resolution -- as defined in [1] -- for the pseudorapidity, $\eta$, with respect to tracks reconstructed offline for tracks reconstructed by the ATLAS inner detector (ID) trigger as a function of the corresponding offline reconstructed track transverse momentum, pT for trigger tracks from LHC collisions at 900 GeV from 2022. The resolution is shown for three minimum bias triggers which run the trigger tracking. The triggers select on hits in the minimum bias trigger scintillator (MBTS) system and require different track selection: in the single track trigger, only the presence of at least one track reconstructed in the trigger with transverse momentum greater than 100 MeV is required; for the 4 GeV and 6 GeV triggers, at least one of these trigger tracks must have a transverse momentum larger than 4 GeV or 6 GeV respectively. The ID minimum bias trigger reconstructs tracks for the ATLAS high level trigger using hits from the silicon detectors -- the Pixel detector and the Semiconductor tracker. png pdf The resolution -- as defined in [1] -- for the inverse of the transverse momentum, pT , with respect to tracks reconstructed offline for tracks reconstructed by the ATLAS inner detector (ID) trigger as a function of the corresponding offline reconstructed track transverse momentum, pT , for trigger tracks from LHC collisions at 900 GeV from 2022. The resolution is shown for three minimum bias triggers which run the trigger tracking. The triggers select on hits in the minimum bias trigger scintillator (MBTS) system and require different track selection: in the single track trigger, only the presence of at least one track reconstructed in the trigger with transverse momentum greater than 100 MeV is required; for the 4 GeV and 6 GeV triggers, at least one of these trigger tracks must have a transverse momentum larger than 4 GeV or 6 GeV respectively. The ID minimum bias trigger reconstructs tracks for the ATLAS high level trigger using hits from the silicon detectors -- the Pixel detector and the Semiconductor tracker. png pdf The resolution -- as defined in [1] -- with respect to tracks reconstructed offline, for the product of the track z position at the beam line, \zo with the track polar angle with respect to the beam line, for tracks reconstructed by the ATLAS inner detector (ID) trigger as a function of the corresponding offline reconstructed track transverse momentum, pT , for trigger tracks from LHC collisions at 900 GeV from 2022. The resolution is shown for three minimum bias triggers which run the trigger tracking. The triggers select on hits in the minimum bias trigger scintillator system (MBTS) and require different track selection: in the single track trigger, only the presence of at least one track reconstructed in the trigger with transverse momentum greater than 100 MeV is required; for the 4 GeV and 6 GeV triggers, at least one of these trigger tracks must have a transverse momentum larger than 4 GeV or 6 GeV respectively. The ID minimum bias trigger reconstructs tracks for the ATLAS high level trigger using hits from the silicon detectors -- the Pixel detector and the Semiconductor tracker. png pdf The resolution -- as defined in [1] -- measured with respect to tracks reconstructed offline, for the transverse impact parameter, d0 , with respect to the beam line, for tracks reconstructed by the ATLAS inner detector (ID) trigger as a function of the corresponding offline reconstructed track transverse momentum, pT , for trigger tracks from LHC collisions at 900 GeV from 2022. The resolution is shown for three minimum bias triggers which run the trigger tracking. The triggers select on hits in the minimum bias trigger scintillator (MBTS) system and require different track selection: in the single track trigger, only the presence of at least one track reconstructed in the trigger with transverse momentum greater than 100 MeV is required; for the 4 GeV and 6 GeV triggers, at least one of these trigger tracks must have a transverse momentum larger than 4 GeV or 6 GeV respectively. The ID minimum bias trigger reconstructs tracks for the ATLAS high level trigger using hits from the silicon detectors -- the Pixel detector and the Semiconductor tracker. png pdf

# Developments for Run-3

## ATL-COM-DAQ-2022-022 Optimization of Fullscan Trigger Tracking

 ATLAS High Level Trigger (HLT) per-track reconstruction efficiency and the overall HLT full detector fast-track-finding CPU time as a function of the half-width of the longitudinal window in z along the beam line which is centred on the beam spot, and which is used to restrict the tracking acceptance. Using 13 TeV ttbar Monte Carlo with a mean pile-up interaction multiplicity of <μ> = 60. The CPU time is normalized to the mean time for the z = 225 mm measurement point. The red triangles show the mean CPU time as a function of the z half-width while the black dots show the per-track reconstruction efficiency as compared to offline reconstructed tracks, the uncertainty of which is smaller than the marker size. The measurements are made at fixed points of z window half-width and the points are joined with a line for visibility. Pink dotted lines indicate the positions of 3σz, 4σz and 5σz, where σz is the half-width of the beam spot in the z direction and σz = 43.7 mm. png pdf Event processing time distributions for full detector fast-track-finding in the ATLAS High Level Trigger with PPS (Pixel-Pixel-SCT) seeds enabled and with PPS seeds disabled as obtained from 13 TeV ttbar Monte Carlo with a mean pile-up interaction multiplicity of <μ> = 60. PPS seeds is described in ATL-SOFT-PROC-2015-056. The red dashed histogram is the event processing time with PPS seeds enabled, while the black solid histogram shows the event processing time with PPS seeds disabled. The PPS Seeds Enabled values are scaled by a normalising factor such that the distribution's mean is 1. The PPS Seeds Disabled values are scaled by the same factor, the mean of the PPS Seeds Disabled distribution is 0.53, indicating a mean speed-up factor of 1.9. png pdf ATLAS High Level Trigger (HLT) full detector fast-track-finder per-track efficiencies as a function of the azimuthal angle φ of offline reconstructed tracks for the fast-track-finder running with PPS (Pixel-Pixel-SCT) seeds enabled and with PPS seeds disabled. PPS seeds is described in ATL-SOFT-PROC-2015-056. Results are shown for 13 TeV ttbar Monte Carlo with a mean pile-up interaction multiplicity of <μ> = 60. Solid red dots are the per-track tracking efficiency with respect to offline tracks with PPS seeds enabled in the HLT fast-track-finder, while the hollow dots are the efficiency with PPS seeds disabled. png pdf

## ATL-COM-DAQ-2022-026 Expected Timing for High Level Trigger Large Radius Tracking

 The normalised processing time for Full-Scan (FS) standard tracking (green dashed line) and Full-Scan Large Radius tracking (FS LRT) (blue solid line) in the High Level Trigger (HLT). Improvements to the offline LRT [IDTR-2021-003, ATL-PHYS-PUB-2017-014] in both speed and reduction of fake tracks allow it to be used online in the trigger. The FS LRT runs over the entire Inner Detector, using the clusters remaining after standard tracking, and is designed to track particles with at least eight hits in the silicon detectors, pT > 1 GeV, and |d0|>2 mm. The normalised time is defined as the processing time divided by the mean of the standard tracking histogram. Both histograms are normalised to unit area. The event sample is enhanced bias data from 2018, re-processed offline with the Run 3 trigger software. The mean pileup is 50. The mean normalised processing time for LRT is 1.71. The mean number of track seeds constructed is higher for LRT compared to standard tracking, which results in increased processing time. png pdf The normalised processing time for muon Region of Interest (RoI) standard tracking (green dashed line) and Large Radius tracking (RoI LRT) (blue solid line) in the High Level Trigger (HLT). Improvements to the offline LRT [IDTR-2021-003, ATL-PHYS-PUB-2017-014] in both speed and reduction of fake tracks allow it to be used online in the trigger. The LRT in the HLT uses all clusters in regions of interest (RoI) and is designed to track particles with at least eight hits in the silicon detectors, pT > 1 GeV, and |d0|>2 mm. The normalised time is defined as the processing time divided by the mean of the standard tracking histogram. Both histograms are normalised to unit area. The event sample is enhanced bias data from 2018, re-processed offline with the Run 3 trigger software. The mean pileup is 50. The mean normalised processing time for LRT is 0.36. The mean number of track seeds constructed is lower for LRT compared to standard tracking, which results in reduced processing time. png pdf

## High Level Trigger Run-3 Disappearing Track Trigger Performance

 The integrated signal efficiency is shown for each High Level Trigger (HLT) selection with respect to the Level-1 Trigger $E_{\rm{T}}^{miss}>50$ GeV selection, plotted as a function of the chargino pair system transverse momentum ($p_{\rm{T}}$), which corresponds to the missing transverse energy ($E_{\rm{T}}^{miss}$) from the disappearing track. Monte Carlo simulation of chargino pair-production is used, where the charginos are forced to decay between the Pixel and SCT detectors (13--30 cm). The chargino’s lifetime is set to 1 ns, the $E_{\rm{T}}^{miss}$ is required to be greater than 60 GeV, and 10 different masses are used for chargino mass: 91, 200, 300, 400, 500, 600, 700, 800, 900, and 1000 GeV. The green dotted line shows the efficiency of the HLT $E_{\rm{T}}^{miss}>$110 GeV selection, and the blue line shows the efficiency of combining two HLT selections ($E_{\rm{T}}^{miss}>$110 GeV or ($E_{\rm{T}}^{miss}>$80 GeV and disappearing track trigger)). The disappearing track trigger targets disappearing tracks by using Machine Learning (Boosted Decision Tree) classifiers to predict whether a track is signal or background.The HLT $E_{\rm{T}}^{miss}>$110 GeV selection was used through Run 2. png pdf The disappearing track trigger classifies each track into four categories based on the number of Pixel and SCT detector hits. Tracks which have at least one hit in the SCT detector and exactly four hits in the Pixel detector are classified in one of the four categories. Distributions of the Boosted Decision Tree (BDT) score from this category are shown. The histogram is normalised to an integral of 1.0. The BDT is a Machine Learning classifier used to predict whether tracks belong to the signal category (disappearing track from a chargino) or to a background category. The signal sample is a Monte Carlo simulation of chargino pair production, which were forced to decay between the Pixel and SCT detectors (13--30 cm), whose lifetime is 1 ns, and 10 different masses are used for the chargino mass: 91, 200, 300, 400, 500, 600, 700, 800, 900, and 1000 GeV. The background sample is obtained from 2018 data, where the $pp$ center of mass energy was 13 TeV. The data are filtered by requiring events to pass the High Level Trigger with at least one jet with $p_{\rm{T}}>$30 GeV. In addition, the signal and background samples are required to have tracks reconstructed by disappearing track trigger with $p_{\rm{T}}>$20 GeV. Twelve variables are used as input to the BDT classifier, amongst these are discrete variables such as isolation (1 track nearby or no tracks nearby), and the number of hits in the SCT detector. The bump at around a BDT score of -0.3 originates mainly due to the binary isolation classification. png pdf

## ATL-COM-DAQ-2021-003 : Machine Learning Studies for the fast tracking Trigger algorithm

 Distributions of |cot(θ)| for pixel-barrel doublets of spacepoints in the ATLAS pixel detector, where θ is the inclination angle of the doublet with respect to the z-axis, from t ̄t Monte Carlo 13 TeV with mean pile-up interaction multiplicity of <μ> = 80. Shown are pixel-barrel doublets from triplets constructed at the combinatorial stage in ATLAS track seeding. These triplets are formed from pairs of doublets which share a common spacepoint where that shared middle spacepoint consists of pixel clusters with wη ≤ 0.4 mm, where wη is the cluster width measured in the η direction. Shown are the distributions for doublets with hits correctly associated to corresponding truth particles by the tracking algorithms, for which its doublet spacepoints belong to the same track and also shown are doublets that have hits incorrectly associated, for which its spacepoints do not belong to the same track. The data was used to train a Machine Learning classifier to predict whether a doublet of spacepoints has correct hit association and hence belong to the same track corresponding to truth particles, or incorrect hit association, using the input hit features of wη and the absolute inverse track inclination |cot(θ)|. This study was conducted in order to speed up the track seeding stage and reduce CPU time in the ATLAS fast tracking trigger algorithm. png pdf Shown is the Receiver Operating Characteristic (ROC) curve indicating the rates of false positive and true positive of pixel-barrel doublets from the ATLAS pixel detector to tracks corresponding to truth particles, for spacepoints with wη ≤ 0.4 mm, where wη is the cluster width measured in the η direction, using a Machine Learning (ML) classifier to predict whether a doublet of spacepoints belong to the same track and hence defined as having correct hit association, or have incorrect hit association. The classifier was trained using pixel-barrel doublets from Monte Carlo 13 TeV t ̄t <μ> = 80 samples. Each pair of false positive and true positive rates correspond to a prediction probability, which can be used as a tuning parameter. The classifier’s predictions were adjusted using the prediction probability derived from the ROC curve which would yield a true positive rate of 0.95. The Area Under the Curve (AUC) is a measure of the ability of the classifier to distinguish between correct and incorrect hit association classes and is in the range 0.0 ≤ AUC ≤ 1.0, where AUC = 1 corresponds to perfect classification. The AUC achieved by the ML classifier shown was AUC = 0.79. Also shown is the ‘no skill’ classifier result with AUC = 0.5, which cannot discriminate between correct or incorrect hit association classes and would predict a random class in all cases with 50% probability for each class. A similar procedure was executed for training a model to determine whether spacepoints in pixel-endcap doublets belonged to the same track, using truth from Monte Carlo 13 TeV t ̄t <μ> = 80 samples. This study was conducted in order to speed up the track seeding stage and reduce CPU time in the ATLAS fast tracking trigger algorithm. png pdf Shown is the predicted classification of pixel-barrel doublet spacepoints from the ATLAS pixel detector to tracks corresponding to truth particles, based on |cot(θ)|, where θ is the inclination angle of the doublet with respect to the z-axis, plotted as a function of longitudinal cluster width measured in the η direction, wη, using a set of Machine Learning (ML) classifiers to predict whether a doublet of spacepoints belong to the same track and hence defined as having correct hit association, or have incorrect hit association. The classifiers were trained using pixel-barrel doublets from Monte Carlo 13 TeV t̄t with mean pile-up interaction multiplicity of <μ> = 80 samples. A discrete 1-dimensional nature is observed increasing in increments of 0.2 mm, as a direct result of varying permutations of ‘standard’ and ‘long’ pixels in cluster width calculation, where each 1-dimensional band was independently trained and tuned to yield a true positive rate of 0.95. A similar procedure was executed for training and predicting doublet hit association for pixel-endcaps doublets using truth from Monte Carlo 13 TeV t̄t <μ> = 80 samples. This study was conducted in order to speed up the track seeding stage and reduce CPU time in the ATLAS fast tracking trigger algorithm. png pdf Shown is the seed selection efficiency and total seed rejection rate for seeds consisting of pixel-barrel doublet spacepoints from the ATLAS pixel detector, plotted as a function of longitudinal cluster widths measured in the η direction, wη, where a set of machine learning classifiers were trained to distinguish pixel-barrel doublets matched to tracks corresponding to truth particles from Monte Carlo 13 TeV t̄t <μ> = 80 samples, and to predict whether a doublet of spacepoints belong to the same track, hence defined as having correct hit association, or predict if a doublet has incorrect hit association. The seed selection efficiency is defined as the proportion of seeds with both its constituent doublet pairs classified as correctly associated, out of all correctly associated seeds corresponding to Monte Carlo truth from ATLAS tracking algorithms, and the total seed rejection efficiency considers the proportion of rejected seeds, thereby providing an estimate of the total CPU time saved. The lower efficiency and corresponding reduced purity around wη ~ 2.0 mm is due to the transition between the barrel and endcap pixel detector. Errors shown here are purely statistical. This study was conducted in order to speed up the track seeding stage and reduce CPU time in the ATLAS fast tracking trigger algorithm. A similar procedure was executed for pixel-endcap triplets using truth information from Monte Carlo t̄t <μ> = 80 samples. png pdf Shown is a visual representation of a 2-dimensional look up table showing the expected correlation between longitudinal cluster width measured in the η direction, wη, and |cot(θ)| where θ is the inclination angle of the doublet with respect to the z-axis, for pixel-barrel doublet spacepoints from the ATLAS pixel detector. The expected correlation was predicted by a set of machine learning classifiers, trained using pixel-barrel doublets from Monte Carlo 13 TeV t̄t <μ> = 80 samples and used to predict doublets that are correctly associated to corresponding truth particles by the tracking algorithms for which its doublet of spacepoints belong to the same track or predict if the doublet has incorrect hit association. Morphological smoothing was applied to the classifier predictions, forming a smoothed doublet acceptance region, whereby any pixel-barrel doublets within this region would be accepted in the fast tracking trigger stage of the ATLAS detector. The look up table shown is plotted with binned |cot(θ)| axes with 45 equally separated bins between 0.0-9.0, plotted as a function of binned wη (mm) with 30 equally separated bins between 0.0-3.0 (mm). This study was conducted in order to speed up the track seeding stage and reduce CPU time in the ATLAS fast tracking trigger algorithm. A similar procedure was executed to train and predict doublet hit association for pixel-endcap doublets, using truth from Monte Carlo 13 TeV simulation of t̄t <μ> = 80 samples, and convert classifier predictions to a 2-dimensional look up table. png pdf Shown are the tracking efficiencies as a function of the Monte Carlo truth track η, for pT > 3 GeV for the ATLAS full detector tracking with t̄t Monte Carlo 13 TeV and mean pile-up interaction multiplicity of <μ> = 80. The data points show the efficiency when using machine learning extensions in the seed building stages of the fast tracking trigger in the ATLAS pixel detector, prior to the track fitting. The dashed line shows the efficiency of the standard trigger seeding with no application of machine learning extensions. There is little deviation from the standard trigger seeding with application of the machine learning extensions, where the average tracking efficiency achieved was 93.9% and the greatest efficiency loss from the standard trigger seeding is at large |η|. The errors shown are purely statistical. png pdf Shown are the tracking efficiencies as a function of the Monte Carlo truth track pT for pT > 3 GeV, for the ATLAS full detector tracking with t̄t Monte Carlo 13 TeV and mean pile-up interaction multiplicity of <μ> = 80. The data points show the efficiency when using a machine learning extension in the seed building stages of the fast tracking trigger in the ATLAS pixel detector, prior to the track fitting. The dashed line shows the efficiency of the standard trigger seeding with no application of machine learning extensions. There is little deviation from the standard trigger seeding with application of the machine learning extensions, where the errors shown are statistical. png pdf Performance of the ATLAS full detector tracking with Monte Carlo 13 TeV t̄t samples at average pile-up multiplicity of <μ> = 80, with the application of machine learning extensions for filtering on pixel detector doublet spacepoints in the fast tracking trigger stage prior to the track fitting. Shown are the total speed-up factor and breakdown of speed-ups for the different stages within the fast tracking trigger algorithm, where each speed-up is presented with respect to the standard trigger seeding where no machine learning extensions were applied. The total speed-up factor achieved for the fast tracking trigger algorithm in the full detector with application of machine learning extensions was 2.3x, where the execution time was observed to be an order of several seconds faster. The greatest saving in CPU time is achieved during the Seed Processing stage of the fast tracking trigger algorithm, as a direct result of a significant reduction in the number of seeds. png pdf Performance of the ATLAS full detector tracking with Monte Carlo 13 TeV t̄t samples at average pile-up multiplicities of <μ> = 40, 60 and 80, with the application of machine learning extensions for filtering on pixel detector doublet spacepoints in the fast tracking trigger stage prior to the track fitting. The absolute loss in average tracking efficiency and the total speed-up factor for seeded track finding in the ATLAS pixel detector is presented with respect to the standard trigger seeding where no machine learning extensions were applied. The efficiency loss is mainly observed at large |η|, where the statistical uncertainties in efficiencies are O(10-3), hence are not quoted in the table. png pdf

# 2017 Z-finder Performance Plots

## ATL-COM-DAQ-2022-010 : Inner Detector Z-Finder Performance

 The ATLAS trigger ZFinder is an algorithm for finding an approximation of the z-position of the collision vertices without reconstructing charged particle tracks. The ZFinder uses extrapolations of approximate helix segments through multiplets of spacepoints from the tracking detectors. In addition to the estimation of the z position of the vertex it also provides the count (weight) of extrapolations compatible with a given vertex z position. This histogram shows an example of histogram from extrapolated vertex z positions identified by the ZFinder algorithm for a single event of pp collision. The highest peak(s) tells where the vertices z positions are. The compared plots are two different configurations of the ZFinder. The default configuration (black line) uses any doublet of spacepoints combination between any two layers and with z bin width Δz = 0.2 mm and angle ϕ window Δϕ = 0.2. The triplet mode (red-dashed line) is optimised for low pile-up condition and uses triplets of spacepoints from the pixel-barrel with larger values of Δz = 3.5 mm and Δϕ = 0.5. Narrow Δz allows better precision for the vertex z estimation but results in splitting the weights into several bins. Larger Δϕ increases the acceptance for low-pT charged particles. The second configuration gives a better signal (value at peak) to noise (value around the peak) ratio. The algorithm performance is obtained by re-running the trigger on an existing √s = 13 TeV pp dataset from 2017 with a mean number of interactions per bunch crossing μ ∽ 2. png pdf The ATLAS trigger ZFinder is an algorithm for finding an approximation of the z-position of the collision vertices without reconstructing charged particle tracks. The ZFinder uses extrapolations of approximate helix segments through multiplets of spacepoints from the tracking detectors. In addition to the estimation of the z position of the vertex it also provides the count (weight) of extrapolations compatible with a given vertex z position. This histogram shows the correlation between the number of pixel-barrel triplets in the largest peak found by the ZFinder algorithm with the number of charged particle tracks in an event of pT > 0.2 GeV and |η| < 1. The largest peak found by the ZFinder algorithm is proportional to the number of offline reconstructed tracks as indicated in this figure. The parameters of the ZFinder algorithm, z bin width Δz = 3.5 mm and angle ϕ window Δϕ = 0.5, are optimised for low pile-up conditions. The algorithm performance is obtained by re-running the trigger on an existing √s = 13 TeV pp dataset from 2017 with a mean number of interactions per bunch crossing μ ∽ 2. png pdf The ATLAS trigger ZFinder is an algorithm for finding an approximation of the z-position of the collision vertices without reconstructing charged particle tracks. The ZFinder uses extrapolations of approximate helix segments through multiplets of spacepoints from the tracking detectors. In addition to the estimation of the z position of the vertex it also provides the count (weight) of extrapolations compatible with a given vertex z position. This histogram shows the efficiency as a function of the number of offline tracks with pT > 0.2 GeV and within |η| < 1 for several selections based on number of pixel-barrel triplets (Ntriplet) counted in ZFinder algorithm. Prior to the selection an additional cut is applied on the number of space points (measurements) in Silicon Strips Tracker detector. That cut does not affect the turn-on shape that is solely determined by the requirement on the number of pixel triplets. The algorithm performance is obtained by re-running the trigger on an existing √s = 13 TeV pp dataset from 2017 with a mean number of interactions per bunch crossing μ ∽ 2. png pdf The ATLAS trigger ZFinder is an algorithm for finding an approximation of the z-position of the collision vertices without reconstructing charged particle tracks. The ZFinder uses extrapolations of approximate helix segments through multiplets of spacepoints from the tracking detectors. In addition to the estimation of the z position of the vertex it also provides the count (weight) of extrapolations compatible with a given vertex z position. This histogram shows the efficiency as a function of the number of offline reconstructed tracks with pT > 0.2 GeV and within |η| < 1 to select events that have more than 80 pixel triplets reconstructed by the ZFinder algorithm. The efficiency curves are obtained for events with varying requirement as to the number of offline reconstructed vertices thus indicating pile-up sensitivity of the ZFinder. The algorithm performance is obtained by re-running the trigger on an existing √s = 13 TeV pp dataset from 2017 with a mean number of interactions per bunch crossing μ ∽ 2. png pdf

# Run 2 Trigger Performance Plots - combined luminosity 2016-2018

## ATL-COM-DAQ-2020-059 Inner Detector Trigger Performance for Run 2

 The timing performance of the tracking algorithms running online for the muon signature for a run taken in September 2018 with a proton-proton centre of mass energy of 13 TeV and where the mean pileup interaction multiplicity was 52. For the muon triggers the inner detector reconstruction first runs a fast track finder algorithm followed by a precision tracking step which runs aspects of the offline track reconstruction using the hits from the fast track finder tracks. To reduce processing time the trigger reconstructs tracks only in a region of interest (RoI) centred on the muon spectrometer candidate from earlier in the ATLAS trigger. In some chains a second tracking stage is executed to establishing muon isolation by running the reconstruction in a wider region of interest in the detector. Shown are the precision tracking times, and the fast track finder times for this first stage and for the fast track finder running in the larger second stage isolation regions of interest. png pdf The track finding efficiency for the inner detector (ID) trigger for tracks from offline medium quality muon candidates as a function of the offline muon transverse momentum (pT). The efficiency is evaluated using a tag and probe analysis using a dimuon trigger where one of the muons — the probe muon — was selected without any requirement on the ID Trigger tracks. Offline muon candidates are required to have at least one pixel cluster, at least 4 SCT clusters, and no more than two missing hits from the silicon detectors where such hits would be expected. Also if expected, they should have at least one hit in the innermost pixel layer. The selection for offline muon candidates from below the trigger threshold is biased in favour of candidates that appear to be higher in pT in the Muon Spectrometer. For the muon triggers the ID reconstruction first runs a fast track finder algorithm followed by a precision tracking step which runs aspects of the offline track reconstruction. Statistical, Bayesian uncertainties only are shown. png pdf The track finding efficiency of the inner detector (ID) trigger for tracks from offline medium quality muon candidates as a function of the offline muon track transverse impact parameter, d0, with respect to the beamline. The efficiency is evaluated using a tag and probe analysis using a dimuon trigger where one of the muons -- the probe muon -- was selected without any requirement on the ID Trigger tracking. Offline muon candidates are required to have at least one pixel cluster, at least 4 SCT clusters, and no more than two missing hits from the silicon detectors where such hits would be expected. Also if expected, they should have at least one hit in the innermost pixel layer. Offline muons with transverse momentum (pT) greater than 13 GeV are used and as such the selection for muon candidates below the trigger threshold is biased towards candidates that appear to be higher in pT in the Muon Spectrometer. For the muon triggers the ID reconstruction first runs a fast track finder algorithm followed by a precision tracking step which runs aspects of the offline track reconstruction. Statistical, Bayesian uncertainties are shown. png pdf The resolution on the transverse impact parameter, d0, with respect to the beamline, of the trigger tracking with respect to offline for the inner detector (ID) trigger for muons with pT > 13 GeV from medium quality offline muon candidates, shown as a function of the offline muon pseudorapidity. The efficiency is evaluated using a tag and probe analysis using a dimuon trigger where one of the muons -- the probe muon -- was selected without any requirement on the ID Trigger tracking. Offline muons are required to have at least one pixel cluster and at least 4 SCT clusters, with no more than two holes in the silicon detectors. For the muon triggers the ID reconstruction first runs a fast track finder algorithm followed by a precision tracking step which runs aspects of the offline track reconstruction. Statistical uncertainties only are shown. png pdf The track finding efficiency of the inner detector (ID) trigger for tracks from offline tight quality electron candidates as a function of the offline electron transverse energy (ET). The efficiency is evaluated using a tag and probe analysis using a dielectron trigger where one of the electron candidates -- the probe electron -- was selected only using the calorimeter cluster, and without any requirement on the ID Trigger tracking. Offline electron candidates are required to have at least two pixel hits with at least one in the innermost pixel layer if such a hit is expected, and at least 4 SCT clusters. For the electron triggers the ID reconstruction first runs a fast track finder algorithm followed by a precision tracking step which runs aspects of the offline track reconstruction. Statistical, Bayesian uncertainties are shown. png pdf The track finding efficiency of the inner detector (ID) trigger for tracks from offline tight quality electron candidates as a function of the offline electron pseudorapidity. The efficiency is evaluated using a tag and probe analysis using a dielectron trigger where one of the electron candidates -- the probe electron -- was selected only using the calorimeter cluster, and without any requirement on the ID Trigger tracking. Offline electron candidates are required to have at least two pixel hits with at least one in the innermost pixel layer if such a hit is expected, and at least 4 SCT clusters. For the electron triggers the ID reconstruction first runs a fast track finder algorithm followed by a precision tracking step which runs aspects of the offline track reconstruction. Statistical, Bayesian uncertainties are shown. png pdf The track finding efficiency of the inner detector (ID) trigger for tracks from offline tight quality electron candidates as a function of the offline electron transverse energy measured in the calorimeter over the transverse momentum from the track: (ET/pT). The efficiency is evaluated using a tag and probe analysis using a dielectron trigger where one of the electron candidates -- the probe electron -- was selected only using the calorimeter cluster, and without any requirement on the ID Trigger tracking. Offline electron candidates are required to have at least two pixel hits with at least one in the innermost pixel layer if such a hit is expected, and at least 4 SCT clusters. Bremsstrahlung electron candidates which have radiated significant energy in the form of a hard photon are reconstructed with ET/pT greater than one. Values below one indicate poorly reconstructed candidates where the offline track pT has been overestimated. For the electron triggers the ID reconstruction first runs a fast track finder algorithm followed by a precision tracking step which runs aspects of the offline track reconstruction. Statistical, Bayesian uncertainties are shown. png pdf The track finding efficiency of the inner detector (ID) trigger for offline track candidates from jet events shown as as a function of the offline track transverse momentum (pT). The efficiency is evaluated with a 55 GeV b-jet trigger configured to select only on the jets and without any requirement on the ID tracks. Offline tracks candidates are required to have at least 9 silicon clusters if |η| ≤ 1.65 or 11 if |η| > 1.65, at least one hit in the pixel detector, with one in the innermost pixel layer if such a hit is expected, and no missing pixel hits. For the b-jet triggers the ID reconstruction runs in two stages - The first stage runs a fast vertex tracking stage for tracks in η and φ around the jet axis for each jet but extended along the interaction region at the beamline, and with a higher transverse momentum selection in the pattern recognition. The second stage runs the fast track finder algorithm in a wider region about the jets but with a tight selection about the z position of the vertex identified in the first stage, followed by the precision tracking which runs aspects of the offline track reconstruction. Statistical, Bayesian uncertainties are shown. png pdf The resolution on the transverse impact parameter, d0, with respect to the beamline, of the trigger tracking with respect to offline for the inner detector (ID) trigger for jet events shown as as a function of the offline track pseudorapidity. The resolution is evaluated using a 55 GeV b-jet trigger and a 150 GeV b-jet trigger, both configured to select only on the jets and without any requirement on the ID tracks. Offline tracks candidates are required to have at least 9 silicon clusters if |η| ≤ 1.65 or 11 if |η| > 1.65, at least one hit in the pixel detector, with one in the innermost pixel layer if such a hit is expected, and no missing pixel hits. For the b-jet triggers the ID reconstruction runs in two stages. The second stage, shown here, runs a fast track finder algorithm in a wide region about the jets but with a tight selection about the z position of the vertex identified in the first stage, followed by the precision tracking which runs aspects of the offline track reconstruction. Statistical uncertainties only are shown. png pdf The resolution on the z position, z0, closest to the beamline, of the trigger tracking with respect to offline for the inner detector (ID) trigger for jet events shown as as a function of the offline track pseudorapidity. The resolution is evaluated using a 55 GeV b-jet trigger and a 150 GeV b-jet trigger, both configured to select only on the jets and without any requirement on the ID tracks. Offline tracks candidates are required to have at least 9 silicon clusters if |η| ≤ 1.65 or 11 if |η| > 1.65, at least one hit in the pixel detector, with one in the innermost pixel layer if such a hit is expected, and no missing pixel hits. For the b-jet triggers the ID reconstruction runs in two stages. The second stage, shown here, runs a fast track finder algorithm in a wide region about the jets but with a tight selection about the z position of the vertex identified in the first stage, followed by the precision tracking which runs aspects of the offline track reconstruction. Statistical uncertainties only are shown. png pdf The resolution on the z position, z0, closest to the beamline, of the trigger tracking with respect to offline for the inner detector (ID) trigger for jet events shown as as a function of the offline track transverse momentum, pT. The resolution is evaluated using a 55 GeV b-jet trigger and a 150 GeV b-jet trigger, both configured to select only on the jets and without any requirement on the ID tracks. Offline tracks candidates are required to have at least 9 silicon clusters if |η| ≤ 1.65 or 11 if |η| > 1.65, at least one hit in the pixel detector, with one in the innermost pixel layer if such a hit is expected, and no missing pixel hits. For the b-jet triggers the ID reconstruction runs in two stages. The second stage, shown here, runs a fast track finder algorithm in a wide region about the jets but with a tight selection about the z position of the vertex identified in the first stage, followed by the precision tracking which runs aspects of the offline track reconstruction. Statistical uncertainties only are shown. png pdf The vertex finding efficiency of the inner detector (ID) trigger for tracks from jet events as a function of the mean number of proton-proton interactions per bunch crossing. The efficiency is evaluated with both 110 GeV and 420 GeV b-jet triggers configured to select only on the jets, with no selection on the trigger tracking. Offline track candidates on the vertex are required to have at least 9 silicon clusters if |η| ≤ 1.65 or 11 if |η| > 1.65, at least one hit in the pixel detector, with one in the innermost pixel layer if such a hit is expected, and no missing pixel hits. For the b-jet triggers the ID vertex reconstruction runs two algorithms — a simple histogramming algorithm an offline-based vertex algorithm — using tracks reconstructed in the vertex tracking stage of the multistage b-jet tracking. Bayesian uncertainties are shown. png pdf The vertex finding efficiency of the inner detector (ID) trigger for tracks from jet events as a function of the offline track multiplicity in the reconstruction region used in the trigger. The efficiency is evaluated with both 110 GeV and 420 GeV b-jet triggers configured to select only on the jets, with no selection on the trigger tracking. Offline track candidates on the vertex are required to have at least 9 silicon clusters if |η| ≤ 1.65 or 11 if |η| > 1.65, at least one hit in the pixel detector, with one in the innermost pixel layer if such a hit is expected, and no missing pixel hits. For the b-jet triggers the ID vertex reconstruction runs two algorithms — a simple histogramming algorithm and an offline-based vertex algorithm — using tracks reconstructed in the vertex tracking stage of the multistage b-jet tracking. Bayesian uncertainties are shown. png pdf The resolution of the trigger vertex z position, of the inner detector (ID) trigger with respect to the offline vertex for jet events, shown as a function of the multiplicity of offline tracks found in the reconstruction region used in the trigger. The efficiency is evaluated with both 110 GeV and 420 GeV b-jet triggers configured to select only on the jets, with no selection on the trigger tracking. Offline track candidates on the vertex are required to have at least 9 silicon clusters if |η| ≤ 1.65 or 11 if |η| > 1.65, with at least one hit in the pixel detector, with one in the innermost pixel layer if such a hit is expected, and no missing pixel hits. For the b-jet triggers the ID vertex reconstruction runs two algorithms — a simple histogramming algorithm and an offline-based vertex algorithm — using tracks reconstructed in the vertex tracking stage of the multistage b-jet tracking. Statistical uncertainties only are shown. png pdf

# 2017 Trigger Performance Plots

## ATL-COM-DAQ-2016-121 Inner Detector trigger timing study

 The trigger track reconstruction time for the beamspot trigger for 14 TeV t ̄t Monte Carlo simulated with 46, 69 and 138 interactions per bunch crossing, measured on a 2.4 GHz Intel Xeon CPU. The software version used corresponds to the 2016 online trigger system. Statistical uncertainties are shown. A second-order polynomial is fit to the points. png pdf

 Timing of the Inner Detector (ID) track seeding algorithm for the full detector. The red dots represent the standard HLT ID algorithm, running on a single CPU core, the blue dots show the same logic algorithm ported to GPU. The track seeding algorithm is the first part of the track finding algorithm which produces triplets of spacepoints known as track seeds. The timing is shown as a function of the number of spacepoints in an event. png eps pdf Timings of the Inner Detector (ID) full-detector data preparation implemented in CUDA® performed on a NVIDIA® Tesla® K40m Graphical Processor Unit (GPU) compared with the standard C++ implementation of the ID trigger full detector data preparation running on a single core of an Intel® Xeon® E5-2670 2.6 GHz CPU. The measurements were made with a Monte Carlo simulated data sample of 𝑡𝑡 ̅ events at a centre of mass energy of 14 TeV with a mean of 46 interactions per bunch crossing. The mean execution time per event, for this dataset, was a factor of 21 faster for the GPU implementation. The data preparation consists of bytestream decoding, hit clustering, and spacepoint formation in both the Pixel and SCT detectors. The input data volume is the combined size of Pixel and SCT bytestream data. png pdf

# 2015 Trigger Performance Plots

## ATL-COM-DAQ-2016-083 Run 2 HLT tracking performance from 25ns running

 The electron track finding efficiency of the Inner Detector (ID) trigger, shown for offline tracks with pT > 20 GeV from tight offline electron candidates, shown as a function of the offline track pseudorapidity. The efficiency is evaluated for the 24 GeV electron trigger. Offline tracks are required to have at least two pixel clusters, and at least six SCT clusters. The reconstruction in the ID trigger runs in two stages - the first runs a Fast Track Finder algorithm for fast pattern recognition, and the second stage runs a more detailed track fit using the clusters identified in the Fast Track Finder and using a track fitting algorithm from the offline processing. This strategy is adopted to accommodate the high rate and pileup expected during LHC run 2. Statistical, Bayesian uncertainties are shown. png pdf The electron track finding efficiency of the Inner Detector (ID) trigger, shown for offline tracks with |η| < 2.4 from tight offline electron candidates, shown as a function of the offline track transverse momentum. The efficiency is evaluated for the 24 GeV electron trigger. Offline tracks are required to have at least two pixel clusters, and at least six SCT clusters. The reconstruction in the ID trigger runs in two stages - the first runs a Fast Track Finder algorithm for fast pattern recognition, and the second stage runs a more detailed track fit using the clusters identified in the Fast Track Finder and using a track fitting algorithm from the offline processing. This strategy is adopted to accommodate the high rate and pileup expected during LHC run 2. Statistical, Bayesian uncertainties are shown. png pdf The muon track finding efficiency of the Inner Detector (ID) trigger, shown for offline muon candidates with |η| < 2.4, shown as a function of the offline track transverse momentum. The efficiency is evaluated for the 6 GeV muon trigger. Offline muon candidates are required to have at least two pixel clusters, and at least six SCT clusters. The reconstruction in the ID trigger runs in two stages - the first runs a Fast Track Finder algorithm for fast pattern recognition, and the second stage runs a more detailed track fit using the clusters identified in the Fast Track Finder and using a track fitting algorithm from the offline processing. This strategy is adopted to accommodate the high rate and pileup expected during LHC run 2. Statistical, Bayesian uncertainties are shown. png pdf The resolution for the transverse impact parameter of tracks reconstructed with the Inner Detector (ID) trigger, shown for offline muon candidates with |η| < 2.4, shown as a function of the offline muon transverse momentum from muon candidates collected with the 6 GeV muon trigger. Offline muon candidates are required to have at least two pixel clusters, and at least six SCT clusters. The reconstruction in the ID trigger runs in two stages - the first runs a Fast Track Finder algorithm for fast pattern recognition, and the second stage runs a more detailed track fit using the clusters identified in the Fast Track Finder and using a track fitting algorithm from the offline processing. This strategy is adopted to accommodate the high rate and pileup expected during LHC run 2. Statistical, Bayesian uncertainties are shown. png pdf The vertex finding efficiency of the Inner Detector (ID) trigger from the b-jet trigger. Vertex candidates are found using tracks reconstructed in the trigger from within all Regions of Interest (RoI) identified by jets with ET > 30 GeV found in the HLT using the anti-kT algorithm. The efficiency is shown as a function of the multiplicity of offline tracks with pT >1 GeV and |η| < 2.4 that lie within all the jet RoI in the event. Offline tracks are required to have at least two pixel clusters, and at least six SCT clusters. For speed the tracking for the vertexing runs only a single stage fast track finding stage since the vertex position is primarily used in the trigger only to update the RoI position for the subsequent precision track reconstruction and b-tagging, and so the full precision tracking is not required. Statistical, Bayesian uncertainties are shown. png pdf The resolution of z-vertex position of the Inner Detector (ID) trigger from the b-jet trigger. Vertex candidates are found using tracks reconstructed in the trigger from within all Regions of Interest (RoI) identified by jets with ET > 30 GeV found in the HLT chains with using the anti-kT algorithm. The resolution is shown as a function of the multiplicity of offline tracks with pT >1 GeV and |η| < 2.4 that lie within all the jet RoI in the event. Offline tracks are required to have at least two pixel clusters, and at least six SCT clusters.. For speed the tracking for the vertexing runs only a single stage fast track finding stage since the vertex position is primarily used in the trigger only to update the RoI position for the subsequent precision track reconstruction and b-tagging, and so the full precision tracking is not required. Statistical, Bayesian uncertainties are shown. png pdf

## ATL-COM-DAQ-2015-148 Run 2 HLT tracking timing plots

 The Run 2 HLT Inner Detector tracking trigger processing time for the Fast Track Finder stage for the tau signature. Shown are the times for the single-stage, and the two-stage tracking. In the single-stage tracking, the tracking is performed in a single, large Region of Interest (RoI) with Δη = 0.4, Δφ=0.4 and Δz = 225 mm with respect to the RoI direction and position z=0 along the beamline. In the two-stage tracking, the tracking is first performed in an RoI with Δη = 0.1, Δφ=0.1 and Δz = 225 mm with respect to the RoI direction, to identify the core tracks, and then a second tracking stage is performed in an updated RoI centred on the highest pT track with Δη = 0.4, Δφ=0.4 and Δz = 10 mm with respect to that track. The total mean time for the two-stage tracking is 44.5 ms corresponding to a fractional saving in processing time for the fast tracking with respect to the single-stage tracking of greater than 30%. The data were taken during collisions in August 2015 with the LHC colliding with a 25 ns bunch spacing. The mean number of interactions per bunch crossing was <μ> ~ 14. png pdf The Run 2 HLT Inner Detector tracking trigger processing time for the Precision Tracking stage for the tau trigger. Shown are the times for the single-stage, and the two-stage tracking. In the single-stage tracking, the tracking is performed in a single, large Region of Interest (RoI) with Δη = 0.4, Δφ=0.4 and Δz = 225 mm with respect to the RoI direction and z=0 along the beamline. In the two-stage tracking, the precision tracking is performed only in the second stage RoI with Δη = 0.4, Δφ=0.4 and Δz = 10 mm, centred on the highest pT track reconstructed in the first stage. The data were taken during collisions in August 2015 with the LHC colliding with a 25 ns bunch spacing. The mean number of interactions per bunch crossing was <μ> ~ 14. png pdf A figure illustrating the RoIs from the one-stage tracking (pink) and two-stage tracking (blue - first stage, green - second stage) png pdf

# 2015 Trigger Performance Plots

## ATL-COM-DAQ-2015-110 Run 2 HLT tracking performance plots for 50 ns running

 The Run 2 HLT Inner Detector tracking efficiency in the Minimum Bias Trigger shown as a function of offline track PT for good quality offline tracks with at least 2 pixel clusters and 6 SCT clusters. The offline tracks are required to be in the region |ηoffline| < 2.5. The closest matching trigger track within a cone of ΔR < 0.05 of the offline track is chosen. The data were taken during the early collisions in June 2015 with a mean number of interactions per bunch crossing of <μ> ~ 10. For the Minimum Bias Trigger the tracking runs as a single Precision Tracking stage. Bayesian uncertainties are shown. png pdf The Run 2 HLT Inner Detector tracking efficiency in the Minimum Bias Trigger shown as a function of offline track pseudorapidity for good quality offline tracks with at least 2 pixel clusters and 6 SCT clusters. The offline tracks are required to be in the region |ηoffline| < 2.5 and pT > 1 GeV. The closest matching trigger track within a cone of ΔR < 0.05 of the offline tracks is chosen. The data were taken during the early collisions in June 2015 with a mean number of interactions per bunch crossing of <μ> ~ 10. For the Minimum Bias Trigger the tracking runs as a single Precision Tracking stage. Bayesian uncertainties are Data 13 TeV, minimum Bias Trigger minBias Precision Tracking minBias Precision Tracking shown. png pdf The Run 2 HLT Inner Detector tracking efficiency in the Minimum Bias Trigger shown as a function of offline track transverse impact parameter, d0, with respect to the beamline for good quality offline tracks with at least 2 pixel clusters and 6 SCT clusters. The offline tracks are required to be in the region |ηoffline| < 2.5 and pT > 1 GeV. The closest matching trigger track within a cone of ΔR < 0.05 of the offline tracks is chosen. The data were taken during the early collisions in June 2015 with a mean number of interactions per bunch crossing of <μ> ~ 10. For the Minimum Bias Trigger the tracking runs as a single Precision Tracking stage. Bayesian uncertainties are shown. png pdf The residual in pseudorapidity between the HLT Inner Detector trigger track and the offline track for good offline tracks from the Minimum Bias trigger where the offline tracks are required to have at least 2 pixel clusters and 6 SCT clusters. The offline tracks are required to be in the region |ηoffline| < 2.5 and pT > 1 GeV. The closest matching trigger track within a cone of ΔR < 0.05 of the offline track is chosen. The data were taken during the early collisions in June 2015 with a mean number of interactions per bunch crossing of <μ > ~ 10. For the Minimum Bias Trigger the tracking runs as a single Precision Tracking stage. png pdf The Run 2 HLT Inner Detector tracking efficiency in the 24 GeV Electron Trigger shown as a function of the PT of the Electron track for tight offline electron candidates and where the offline track is required to have at least 2 pixel clusters and 6 SCT clusters. The offline electron tracks are required to lie in the region |ηoffline| < 2.5 and pT > 20 GeV. The closest matching trigger track within a cone of ΔR < 0.05 of the offline track is chosen. The data were taken during early July 2015. The HLT tracking for the Electron trigger runs a Fast Track Finder stage followed by a Precision Tracking stage. Bayesian uncertainties are shown. png pdf The Run 2 HLT Inner Detector tracking efficiency in the 24 GeV Electron Trigger shown as a function of the pseudorapidity of the Electron track for tight offline electron candidates and where the offline track is required to have at least 2 pixel clusters and 6 SCT clusters. The offline electron tracks are required to be in the region |ηoffline| < 2.5 and pT>20 GeV. The closest matching trigger track within a cone of ΔR < 0.05 of the offline track is chosen. The data were taken during early July 2015. The HLT tracking for the Electron trigger runs a Fast Track Finder stage followed by a Precision Tracking stage. Bayesian uncertainties are shown. png pdf The residual in 1/pT between the HLT Inner Detector trigger track and the Electron track for tight offline electron candidates passing the 24 GeV medium Electron trigger where the offline track is required to have at least 2 pixel clusters and 6 SCT clusters. The offline electron tracks are required to be in the region |ηoffline| < 2.5 and pT>20 GeV. The closest matching trigger track within a cone of ΔR < 0.05 of the offline track is chosen. The data were taken during early July 2015. The HLT tracking for the Electron trigger runs a Fast Track Finder stage followed by a Precision Tracking stage. png pdf The Run 2 HLT Inner Detector tracking efficiency in the 10 GeV Muon Trigger shown as a function of the PT of the muon track for offline muon candidates where the offline muon track is required to have at least 2 pixel clusters and 6 SCT clusters. The offline muons tracks are required to be in the region |ηoffline| < 2.5 and pT > 10 GeV. The closest matching trigger track within a cone of ΔR < 0.05 of the offline track is chosen. The data were taken during early July 2015. The HLT tracking for the Muon trigger runs a Fast Track Finder stage followed by a Precision Tracking stage. Bayesian uncertainties are shown. png pdf The Run 2 HLT Inner Detector tracking efficiency in the 10 GeV Muon Trigger shown as a function of the pseudorapidity of the muon track for offline muon candidates where the offline muon track is required to have at least 2 pixel clusters and 6 SCT clusters. The offline muon tracks are required to be in the region |ηoffline| < 2.5 and pT > 10 GeV. The closest matching trigger track within a cone of ΔR < 0.05 of the offline track is chosen. The data were taken during early July 2015. The HLT tracking for the Muon trigger runs a Fast Track Finder stage followed by a Precision Tracking stage. Bayesian uncertainties are shown. png pdf The Run 2 HLT Inner Detector tracking resolution on the transverse impact parameter with respect to the beamline in the 10 GeV Muon Trigger shown as a function of the pseudorapidity of the muon track for offline muon candidates where the offline muon track is required to have at least 2 pixel clusters and 6 SCT clusters. The offline muons tracks are required to be in the region |ηoffline| < 2.5 and pT > 10 GeV. The closest matching trigger track within a cone of ΔR < 0.05 of the offline track is chosen. The data were taken during early July 2015. The HLT tracking for the Muon trigger runs a Fast Track Finder stage followed by a Precision Tracking stage. png pdf The Run 2 HLT Inner Detector tracking resolution on the track pseudorapidity in the 10 GeV Muon Trigger shown as a function of the pseudorapidity of the muon track for offline muon candidates where the offline muon track is required to have at least 2 pixel clusters and 6 SCT clusters. The offline muons tracks are required to be in the region |ηoffline| < 2.5 and pT > 10 GeV. The closest matching trigger track within a cone of ΔR < 0.05 of the offline track is chosen. The HLT tracking for the Muon trigger runs a Fast Track Finder stage followed by a Precision Tracking stage. The data were taken during early July 2015. png pdf The residual in 1/pT between the HLT Inner Detector trigger track and the Muon track for Muon candidates passing the 10 GeV Muon trigger where the offline Muon track is required to have at least 2 pixel clusters and 6 SCT clusters. The offline muon tracks are required to be in the region |ηoffline| < 2.5 and pT > 10 GeV. The closest matching trigger track within a cone of ΔR < 0.05 of the offline track is chosen. The data were taken during early July 2015. png pdf

# 2015 Trigger development Pubnote

## ATL-DAQ-PUB-2015-026 Inner Detector trigger timing studies for Run 2

 The full trigger tau reconstruction time for 14 TeV ttbar Monte Carlo with a mean of 46 interactions per bunch crossing, measured on a 2.4 GHz Intel Xeon CPU. The timing is dominated by track finding - only 36 ms is spent in calorimetry. Shown are the times for two strategies; in the first, “One-step”, strategy, tracks are reconstructed within a tau candidate Region of Interest (RoI) of size ∆η × ∆φ = 0.4 × 0.4 pointing to a calorimeter cluster, and with a spread along the beamline of |z0| < 225 mm, corresponding to the size of the interaction region. There is a mean of approximately 3 tau candidates per event. In the second (“Two-step”) trigger strategy, tracks are first reconstructed within a small RoI of size ∆η × ∆φ = 0.1 × 0.1 with the same z0 extent. Further tracks are then reconstructed in a wider (in η and φ) RoI with ∆η × ∆φ = 0.4 × 0.4, centred on the leading (highest-pT ) track from the previous step. This RoI is constrained to |Δz0| < 10 mm with respect to the perigee of the leading track. Splitting the tracking into these two steps reduces the non-linear dependence of the tracking on occupancy with minimal effect on efficiency. png pdf The processing time per event for two tau trigger strategies, shown for different pileup interaction multiplicities per bunch crossing of 46, 69 and 138, measured with 14 TeV ttbar Monte Carlo running on a 2.4 GHz Intel Xeon CPU. The timing is dominated by tracking - only between 36 and 50 ms is spent in calorimetry. In the first, “One-step”, trigger strategy tracks are reconstructed within a tau candidate Region of Interest (RoI) of size ∆η × ∆φ = 0.4 × 0.4 pointing to a calorimeter cluster and with a spread along the beamline of |z0|< 225 mm corresponding to the size of the interaction region. In the second (“Two-step”) trigger strategy, tracks are first reconstructed within a smaller RoI of size ∆η × ∆φ = 0.1 × 0.1 with the same z0 extent. Additional tracks are then reconstructed in a wider RoI (in η and φ) with ∆η × ∆φ = 0.4 × 0.4, centred on the leading (highest-pT ) track from the previous step. This RoI is constrained to |Δz0| < 10 mm with respect to the perigee of the leading track. png pdf

# 2014 Trigger development Public plots

## ATL-COM-DAQ-2014-088 ID Trigger timing and efficiency results

 The Event Filter Inner Detector (EFID) trigger track reconstruction time for the ATLAS Muon trigger for 14 TeV Z to mu+mu- Monte Carlo, measured running 2.3-2.8 GHz Xeon processors. The ATLAS Trigger code for these measurements was built on April 18th 2013. Shown are the times versus the mean number of pileup interactions for three different pileup multiplicities; 46, 69 and 139 interactions per bunch crossing. Shown are the total EF ID tracking time, and the times for the two most time consuming tracking components - the main pattern recognition algorithm, and the Ambiguity Solver - which together constitute nearly 90% of the ID tracking time at the Event Filter. Also shown are fits to the data points using a quadratic function. png pdf The distribution of processing times per call for the pattern recognition stage of the Event Filter Inner Detector (EFID) trigger tracking measured using Z→e+e- Monte Carlo running on a 2.4 GHz Xeon processor. Shown are the times for the tracking strategy used during the ATLAS Run 1 data taking in 2012 but implemented in different versions of the ATLAS code; one built in April 2013 and a more recent build from August 2014, with mean processing times of 297 ms and 100 ms per call respectively. The code from 2014 is significantly faster than that from 2013 as the offline tracking algorithms used in the Event Filter tracking underwent significant optimisation prior to the later build. Besides the Z to e+e- signal interaction, the Monte Carlo sample has a mean number of additional pileup interactions per bunch crossing, , equal to 46. png pdf The distribution of processing times per call for the Ambiguity Solver stage of the Event Filter Inner Detector (EFID) tracking measured using Z to e+e- Monte Carlo running on a 2.4 GHz Xeon processor. Shown are the times for the tracking strategy used during ATLAS Run 1 data taking in 2012 but implemented in different versions of the ATLAS code; one built in April 2013 and a more recent build from August 2014, with mean processing times of 129 ms and 13.3 ms per call respectively. The code from 2014 is significantly faster than that from 2013 as the offline tracking algorithms used in the Event Filter tracking underwent significant optimisation prior to the later build. Besides the Z to e+e- signal i nteraction, the Monte Carlo sample has a mean number of additional pileup interactions per bunch crossing, equal to 46. png pdf The distribution of total trigger processing time for the complete processing for the 24 GeV isolated electron trigger, from the ATLAS High Level Trigger configured to run on a single node running on Z to e+e- Monte Carlo running on a 2.4 GHz Xeon processor. Shown are the results from the development version of the ATLAS Trigger code built in August 2014 for the full electron trigger, with two alternative "strategies" for the ID Trigger; The "Run 1 strategy" runs the same algorithms that were executed during Run 1 data taking, which consisted of a fast tracking stage followed by a modified version of the full offline precision tracking, running the pattern recognition and Ambiguity Solver stages. In contrast, the "Run 2 strategy" [1] also runs a fast tracking stage, but then performs the precision tracking by directly seeding the ambiguity solver stage from the output of this fast tracking stage rather than running the offline pattern recognition. The full trigger processing times per event are shown and include the time spent running the chain multiple times in events with more than one electron candidate. The times per candidate are approximately 40% faster than the per event times. It should be noted that the total trigger processing includes that for the calorimeter reconstruction and additional, non-tracking, algorithms, which are common to both strategies and contribute approximately 22 ms to the total event processing time in each case. Besides the Z to e+e- signal interaction, the Monte Carlo sample has a mean number additional pileup interactions per bunch crossing, , equal to 46. [1] ATL-DAQ-PUB-2013-002 png pdf The distribution of processing time for the Ambiguity Solver from the ATLAS High Level Trigger running on Z to e+e- Monte Carlo running on a 2.4GHz Xeon processor. Shown are the results from a development version of the ATLAS Trigger code built in August 2014, with two alternative "strategies" for the ID Trigger; The "Run 1 strategy" r uns the same algorithms that were executed during Run 1 data taking, which consisted of a fast tracking stage followed by a modified version of the full offline precision tracking, running the pattern recognition and Ambiguity Solver stages. In contrast, the "Run 2 strategy" [1] also runs a fast tracking stage, but then performs the precision tracking by directly seeding the Ambiguity Solver stage from the output of this fast tracking stage rather than running the offline pattern recognition. Besides the Z to e+e- signal interaction, the Monte Carlo sample has a mean number additional pileup interactions per bunch crossing. equal to 46. [1] ATL-DAQ-PUB-2013-002 png pdf The muon finding efficiency for the ATLAS Inner Detector trigger tracking with respect to the true muon pseudorapidity for muons from Z to mu+mu- Monte Carlo at 14 TeV with a mean number of pileup interactions per bunch crossing, equal to 40. The efficiency is shown for the "Run 2 strategy" [1] which runs a Fast Tracking stage, the output of which is used to directly seed a Precision Tracking stage. Shown are the efficiencies for both the Fast, and the Precision Tracking. The efficiency is defined for trigger tracks matched to within DeltaR < 0.05 of a true muon direction and where the true muon pT > 3 GeV. The simulated events contain hits from the new ATLAS Insertable B-layer. [1] ATL-DAQ-PUB-2013-002 png pdf The muon finding efficiency for the ATLAS Inner Detector trigger tracking with respect to the true muon transverse momentum for muons from Z to mu+mu- Monte Carlo 14 TeV with a mean number of pileup interactions per bunch crossing, , equal to 40. The efficiency is shown for the "Run 2 strategy" [1] which consists of a Fast Tracking stage, the output of which is used to directly seed a Precision Tracking stage. Shown are the efficiencies for both the Fast, and the Precision Tracking. The efficiency is defined for trigger tracks matched to within DeltaR < 0.05 of a true muon direction and where the modulus of the true muon pseudorapidity |eta| < 2.5. The simulated events contain hits from the new ATLAS Insertable B-layer. [1] ATL-DAQ-PUB-2013-002 png pdf

# 2013 Trigger development Pubnote

## ATL-DAQ-PUB-2013-002 Studies for the development of the Inner Detector trigger algorithms at ATLAS

 A schematic of the planned redesigned software HLT Inner Detector trigger png pdf EF reconstructed d0 resolution as a function of eta. The hollow (red) points are without IBL, the solid points are with IBL. png pdf EF reconstructed d0 resolution as a function of signed pt. The hollow (red) points are without IBL, the solid points are with IBL. png pdf EF reconstructed eta resolution as a function of eta. The hollow (red) points are without IBL, the solid points are with IBL. png pdf EF reconstructed eta resolution as a function of pt. The hollow (red) points are without IBL, the solid points are with IBL. png pdf EF reconstructed zed resolution as a function of eta. The hollow (red) points are without IBL, the solid points are with IBL. png pdf EF reconstructed zed resolution as a function of pt. The hollow (red) points are without IBL, the solid points are with IBL. png pdf L2 reconstructed d0 resolution as a function of eta. The hollow (red) points are without IBL, the solid points are with IBL. png pdf L2 reconstructed d0 resolution as a function of pt. The hollow (red) points are without IBL, the solid points are with IBL. png pdf L2 reconstructed eta resolution as a function of eta. The hollow (red) points are without IBL, the solid points are with IBL. png pdf L2 reconstructed eta resolution as a function of pt. The hollow (red) points are without IBL, the solid points are with IBL. png pdf L2 reconstructed z resolution as a function of eta. The hollow (red) points are without IBL, the solid points are with IBL. png pdf L2 reconstructed z resolution as a function of pT. The hollow (red) points are without IBL, the solid points are with IBL. png pdf Relative time per event spent in Level 2 Strategy A algorithms. The timing values were taken from the processing of 1367 events collected in the 2012 running period. png pdf Number of CPU instruction fetches per event generated collected by the callgrind profiling tools Functions with the highest number of instruction fetches are illustrated. The software library containing each function is shown in parentheses. png pdf Total number of unhalted CPU cycles sampled in the ID Trigger function findZinternal. Stalled cycles are a subset of the total unhalted cycles and load latency, branch-stalled cycles are a subset of the total unhalted cycles and load latency, branch-misprediction and instruction latency are a subset of the total stalled cycles. png pdf Comparing execution time for two versions of the Z-Finder algorithm, before and after optimisation to reduce branch mis-prediction. png pdf Comparing execution time for three versions of the test method. Two versions have identical code, but auto-vectorisation has been enabled in one case. The third has had vectorisation explicitly introduced using SSE intrinsics (auto-vectorisation is also enabled). png pdf

# 2012 Data @ 8 TeV

## ATL-COM-DAQ-2013-064 HLT tracking performance in 2012

 The L2 and EF tracking efficiencies measured using a Tag & Probe analysis for probe electron candidates with ET>15 GeV and that are located inside L2 electron Regions of Interest, shown as a function of the offline track pseudorapidity. The trigger selects electron candidates based on the reconstruction in the Calorimeter only. Errors are statistical only. png pdf The L2 and EF tracking efficiencies measured using a Tag & Probe analysis for probe electron candidates with ET>15 GeV and that are located inside L2 electron Regions of Interest, shown as a function of the offline electron track pT. The trigger selects electron candidates based on the reconstruction in the Calorimeter only. Errors are statistical only. png pdf The L2 and EF tracking efficiencies measured using a Tag & Probe analysis for probe electron candidates with ET>15 GeV and that are located inside L2 electron Regions of Interest, shown as a function of the mean number of interactions per bunch crossing. The trigger selects electron candidates based on the reconstruction in the Calorimeter only. Errors are statistical only. png pdf The L2 and EF tracking efficiencies measured using a Tag & Probe analysis for probe electron candidates with ET>15 GeV and that are located inside L2 electron Regions of Interest, shown as a function of the ratio of the offline electron track pT to the offline cluster ET. The trigger selects electron candidates based on the reconstruction in the Calorimeter only. Errors are statistical only. png pdf

## ATL-COM-DAQ-2012-061 HLT Tracking Performance in electron and muon triggers in 2012 Data

 L2 and EF Tracking efficiency for offline electron tracks of |η|<2.5 and that are located inside L2 electron Regions of Interest, shown as a function of the transverse momentum of the offline electron. The trigger monitoring selects high ET electron objects with a threshold of 24 GeV. Entries below this threshold represent candidates that have undergone significant bremstrahlung or originate from background mimicking an electron candidate. Errors are statistical only. png eps L2 and EF Tracking efficiency for offline electron tracks of |η|<2.5 and that are located inside L2 electron Regions of Interest, shown as a function of the pseudorapidity of the offline electron. The trigger monitoring selects high ET electron objects with a threshold of 24 GeV. Errors are statistical only. png eps L2 and EF Tracking efficiency for offline electron tracks of |η|<2.5 and that are located inside L2 electron Regions of Interest, shown as a function of the number of vertices per event. The trigger monitoring selects high ET electron objects with a threshold of 24 GeV. Errors are statistical only. png eps L2 and EF Tracking efficiency for offline electron tracks of |η|<2.5 and that are located inside L2 electron Regions of Interest, shown as a function of the number of offline tracks per event. The trigger monitoring selects high ET electron objects with a threshold of 24 GeV. Errors are statistical only. png eps L2 and EF Tracking efficiency for offline electron tracks of |η|<2.5 and that are located inside L2 electron Regions of Interest, shown as a function of the pseudorapidity of the offline electron. The trigger monitoring selects high ET electron objects with a threshold of 22 GeV (Data 2011) and 24 GeV (Data 2012). Errors are statistical only. png eps L2 and EF Tracking efficiency for offline electron tracks of |η|<2.5 and that are located inside L2 electron Regions of Interest, shown as a function of the number of offline tracks per event. The trigger monitoring selects high ET electron objects with a threshold of 22 GeV (Data 2011) and 24 GeV (Data 2012). Errors are statistical only. png eps

# 2011 Data @ 7 TeV

## ATLAS-COM-CONF-2012-054

 The tracking eﬃciency is studied in an unbiased monitoring mode with events passing a L1 calorimetric threshold of transverse energy 29 GeV. Oﬄine tracks found in the RoI region are used as a reference set of tracks for the eﬃciency calculation. The oﬄine tracks are required to have at least seven clusters in the Si-tracker including two pixel hits, one of which is required to be in the innermost layer of the Pixel detector. A minimal track pT of 1.5 GeV is required, which corresponds to the track selection applied in the oﬄine tau reconstruction. The tracking eﬃciencies as a function of the track parameters pT (a) and η (b) of the oﬄine track and as a function of the number of oﬄine vertices found in the event (c), for 2011 data. The errors shown are statistical. png eps The tracking eﬃciency is studied in an unbiased monitoring mode with events passing a L1 calorimetric threshold of transverse energy 29 GeV. Oﬄine tracks found in the RoI region are used as a reference set of tracks for the eﬃciency calculation. The oﬄine tracks are required to have at least seven clusters in the Si-tracker including two pixel hits, one of which is required to be in the innermost layer of the Pixel detector. A minimal track pT of 1.5 GeV is required, which corresponds to the track selection applied in the oﬄine tau reconstruction. The tracking eﬃciencies as a function of the track parameters pT (a) and η (b) of the oﬄine track and as a function of the number of oﬄine vertices found in the event (c), for 2011 data. The errors shown are statistical. png eps The tracking eﬃciency is studied in an unbiased monitoring mode with events passing a L1 calorimetric threshold of transverse energy 29 GeV. Oﬄine tracks found in the RoI region are used as a reference set of tracks for the eﬃciency calculation. The oﬄine tracks are required to have at least seven clusters in the Si-tracker including two pixel hits, one of which is required to be in the innermost layer of the Pixel detector. A minimal track pT of 1.5 GeV is required, which corresponds to the track selection applied in the oﬄine tau reconstruction. The tracking eﬃciencies as a function of the track parameters pT (a) and η (b) of the oﬄine track and as a function of the number of oﬄine vertices found in the event (c), for 2011 data. The errors shown are statistical. png eps

## ATL-COM-DAQ-2012-038 HLT Tracking Performance for Electrons and Muons in 2011 Data

 L2 and EF Tracking efficiency for offline electron tracks of |η|<2.5 and that are located inside L2 electron Regions of Interest, shown as a funcKon of the transverse momentum of the offline electron. Errors are staKsKcal only. png eps L2 and EF Tracking efficiency for offline electron tracks of |η|<2.5 and that are located inside L2 electron Regions of Interest, shown as a funcKon of the rapidity of the offline electron. Errors are staKsKcal only. png eps L2 and EF Tracking efficiency for offline electron tracks of |η|<2.5 and that are located inside L2 electron Regions of Interest, shown as a funcKon of the number of verKces per event. Errors are staKsKcal only. png eps L2 and EF Tracking efficiency for offline electron tracks of |η|<2.5 and that are located inside L2 electron Regions of Interest, shown as a funcKon of the number of offline tracks per event. Errors are staKsKcal only. png eps L2 and EF Tracking efficiency for offline muon tracks of |η|<2.5 and pT > 15 GeV and that are located inside L2 muon Regions of Interest, shown as a funcKon of the transverse momentum of the offline muon. Errors are staKsKcal only. png eps L2 and EF Tracking efficiency for offline muon tracks of |η|<2.5 and pT > 15 GeV and that are located inside L2 muon Regions of Interest, shown as a funcKon of the rapidity of the offline muon. Errors are staKsKcal only. png eps L2 and EF Tracking efficiency for offline muon tracks of |η|<2.5 and pT > 15 GeV and that are located inside L2 muon Regions of Interest, shown as a funcKon of the number of verKces per event. Errors are staKsKcal only. png eps L2 and EF Tracking efficiency for offline muon tracks of |η|<2.5 and pT > 15 GeV and that are located inside L2 muon Regions of Interest, shown as a funcKon of the number of offline tracks per event. Errors are staKsKcal only. png eps

## ATL-COM-DAQ-2011-085

 L2 and EF Inner Detector tracking reconstruction efficiency wrt offline muon tracks that are located inside monitoring (unbiased) triggers with a transverse momentum threshold 20GeV shown as a function of the transverse momentum of the offline muon. Offline tracks are selected by cuts that reduce secondaries and choose tracks which have at least a minimum number of Si hits. (|η|<2.5, Npixel hits>0, NSCT clusters>5, |zofflineVertex|<200mm, with respect to the offline vertex: |a0|<1.5mm, |Δz sinθ|<1.5 mm.) png eps L2 and EF Inner Detector tracking reconstruction efficiency wrt offline electron candidates that are located inside monitoring (unbiased) triggers with a threshold 22GeV in transverse energy shown as a function of the transverse momentum of the offline electron track. Offline tracks are selected by cuts that reduce secondaries and choose tracks which have at least a minimum number of Si hits. (|η|<2.5, Npixel hits>0, NSCT clusters>5, |zofflineVertex|<200mm, with respect to the offline vertex: |a0|<1.5mm, |Δz sinθ|<1.5 mm.) png eps L2 and EF Inner Detector tracking reconstruction efficiency wrt offline muon tracks that are located inside monitoring (unbiased) triggers with a threshold 20GeV in transverse momentum shown as a function of the number of vertices found by the offline reconstruction. Small inefficiency in the L2 is related to misidentification of the primary interaction and it is addressed in algorithm tuning for higher pile-up – Offline tracks are selected by cuts that reduce secondaries and choose tracks which have at least a minimum number of Si hits. (|η|<2.5, Npixel hits>0, NSCT clusters>5, |zofflineVertex|<200mm, with respect to the offline vertex: |a0|<1.5mm, |Δz sinθ| <1.5 mm.) png eps Residuals of the inverse of the track pT between Inner Detector trigger track and a matching offline Inner Detector track. The resolution is quoted as RMS95, the RMS deviation for the central 95% of the distribution. The trigger tracks come from the monitoring triggers in muon region of interest with a threshold of 20GeV. Small differences between trigger and offline track parameters are expected as a consequence of a simplified material model and different pattern recognition (L2) and partial access to calibration (L2,EF) Offline tracks are selected by cuts that reduce secondaries and choose tracks which have at least a minimum number of Si hits. (|η| <2.5, Npixel hits>0, NSCT clusters>5, |zofflineVertex|<200mm, with respect to the offline vertex: |a0|<1.5mm, |Δz sinθ|<1.5 mm.). png eps Efficiency to find a hit on the Event Filter Inner Detector trigger track in the innermost layer of the pixel detector when a hit is expected and found by the offline reconstruction. A high efficiency is important for signatures which perform rejection on the basis of a non-existent hit in the innermost pixel layer (e.g. tigher electron selection). Whether an innermost layer hit is expected depends on the knowledge of the detector status which can be largely deduced from data and is further refined by the conditions information for the offline. The difference between offline and online calibration caused a small inefficiency in this particular run at the end of negative eta. png eps

# 2010 Data @ 7 TeV

## CERN-PH-EP-2011-078

Plots are on a separate page

## ATL-COM-DAQ-2010-050

 L2 tracking efficiency as function of η Tracking efficiency for L2 as a function of the η of the offline tracks, measured on good offline tracks with various PT thresholds.Trigger tracking has been run in the whole of the detector (ie. the region-of-interest is the whole of the detector). Considered tracks were identified in the Si detectors.The threshold behaviour of the efficiency is expected and comes from using specialised algorithms optimised for L2 requirements on the track reconstruction & timing constraints."Good" offline tracks are selected by cuts that would reduce secondaries and choose tracks which have at least a minimum number of Si hits. (|η|<2.5, Npixel hits>0, NSCT clusters>5, |zofflineVertex|<200mm, with respect to the offline vertex: |a0|<1.5mm, |Δz sinθ|<1.5 mm.) png eps EF tracking efficiency as function of η Tracking efficiency for EF as a function of the η of the offline tracks, measured on good offline tracks with various PT thresholds. Trigger tracking has been run in the whole of the detector (ie. the region-of-interest is the whole of the detector). Considered tracks were identified in the Si detectors.The threshold behaviour of the efficiency is expected and comes from using a configuration tuning optimised for EF requirements on the track reconstruction & timing constraints."Good" offline tracks are selected by cuts that would reduce secondaries and choose tracks which have at least a minimum number of Si hits. (|η|<2.5, Npixel hits>0, NSCT clusters>5, |zofflineVertex|<200mm, with respect to the offline vertex: |a0|<1.5mm, |Δz sinθ|<1.5 mm.) png eps EF d0 residual with respect to offline Δd0 (impact parameter) between good offline tracks of PT>1GeV and matching EF trigger tracks.Trigger tracking has been run in the whole of the detector (ie. the region-of-interest is the whole of the detector). Considered tracks were identified in the Si detectors."Good" offline tracks are selected by cuts that would reduce secondaries and choose tracks which have at least a minimum number of Si hits. (|η|<2.5, Npixel hits>0, NSCT clusters>5, |zofflineVertex|<200mm, with respect to the offline vertex: |a0|<1.5mm, |Δz sinθ|<1.5 mm.) png eps Stability of HLT tracking efficiency over time Efficiency for Si-based L2 and EF tracking for a run at √s=7TeV as a function of time, for good offline tracks of PT>1GeV.Larger error bars in some bins & empty bins reflect partial statistics of the run used in this plot.Trigger tracking has been run in the whole of the detector (ie. the region-of-interest is the whole of the detector). Considered tracks were identified in the Si detectors."Good" offline tracks are selected by cuts that would reduce secondaries and choose tracks which have at least a minimum number of Si hits. (|η|<2.5, Npixel hits>0, NSCT clusters>5, |zofflineVertex|<200mm, with respect to the offline vertex: |a0|<1.5mm, |Δz sinθ|<1.5 mm.) png pdf Mean track z position over time Mean track z0 position obtained from L2 and EF tracks for a run at √s=7TeV as a function of time.Larger error bars in some bins & empty bins reflect partial statistics of the run used in this plot.Trigger tracking has been run in the whole of the detector (ie. the region-of-interest is the whole of the detector). Considered tracks were identified in the Si detectors. png pdf HLT tracking efficiency for muon tracks as function of η L2 and EF Si-tracking efficiency for good offline muon tracks of PT>4GeV and that are located inside L2 muon regions-of-interest, shown as a function of the η of the offline muon."Good" offline tracks are selected by cuts that would reduce secondaries and choose tracks which have at least a minimum number of Si hits. (|η|<2.5, Npixel hits>0, NSCT clusters>5, |zofflineVertex|<200mm, with respect to the offline vertex: |a0|<1.5mm, |Δz sinθ|<1.5 mm.) png pdf HLT tracking efficiency for muon tracks as function of pT L2 and EF Si-tracking efficiency for offline muon tracks of |η|<2.5 and that are located inside L2 muon regions-of-interest, shown as a function of the transverse momentum of the offline muon. Offline tracks are selected by cuts that would reduce secondaries and choose tracks which have at least a minimum number of Si hits. (|η|<2.5, Npixel hits>0, NSCT clusters>5, |zofflineVertex|<200mm, with respect to the offline vertex: |a0|<1.5mm, |Δz sinθ|<1.5 mm.) png pdf Track parameter residuals wrt offline - Level 2 and Atlas.EventFilter The resolution for HLT tracks with respect to the Offline tracks for the transverse impact parameter measured with respect to the offline vertex as a function of the offline track transverse momentum. The resolution is quoted as σ95%, the RMS deviation for the central 95% of the distribution. Trigger tracking has been run in the whole of the detector (ie. the region-of-interest is the whole of the detector). Considered tracks were identified in the Si detectors. The L2 tracking does not reproduce the offline tracks as well as the EF due to different pattern recognition algorithms and a simplified material model. Small differences in the EF wrt the offline are a consequence of a configuration optimized for trigger requirements and of partial access to the calibration in the online environment. Offline tracks are selected by cuts that would reduce secondaries and choose tracks which have at least a minimum number of Si hits. (|η|<2.5, Npixel hits>0, NSCT clusters>5, |zofflineVertex|<200mm, with respect to the offline vertex: |a0|<1.5mm, |Δz sinθ|<1.5 mm.) png pdf Tracking efficiency for LVL2 tracks wrt offline using the jet instance as a function of η Offline track selection of tracks with at least 1 space point in the pixel and 6 clusters in the SCT, pT>2GeV,|η|<2.5, |d0|<1.5mm, |z0|<200mm (d0 and z0 cuts are corrected with respect to offline vertices), geometrical one-to-one best matching with ΔR<0.05 png pdf Tracking efficiency for LVL2 tracks wrt offline using the jet instance as a function of pT Offline track selection of tracks with at least 1 space point in the pixel and 6 clusters in the SCT, |η|<2.5, |d0|<1.5mm, |z0|<200mm (d0 and z0 cuts are corrected with respect to offline vertices), geometrical one-to-one best matching with ΔR<0.05 png pdf Tracking efficiency for LVL2 tracks wrt offline using the jet instance as a function of Ï• Offline track selection of tracks with at least 1 space point in the pixel and 6 clusters in the SCT, pT>2GeV,|η|<2.5, |d0|<1.5mm, |z0|<200mm (d0 and z0 cuts are corrected with respect to offline vertices), geometrical one-to-one best matching with ΔR<0.05 A smaller efficiency at -2 reflects distribution of inactive sensors. png pdf Tracking efficiency for LVL2 tracks wrt offline using the tau instance as a function of η Offline track selection of tracks with at least 1 space point in the pixel and 6 clusters in the SCT, pT>2GeV,|η|<2.5, |d0|<1.5mm, |z0|<200mm (d0 and z0 cuts are corrected with respect to offline vertices), geometrical one-to-one best matching with ΔR<0.05 png pdf Tracking efficiency for LVL2 tracks wrt offline using the tau instance as a function of pT Offline track selection of tracks with at least 1 space point in the pixel and 6 clusters in the SCT, |η|<2.5, |d0|<1.5mm, |z0|<200mm (d0 and z0 cuts are corrected with respect to offline vertices), geometrical one-to-one best matching with ΔR<0.05 png pdf Tracking efficiency for LVL2 tracks wrt offline using the tau instance as a function of Ï• Offline track selection of tracks with at least 1 space point in the pixel and 6 clusters in the SCT, pT>2GeV,|η|<2.5, |d0|<1.5mm, |z0|<200mm (d0 and z0 cuts are corrected with respect to offline vertices), geometrical one-to-one best matching with ΔR<0.05 A smaller efficiency at -2 reflects distribution of inactive sensors. png pdf Tracking efficiencies for the e/gamma trigger signature group The figure shows the inidividual efficiencies of the L2 and EF tracking algorithms for medium quality electron candidates with a cluster ET of at least 5 GeV as function of the transverse momentum of the offline reconstructed electron track. Offline electron candidates from photon conversions have been excluded. Note, mainly due to brems-strahlung effects the pT of the track can be much lower than the cluster ET. The data were collected in a run on May 21th. 2010. gif eps Tracking efficiencies for the e/gamma trigger signature group The figure shows efficiencies inidvidually for the L2 and EF tracking algorithms for medium quality electron candidates with a cluster ET of at least 5 GeV as function of the h direction of the offline reconstructed electron track. For this figure no additional track pT cut is applied. Offline electron candidates from photon conversions have been excluded. The data were collected in a run on May 21th. 2010. gif eps Number of pixel hits per EF track vs track η Trigger tracking has been run in the whole of the detector (ie. the region-of-interest is the whole of the detector, “FullScan” instance of the algorithm). Minimum bias data and MC are compared png eps Number of holes per L2 track A hole is defined as an ID layer in which an offline track has a hit but the matched L2 track does not. The matching requirement between trigger and offline tracks is ΔR<0.1. Only offline tracks passing the following criteria are considered: |η|<2.5, pT>1GeV, |z0sinθ|<1.5mm (w.r.t. primary vertex), |d0|<1.5mm (w.r.t. primary vertex), number of pixel hits > 0, number of SCT clusters > 5 Trigger tracking has been run in the whole of the detector (ie. the region-of-interest is the whole of the detector, “FullScan” instance of the algorithm). Minimum bias data and MC are compared png eps L2 and EF tracking efficiency vs matched offline track pT for tau instances Match trigger tracks with all offline reconstructed tracks inside the tau RoI. The matching requirement between trigger and offline tracks is ΔR<0.1. Only offline tracks passing the following criteria are considered: |η|<2.5, |z0sinθ|<1.5mm (w.r.t. primary vertex), |d0|<1.5mm (w.r.t. primary vertex), number of b-layer hits >0, number of pixel hits > 1, number of total Si hits > 6, χ2 probability of track fit > 1% png pdf L2 and EF tracking efficiency vs matched offline track η for tau instances Match trigger tracks with all offline reconstructed tracks inside the tau RoI. The matching requirement between trigger and offline tracks is ΔR<0.1. Only offline tracks passing the following criteria are considered: |η|<2.5, pT>1GeV, |z0sinθ|<1.5mm (w.r.t. primary vertex), |d0|<1.5mm (w.r.t. primary vertex), number of b-layer hits >0, number of pixel hits > 1, number of total Si hits > 6, χ2 probability of track fit > 1% pdf png L2 and EF tracking efficiency vs matched offline track pT for jet instances Match trigger tracks with all offline reconstructed tracks inside the jet RoI. The matching requirement between trigger and offline tracks is ΔR<0.1. Only offline tracks passing the following criteria are considered: |η|<2.5, |z0sinθ|<1.5mm (w.r.t. primary vertex), |d0|<1.5mm (w.r.t. primary vertex), number of b-layer hits >0, number of pixel hits > 1, number of total Si hits > 6, χ2 probability of track fit > 1% pdf png L2 and EF tracking efficiency vs matched offline track η for jet instances Match trigger tracks with all offline reconstructed tracks inside the jet RoI. The matching requirement between trigger and offline tracks is ΔR<0.1. Only offline tracks passing the following criteria are considered: |η|<2.5, pT>2GeV, |z0sinθ|<1.5mm (w.r.t. primary vertex), |d0|<1.5mm (w.r.t. primary vertex), number of b-layer hits >0, number of pixel hits > 1, number of total Si hits > 6, χ2 probability of track fit > 1% pdf png

-- MarkSutton - 31-July-2020 Approved plots from Run 2 Responsible: JiriMasik
Subject: public

Topic attachments
I Attachment History Action Size Date Who Comment
eps 2011K-ipt-vs-eta-mu10-pT10-v3.eps r1 manage 14.6 K 2012-06-29 - 14:53 JiriMasik
png 2011K-ipt-vs-eta-mu10-pT10-v3.png r1 manage 12.5 K 2012-06-29 - 14:53 JiriMasik
pdf ATL-COM-DAQ-2013-064-plot1.pdf r1 manage 65.1 K 2013-09-23 - 14:09 JiriMasik
png ATL-COM-DAQ-2013-064-plot1.png r1 manage 79.1 K 2013-09-23 - 14:07 JiriMasik
pdf ATL-COM-DAQ-2013-064-plot2.pdf r1 manage 40.8 K 2013-09-23 - 14:09 JiriMasik
png ATL-COM-DAQ-2013-064-plot2.png r1 manage 80.6 K 2013-09-23 - 14:07 JiriMasik
pdf ATL-COM-DAQ-2013-064-plot3.pdf r1 manage 46.7 K 2013-09-23 - 14:09 JiriMasik
png ATL-COM-DAQ-2013-064-plot3.png r1 manage 78.3 K 2013-09-23 - 14:09 JiriMasik
pdf ATL-COM-DAQ-2013-064-plot4.pdf r1 manage 47.4 K 2013-09-23 - 14:09 JiriMasik
png ATL-COM-DAQ-2013-064-plot4.png r1 manage 84.7 K 2013-09-23 - 14:09 JiriMasik
pdf ATL-COM-DAQ-2014-088-plot1.pdf r1 manage 16.4 K 2014-08-23 - 15:08 MarkSutton
png ATL-COM-DAQ-2014-088-plot1.png r1 manage 143.1 K 2014-08-23 - 15:09 MarkSutton
pdf ATL-COM-DAQ-2014-088-plot2.pdf r1 manage 14.9 K 2014-08-23 - 15:20 MarkSutton
png ATL-COM-DAQ-2014-088-plot2.png r1 manage 103.0 K 2014-08-23 - 15:20 MarkSutton
pdf ATL-COM-DAQ-2014-088-plot3.pdf r1 manage 15.1 K 2014-08-23 - 15:20 MarkSutton
png ATL-COM-DAQ-2014-088-plot3.png r1 manage 104.1 K 2014-08-23 - 15:09 MarkSutton
pdf ATL-COM-DAQ-2014-088-plot4.pdf r1 manage 15.4 K 2014-08-23 - 15:08 MarkSutton
png ATL-COM-DAQ-2014-088-plot4.png r1 manage 113.4 K 2014-08-23 - 15:09 MarkSutton
pdf ATL-COM-DAQ-2014-088-plot5.pdf r1 manage 16.3 K 2014-08-23 - 15:08 MarkSutton
png ATL-COM-DAQ-2014-088-plot5.png r1 manage 124.6 K 2014-08-23 - 15:09 MarkSutton
pdf ATL-COM-DAQ-2014-088-plot6.pdf r1 manage 16.0 K 2014-08-23 - 15:08 MarkSutton
png ATL-COM-DAQ-2014-088-plot6.png r1 manage 90.9 K 2014-08-23 - 15:09 MarkSutton
pdf ATL-COM-DAQ-2014-088-plot7.pdf r1 manage 14.9 K 2014-08-23 - 15:08 MarkSutton
png ATL-COM-DAQ-2014-088-plot7.png r1 manage 85.9 K 2014-08-23 - 15:09 MarkSutton
pdf ATL-COM-DAQ-2015-026-plot1.pdf r1 manage 57.0 K 2015-03-30 - 18:57 MarkSutton ID Trigger tau slice timing plots
png ATL-COM-DAQ-2015-026-plot1.png r1 manage 118.8 K 2015-03-30 - 18:57 MarkSutton ID Trigger tau slice timing plots
pdf ATL-COM-DAQ-2015-026-plot2.pdf r1 manage 36.3 K 2015-03-30 - 18:57 MarkSutton ID Trigger tau slice timing plots
png ATL-COM-DAQ-2015-026-plot2.png r1 manage 135.9 K 2015-03-30 - 18:57 MarkSutton ID Trigger tau slice timing plots
pdf ATL-COM-DAQ-2015-110-plot01.pdf r1 manage 14.9 K 2015-08-03 - 13:17 MarkSutton
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pdf ATL-COM-DAQ-2016-083-plot01.pdf r1 manage 177.4 K 2016-07-28 - 21:20 MarkSutton
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pdf ATL-COM-DAQ-2016-121.pdf r1 manage 15.6 K 2016-09-22 - 14:21 JiriMasik
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pdf ATL-COM-DAQ-2017-107-plot00.pdf r1 manage 17.3 K 2017-09-22 - 21:34 MarkSutton
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pdf ATL-COM-DAQ-2018-061-fig1.pdf r1 manage 57.4 K 2018-06-15 - 10:58 MarkSutton 2018 performance plots for muons and taus
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pdf ATL-COM-DAQ-2018-061-fig2.pdf r1 manage 56.5 K 2018-06-15 - 10:58 MarkSutton 2018 performance plots for muons and taus
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pdf ATL-COM-DAQ-2018-061-fig3.pdf r1 manage 64.0 K 2018-06-15 - 10:58 MarkSutton 2018 performance plots for muons and taus
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pdf ATL-COM-DAQ-2018-061-fig4.pdf r1 manage 109.2 K 2018-06-15 - 10:58 MarkSutton 2018 performance plots for muons and taus
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pdf ATL-COM-DAQ-2018-061-fig5.pdf r1 manage 82.2 K 2018-06-15 - 10:58 MarkSutton 2018 performance plots for muons and taus
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pdf ATL-COM-DAQ-2018-061-fig6.pdf r1 manage 58.1 K 2018-06-15 - 10:58 MarkSutton 2018 performance plots for muons and taus
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pdf ATL-COM-DAQ-2018-061-fig7.pdf r1 manage 70.2 K 2018-06-15 - 10:58 MarkSutton 2018 performance plots for muons and taus
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pdf ATL-COM-DAQ-2020-059-plot00.pdf r1 manage 47.3 K 2020-07-31 - 17:20 MarkSutton
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pdf ATL-COM-DAQ-2020-059-plot12.pdf r1 manage 59.2 K 2020-07-31 - 17:21 MarkSutton
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pdf ATL-COM-DAQ-2020-059-plot13.pdf r1 manage 67.1 K 2020-07-31 - 17:21 MarkSutton
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pdf ATL-COM-DAQ-2021-003-plot00.pdf r1 manage 15.9 K 2021-03-03 - 15:55 NishaNareshbhaiLad Machine Learning Predictor Plots for HLT Track Seeding
png ATL-COM-DAQ-2021-003-plot00.png r1 manage 83.5 K 2021-03-03 - 15:55 NishaNareshbhaiLad Machine Learning Predictor Plots for HLT Track Seeding
pdf ATL-COM-DAQ-2021-003-plot01.pdf r1 manage 17.4 K 2021-03-03 - 15:55 NishaNareshbhaiLad Machine Learning Predictor Plots for HLT Track Seeding
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pdf ATL-COM-DAQ-2021-003-plot02.pdf r1 manage 1142.6 K 2021-03-03 - 15:55 NishaNareshbhaiLad Machine Learning Predictor Plots for HLT Track Seeding
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pdf ATL-COM-DAQ-2021-003-plot03.pdf r1 manage 15.0 K 2021-03-03 - 15:55 NishaNareshbhaiLad Machine Learning Predictor Plots for HLT Track Seeding
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pdf ATL-COM-DAQ-2021-003-plot04.pdf r1 manage 14.9 K 2021-03-03 - 15:55 NishaNareshbhaiLad Machine Learning Predictor Plots for HLT Track Seeding
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pdf ATL-COM-DAQ-2021-003-plot07.pdf r1 manage 55.3 K 2021-03-03 - 18:00 NishaNareshbhaiLad
pdf ATL-COM-DAQ-2021-003-plot08.pdf r1 manage 70.9 K 2021-03-03 - 18:00 NishaNareshbhaiLad
pdf ATL-COM-DAQ-2022-010-HMTeff.pdf r1 manage 53.0 K 2022-03-18 - 20:50 AlexanderKevinGilbert Inner Detector Z-Finder Performance
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pdf ATL-COM-DAQ-2022-010-evn1.pdf r1 manage 18.7 K 2022-03-18 - 20:50 AlexanderKevinGilbert Inner Detector Z-Finder Performance
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pdf ATL-COM-DAQ-2022-010-nVtxEff.pdf r1 manage 25.9 K 2022-03-18 - 20:50 AlexanderKevinGilbert Inner Detector Z-Finder Performance
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pdf ATL-COM-DAQ-2022-010-weightntrackcorr.pdf r1 manage 20.3 K 2022-03-18 - 20:50 AlexanderKevinGilbert Inner Detector Z-Finder Performance
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png ATL-COM-DAQ-2022-011-plo1.png r1 manage 22.5 K 2022-03-23 - 05:17 KunihiroNagano
pdf ATL-COM-DAQ-2022-011-plot1.pdf r1 manage 15.7 K 2022-03-23 - 05:17 KunihiroNagano
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pdf ATL-COM-DAQ-2022-011-plot2.pdf r1 manage 15.3 K 2022-03-28 - 11:55 KunihiroNagano
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pdf ATL-COM-DAQ-2022-022-plot01.pdf r1 manage 15.7 K 2022-05-13 - 11:39 ZuchenHuang Optimization of Fullscan Trigger Tracking
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pdf ATL-COM-DAQ-2022-022-plot02.pdf r1 manage 16.4 K 2022-05-13 - 11:39 ZuchenHuang Optimization of Fullscan Trigger Tracking
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pdf ATL-COM-DAQ-2022-022-plot03.pdf r1 manage 19.2 K 2022-05-13 - 11:39 ZuchenHuang Optimization of Fullscan Trigger Tracking
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pdf ATL-COM-DAQ-2022-023-plot1.pdf r1 manage 16.3 K 2022-05-11 - 13:29 JonathanLong HLT LRT Expected Performance
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pdf ATL-COM-DAQ-2022-023-plot2.pdf r1 manage 15.7 K 2022-05-11 - 13:29 JonathanLong HLT LRT Expected Performance
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pdf ATL-COM-DAQ-2022-023-plot3.pdf r1 manage 15.6 K 2022-05-11 - 13:29 JonathanLong HLT LRT Expected Performance
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pdf ATL-COM-DAQ-2022-023-plot4.pdf r1 manage 17.8 K 2022-05-11 - 13:29 JonathanLong HLT LRT Expected Performance
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pdf ATL-COM-DAQ-2022-023-plot5.pdf r1 manage 17.4 K 2022-05-11 - 13:29 JonathanLong HLT LRT Expected Performance
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pdf ATL-COM-DAQ-2022-023-plot8.pdf r1 manage 15.5 K 2022-05-11 - 13:30 JonathanLong HLT LRT Expected Performance
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pdf ATL-COM-DAQ-2022-026-plot1.pdf r1 manage 14.1 K 2022-05-13 - 10:44 BenjaminPhilipKerridge
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pdf ATL-COM-DAQ-2022-026-plot2.pdf r1 manage 14.6 K 2022-05-13 - 10:44 BenjaminPhilipKerridge
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pdf ATL-COM-DAQ-2022-055-plot00.pdf r1 manage 17.0 K 2022-07-08 - 10:30 MarkSutton
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pdf ATL-COM-DAQ-2022-091-plot00.pdf r1 manage 16.0 K 2022-10-09 - 20:35 AndriusVaitkus Fast track seed selection for track following in the Inner Detector Trigger track reconstruction
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pdf ATL-COM-DAQ-2022-091-plot01.pdf r1 manage 15.5 K 2022-10-09 - 20:35 AndriusVaitkus Fast track seed selection for track following in the Inner Detector Trigger track reconstruction
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pdf ATL-COM-DAQ-2022-091-plot02.pdf r1 manage 14.7 K 2022-10-09 - 20:35 AndriusVaitkus Fast track seed selection for track following in the Inner Detector Trigger track reconstruction
png ATL-COM-DAQ-2022-091-plot02.png r1 manage 19.7 K 2022-10-09 - 20:35 AndriusVaitkus Fast track seed selection for track following in the Inner Detector Trigger track reconstruction
pdf ATL-COM-DAQ-2022-091-plot03.pdf r1 manage 14.7 K 2022-10-09 - 20:35 AndriusVaitkus Fast track seed selection for track following in the Inner Detector Trigger track reconstruction
png ATL-COM-DAQ-2022-091-plot03.png r1 manage 19.8 K 2022-10-09 - 20:35 AndriusVaitkus Fast track seed selection for track following in the Inner Detector Trigger track reconstruction
pdf ATL-COM-DAQ-2022-091-plot04.pdf r1 manage 15.3 K 2022-10-09 - 20:35 AndriusVaitkus Fast track seed selection for track following in the Inner Detector Trigger track reconstruction
png ATL-COM-DAQ-2022-091-plot04.png r1 manage 19.0 K 2022-10-09 - 20:35 AndriusVaitkus Fast track seed selection for track following in the Inner Detector Trigger track reconstruction
pdf ATL-DAQ-2022-011-plot2.pdf r1 manage 15.3 K 2022-03-23 - 05:26 KunihiroNagano
pdf CPUIr-prelim.pdf r1 manage 175.2 K 2013-10-03 - 15:23 MarkSutton
png CPUIr-prelim.png r1 manage 114.7 K 2013-10-03 - 15:23 MarkSutton
pdf EF_rd0_vs_eta_sigma.pdf r1 manage 57.6 K 2013-10-03 - 15:23 MarkSutton
png EF_rd0_vs_eta_sigma.png r1 manage 77.2 K 2013-10-03 - 15:23 MarkSutton
pdf EF_rd0_vs_signed_pt_sigma.pdf r1 manage 42.8 K 2013-10-03 - 15:23 MarkSutton
png EF_rd0_vs_signed_pt_sigma.png r1 manage 86.5 K 2013-10-03 - 15:23 MarkSutton
pdf EF_reta_vs_eta_sigma.pdf r1 manage 57.0 K 2013-10-03 - 15:23 MarkSutton
png EF_reta_vs_eta_sigma.png r1 manage 71.9 K 2013-10-03 - 15:23 MarkSutton
pdf EF_reta_vs_pt_sigma.pdf r1 manage 57.4 K 2013-10-03 - 15:23 MarkSutton
png EF_reta_vs_pt_sigma.png r1 manage 74.0 K 2013-10-03 - 15:23 MarkSutton
pdf EF_rzed_vs_eta_sigma.pdf r1 manage 57.4 K 2013-10-03 - 15:43 MarkSutton
png EF_rzed_vs_eta_sigma.png r1 manage 69.1 K 2013-10-03 - 15:43 MarkSutton
pdf EF_rzed_vs_signed_pt_sigma.pdf r1 manage 56.8 K 2013-10-03 - 15:43 MarkSutton
png EF_rzed_vs_signed_pt_sigma.png r1 manage 76.3 K 2013-10-03 - 15:43 MarkSutton
eps Electrons-SuperImp-eff_vs_ntracks.eps r1 manage 10.8 K 2012-06-29 - 10:12 JiriMasik
png Electrons-SuperImp-eff_vs_ntracks.png r1 manage 9.1 K 2012-06-29 - 10:12 JiriMasik
eps Electrons-SuperImp-eta_eff.eps r1 manage 14.0 K 2012-06-29 - 10:12 JiriMasik
png Electrons-SuperImp-eta_eff.png r1 manage 9.7 K 2012-06-29 - 10:12 JiriMasik
pdf GooDA-findZ-prelim.pdf r1 manage 101.3 K 2013-10-03 - 15:43 MarkSutton
png GooDA-findZ-prelim.png r1 manage 105.6 K 2013-10-03 - 15:43 MarkSutton
pdf IDdataprepGPU.pdf r1 manage 28.3 K 2015-04-08 - 17:43 JohnTMBaines ATL-COM-DAQ-2015-036
png IDdataprepGPU.png r1 manage 219.9 K 2015-04-08 - 17:43 JohnTMBaines ATL-COM-DAQ-2015-036
pdf L2StratA-rel.pdf r1 manage 97.4 K 2013-10-03 - 15:33 MarkSutton
png L2StratA-rel.png r1 manage 363.5 K 2013-10-03 - 15:33 MarkSutton
pdf L2_rd0_vs_eta_sigma.pdf r1 manage 57.4 K 2013-10-03 - 15:45 MarkSutton
png L2_rd0_vs_eta_sigma.png r1 manage 77.8 K 2013-10-03 - 15:45 MarkSutton
pdf L2_rd0_vs_signed_pt_sigma.pdf r1 manage 42.7 K 2013-10-03 - 15:45 MarkSutton
png L2_rd0_vs_signed_pt_sigma.png r1 manage 86.3 K 2013-10-03 - 15:45 MarkSutton
pdf L2_reta_vs_eta_sigma.pdf r1 manage 56.9 K 2013-10-03 - 15:45 MarkSutton
png L2_reta_vs_eta_sigma.png r1 manage 72.3 K 2013-10-03 - 15:45 MarkSutton
pdf L2_reta_vs_pt_sigma.pdf r1 manage 57.2 K 2013-10-03 - 15:45 MarkSutton
png L2_reta_vs_pt_sigma.png r1 manage 74.6 K 2013-10-03 - 15:45 MarkSutton
pdf L2_rzed_vs_eta_sigma.pdf r1 manage 57.6 K 2013-10-03 - 15:49 MarkSutton
png L2_rzed_vs_eta_sigma.png r1 manage 69.4 K 2013-10-03 - 15:49 MarkSutton
pdf L2_rzed_vs_signed_pt_sigma.pdf r1 manage 56.6 K 2013-10-03 - 15:49 MarkSutton
png L2_rzed_vs_signed_pt_sigma.png r1 manage 76.3 K 2013-10-03 - 15:49 MarkSutton
pdf OptimisationTiming-prelim.pdf r1 manage 256.5 K 2013-10-03 - 15:44 MarkSutton
png OptimisationTiming-prelim.png r1 manage 154.8 K 2013-10-03 - 15:44 MarkSutton
pdf VectorTiming-prelim.pdf r1 manage 261.0 K 2013-10-03 - 15:44 MarkSutton
png VectorTiming-prelim.png r1 manage 202.9 K 2013-10-03 - 15:44 MarkSutton
eps electrons-2011-PeriodLM.FinalDecorations.eff_vs_ntracks.eps r1 manage 10.2 K 2012-06-29 - 11:55 JiriMasik
png electrons-2011-PeriodLM.FinalDecorations.eff_vs_ntracks.png r1 manage 8.7 K 2012-06-29 - 11:55 JiriMasik
eps electrons-2011-PeriodLM.FinalDecorations.eff_vs_nvtx.eps r1 manage 11.3 K 2012-06-29 - 11:55 JiriMasik
png electrons-2011-PeriodLM.FinalDecorations.eff_vs_nvtx.png r1 manage 9.0 K 2012-06-29 - 11:55 JiriMasik
eps electrons-2011-PeriodLM.FinalDecorations.eta_eff.eps r1 manage 12.7 K 2012-06-29 - 11:55 JiriMasik
png electrons-2011-PeriodLM.FinalDecorations.eta_eff.png r1 manage 8.7 K 2012-06-29 - 11:55 JiriMasik
eps electrons-2011-PeriodLM.FinalDecorations.pT_eff.eps r1 manage 13.6 K 2012-06-29 - 11:55 JiriMasik
png electrons-2011-PeriodLM.FinalDecorations.pT_eff.png r1 manage 9.4 K 2012-06-29 - 11:55 JiriMasik
eps electrons-2012-PeriodA4.FinalDecorations.FinalLabels.NoGrid.eff_vs_ntracks.eps r1 manage 9.4 K 2012-06-29 - 07:49 JiriMasik
png electrons-2012-PeriodA4.FinalDecorations.FinalLabels.NoGrid.eff_vs_ntracks.png r1 manage 8.5 K 2012-06-29 - 07:51 JiriMasik
eps electrons-2012-PeriodA4.FinalDecorations.FinalLabels.NoGrid.eta_eff.eps r1 manage 11.4 K 2012-06-29 - 07:52 JiriMasik
png electrons-2012-PeriodA4.FinalDecorations.FinalLabels.NoGrid.eta_eff.png r1 manage 8.3 K 2012-06-29 - 07:52 JiriMasik
eps electrons-2012-PeriodA4.FinalDecorations.FinalLabels.NoGrid.pT_eff.eps r1 manage 10.7 K 2012-06-29 - 07:53 JiriMasik
png electrons-2012-PeriodA4.FinalDecorations.FinalLabels.NoGrid.pT_eff.png r1 manage 8.7 K 2012-06-29 - 07:53 JiriMasik
eps electrons-2012-PeriodA4.FinalDecorations.FinalLabels.eff_vs_nvtx.eps r1 manage 11.4 K 2012-06-29 - 07:47 JiriMasik
png electrons-2012-PeriodA4.FinalDecorations.FinalLabels.eff_vs_nvtx.png r1 manage 9.7 K 2012-06-29 - 07:49 JiriMasik
eps muons-2011-PeriodLM.FinalDecorations.eff_vs_ntracks.eps r1 manage 10.1 K 2012-06-29 - 11:55 JiriMasik
png muons-2011-PeriodLM.FinalDecorations.eff_vs_ntracks.png r1 manage 8.6 K 2012-06-29 - 11:55 JiriMasik
eps muons-2011-PeriodLM.FinalDecorations.eff_vs_nvtx.eps r1 manage 11.2 K 2012-06-29 - 11:57 JiriMasik
png muons-2011-PeriodLM.FinalDecorations.eff_vs_nvtx.png r1 manage 8.8 K 2012-06-29 - 11:57 JiriMasik
eps muons-2011-PeriodLM.FinalDecorations.eta_eff.eps r1 manage 12.1 K 2012-06-29 - 11:57 JiriMasik
png muons-2011-PeriodLM.FinalDecorations.eta_eff.png r1 manage 8.5 K 2012-06-29 - 11:57 JiriMasik
eps muons-2011-PeriodLM.FinalDecorations.pT_eff.eps r1 manage 10.7 K 2012-06-29 - 11:57 JiriMasik
png muons-2011-PeriodLM.FinalDecorations.pT_eff.png r1 manage 8.7 K 2012-06-29 - 11:57 JiriMasik
eps periodK-blayer-vs-eta-pt6-v3.eps r1 manage 9.2 K 2012-06-29 - 14:54 JiriMasik
png periodK-blayer-vs-eta-pt6-v3.png r1 manage 8.5 K 2012-06-29 - 14:54 JiriMasik
eps periodK-offline-el-vs-pT-pt10-v3.eps r1 manage 10.0 K 2012-06-29 - 14:53 JiriMasik
png periodK-offline-el-vs-pT-pt10-v3.png r1 manage 10.8 K 2012-06-29 - 14:53 JiriMasik
eps periodK-staco-trk-vs-nvtx-mu10-v3.eps r1 manage 9.9 K 2012-06-29 - 14:53 JiriMasik
png periodK-staco-trk-vs-nvtx-mu10-v3.png r1 manage 9.3 K 2012-06-29 - 14:53 JiriMasik
png periodK-staco-trk-vs-pT-mu10-pt6-v3.png r1 manage 10.7 K 2012-06-29 - 14:53 JiriMasik
eps periodL-tau29-trk-vs-eta.eps r1 manage 11.4 K 2013-09-24 - 16:09 JiriMasik
png periodL-tau29-trk-vs-eta.png r1 manage 13.3 K 2013-09-24 - 16:09 JiriMasik
eps periodL-tau29-trk-vs-nvtx.eps r1 manage 14.2 K 2013-09-24 - 16:09 JiriMasik
png periodL-tau29-trk-vs-nvtx.png r1 manage 15.0 K 2013-09-24 - 16:09 JiriMasik
eps periodL-tau29-trk-vs-pT.eps r1 manage 12.9 K 2013-09-24 - 16:09 JiriMasik
png periodL-tau29-trk-vs-pT.png r1 manage 12.9 K 2013-09-24 - 16:09 JiriMasik
pdf plan.pdf r1 manage 446.8 K 2013-10-03 - 15:33 MarkSutton
png plan.png r1 manage 201.5 K 2013-10-03 - 15:33 MarkSutton