# 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.

# 2018 @ 13 TeV

## Trigger b-tagging hybrid performances ATL-COM-DAQ-2019-018 (May, 2019)

 The ATLAS \textit{b}-jet trigger uses a BDT algorithm to separate \textit{b}-jets from light and \textit{c}-jet backgrounds. The BDT algorithm used for \textit{b}-jet triggers in 2018 data taking has been trained on a $t\bar{t}$ Monte Carlo simulation. The same BDT algorithm has been trained on an alternative (Hybrid) training sample consisting of a mixture of $t\bar{t}$ and $Z'$ Monte Carlo samples, in the same way the BDT algorithm for offline jets is trained. Performance of \textit{b}-tagging algorithms (measured using $t\bar{t}$ Monte Carlo events), with respect to the true flavour of jets, is shown in terms of light-jet rejection as a function of \textit{b}-jet efficiency. Expected performance of \textit{b}-tagging algorithm (MV2c10) for \textit{b}-jet triggers in 2018 data-taking (blue solid line) is compared to the same \textit{b}-tagging algorithm trained on the Hybrid training sample (red solid line). [png] [pdf], [eps]

 The ATLAS \textit{b}-jet trigger uses a BDT algorithm to separate \textit{b}-jets from light and \textit{c}-jet backgrounds. The BDT algorithm used for \textit{b}-jet triggers in 2018 data taking has been trained on a $t\bar{t}$ Monte Carlo simulation. The same BDT algorithm has been trained on an alternative (Hybrid) training sample consisting of a mixture of $t\bar{t}$ and $Z'$ Monte Carlo samples, in the same way the BDT algorithm for offline jets is trained. Performance of \textit{b}-tagging algorithms (measured using $t\bar{t}$ Monte Carlo events), with respect to the true flavour of jets, is shown in terms of \textit{c}-jet rejection as a function of \textit{b}-jet efficiency. Expected performance of \textit{b}-tagging algorithm (MV2c10) for \textit{b}-jet triggers in 2018 data-taking (blue solid line) is compared to the same \textit{b}-tagging algorithm trained on the Hybrid training sample (red solid line). [png] [pdf], [eps]

 The ATLAS \textit{b}-jet trigger uses a BDT algorithm to separate \textit{b}-jets from light and \textit{c}-jet backgrounds. The BDT algorithm used for \textit{b}-jet triggers in 2018 data taking has been trained on a $t\bar{t}$ Monte Carlo simulation. The same BDT algorithm has been trained on an alternative (Hybrid) training sample consisting of a mixture of $t\bar{t}$ and $Z'$ Monte Carlo samples, in the same way the BDT algorithm for offline jets is trained. Performance of \textit{b}-tagging algorithms (measured using $Z'$ Monte Carlo events), with respect to the true flavour of jets, is shown in terms of light-jet rejection as a function of \textit{b}-jet efficiency. Expected performance of \textit{b}-tagging algorithm (MV2c10) for \textit{b}-jet triggers in 2018 data-taking (blue solid line) is compared to the same \textit{b}-tagging algorithm trained on the Hybrid training sample (red solid line). [png] [pdf], [eps]

 The ATLAS \textit{b}-jet trigger uses a BDT algorithm to separate \textit{b}-jets from light and \textit{c}-jet backgrounds. The BDT algorithm used for \textit{b}-jet triggers in 2018 data taking has been trained on a $t\bar{t}$ Monte Carlo simulation. The same BDT algorithm has been trained on an alternative (Hybrid) training sample consisting of a mixture of $t\bar{t}$ and $Z'$ Monte Carlo samples, in the same way the BDT algorithm for offline jets is trained. Performance of \textit{b}-tagging algorithms (measured using $Z'$ Monte Carlo events), with respect to the true flavour of jets, is shown in terms of \textit{c}-jet rejection as a function of \textit{b}-jet efficiency. Expected performance of \textit{b}-tagging algorithm (MV2c10) for \textit{b}-jet triggers in 2018 data-taking (blue solid line) is compared to the same \textit{b}-tagging algorithm trained on the Hybrid training sample (red solid line). [png] [pdf], [eps]

## Performance of the ATLAS b-jet trigger in 2018 data at high pile-up ATL-COM-DAQ-2018-124 (August 2, 2018)

 The $b$-jet trigger efficiency with respect to the offline $b$-tagging algorithm (\texttt{MV2c10}) at the 70\% efficiency operating point for various online efficiency operating points vs. the mean number of interactions per crossing. The relative $b$-jet trigger efficiency is measured in high purity di-lepton $t\bar{t}$ events collected in the 2018 data-set using dedicated single-lepton$+$jets triggers, which are unbiased with respect to the online $b$-tagging. Operating point efficiencies are defined using offline-reconstructed jets from an unbiased sample of Monte Carlo simulated $t\bar{t}$ events, where jets are labeled according to their hadron content. The online operating points were defined to have roughly the quoted efficiency using online-reconstructed jets matched to offline-reconstructed jets. The same $b$-tagging algorithm is used offline and online and retuned for the online environment. Statistical uncertainties only are shown. [pdf], [eps]

 The $b$-jet trigger efficiency with respect to the offline $b$-tagging algorithm (\texttt{MV2c10}) at the 70\% efficiency operating point for various online efficiency operating points vs. the mean number of interactions per crossing. The relative $b$-jet trigger efficiency is measured in high purity di-lepton $t\bar{t}$ events collected in the 2018 data-set using dedicated single-lepton$+$jets triggers, which are unbiased with respect to the online $b$-tagging. Operating point efficiencies are defined using offline-reconstructed jets from an unbiased sample of Monte Carlo simulated $t\bar{t}$ events, where jets are labeled according to their hadron content. The online operating points were defined to have roughly the quoted efficiency using online-reconstructed jets matched to offline-reconstructed jets. The same $b$-tagging algorithm is used offline and online and retuned for the online environment. Statistical uncertainties only are shown. [pdf], [eps]

# 2017 @ 13 TeV

## Performance of the ATLAS b-jet trigger in 2017 data at high pile-up ATL-COM-DAQ-2017-182 (November 28, 2017)

 The $b$-jet trigger efficiency with respect to the offline $b$-tagging algorithm (\texttt{MV2c10}) at the 70\% efficiency operating point for various online efficiency operating points vs. the mean number of interactions per crossing. The relative $b$-jet trigger efficiency is measured in high purity di-lepton $t\bar{t}$ events collected in the 2017 data-set using dedicated single-lepton$+$jets triggers, which are unbiased with respect to the online $b$-tagging. The online operating points were defined to have roughly the quoted efficiency for $b$-jets in an unbiased sample of Monte Carlo simulated $t\bar{t}$ events. The uncertainty bars shown only represent statistical uncertainties. [pdf], [eps]

 The fraction of trigger jets with Global Sequential Calibration (GSC)-corrected $E_T > 55~\textrm{GeV}$ that pass the online $b$-tagging algorithm (\texttt{MV2c10}) at various online efficiency operating points vs. the mean number of interactions per crossing. The pass fraction is measured in a subset of the 2017 data-set with events containing at least one jet with GSC-corrected $E_T > 55~\textrm{GeV}$. The trigger used is unbiased with respect to the online $b$-tagging. The online operating points were defined to have roughly the quoted efficiency for $b$-jets in an unbiased sample of Monte Carlo simulated $t\bar{t}$ events. The uncertainty bars shown only represent statistical uncertainties. [pdf], [eps]

## Expected performance of the ATLAS b-jet trigger in 2017

 The ATLAS b-jet trigger uses a BDT algorithm to separate b-jets from light and c-jet backgrounds. The BDT algorithm is re-optimized to improve b-tagging performance. Performance of b-tagging algorithms (measured using ttbar Monte Carlo events) is shown in terms of light-jet rejection as a function of b-jet efficiency. Expected performance of b-tagging algorithm (MV2c10) for b-jet triggers in 2017 data-taking (green solid line) is compared to b-tagging algorithms used for b-jet triggers in 2016 (MV2c20) and 2015 (IP3D+SV1) data taking. Performance of b-tagging algorithm MV2c10 for offline jets is shown in purple dotted curve. [pdf], [eps]

 The ATLAS b-jet trigger uses a BDT algorithm to separate b-jets from light and c-jet backgrounds. The BDT algorithm is re-optimized to improve b-tagging performance. Performance of b-tagging algorithms (measured using ttbar Monte Carlo events) is shown in terms of c-jet rejection as a function of b-jet efficiency. Expected performance of b-tagging algorithm (MV2c10) for b-jet triggers in 2017 data-taking (green solid line) is compared to b-tagging algorithms used for b-jet triggers in 2016 (MV2c20). Performance of b-tagging algorithm MV2c10 for offline jets is shown in purple dotted curve. [pdf], [eps]

# 2016 @ 13 TeV

## Measurement of the ATLAS b-jet trigger efficiency in 2016 data ATL-COM-DAQ-2017-009 (February 8, 2017)

 The $b$-jet trigger efficiency at the 60\% online operating point with respect to offline $b$-tagging at the 70\% offline operating point as a function of offline jet-$p_{T}$. The $b$-jet trigger efficiency is measured using a high $b$-jet purity di-lepton $t\bar{t}$ selection in the 2016 data-set and $t\bar{t}$ simulation is used to extrapolate for jet-$p_{T} >$ 240 GeV. Events in data are required to have passed data quality selection for the $b$-jet trigger. Offline jets are required to match a trigger-level jet, where matching is done exclusively using $\Delta R$ matching. Systematics account for non $b$-jet contamination and the simulation-based extrapolation to high jet-$p_{T}$. [pdf], [eps]

# 2015 @ 13 TeV

## Impact Parameter Significance: Online vs Offline

 Transverse impact parameter significance for tracks associated to light-flavour (black) and b-quark (red) jets measured on a sample of simulated ttbar events. The solid lines show the distribution for the offline tracks. The points show the corresponding distribution for tracks used in the b-jet trigger. The impact parameter significance is defined as the impact parameter divided by the associated uncertainty. The impact parameters are signed such that track displacements in the direction of the jet have positive values, while tracks with displacements opposite of the jet direction are negative. The impact parameter significance is used to identify jets originating from decays of $b$-quarks. [pdf], [eps]

 Longitudinal impact parameter significance for tracks associated to light-flavour (black) and b-quark (red) jets measured on a sample of simulated ttbar events. The solid lines show the distribution for the offline tracks. The points show the corresponding distribution for tracks used in the b-jet trigger. The impact parameter significance is defined as the impact parameter divided by the associated uncertainty. The impact parameters are signed such that track displacements in the direction of the jet have positive values, while tracks with displacements opposite of the jet direction are negative. The impact parameter significance is used to identify jets originating from decays of $b$-quarks. [pdf], [eps]

## Trigger rates of the ATLAS b-jet trigger in Run 2

 Output rates of ATLAS multi-b-jet triggers as a function of the instantaneous luminosity during 2015 proton-proton data taking with a center-of-mass energy of 13 TeV and an LHC bunch-crossing interval of 50 ns. These triggers consist of hardware-based first-level (L1) and software-based high-level trigger (HLT) selections. At L1, one jet with a transverse energy (ET) > 100 GeV is required. At the HLT, either one b-tagged jet with ET> 175 GeV, 225 GeV or 300 GeV, or two b-tagged jets with E_T thresholds of 175 and 60 GeV are requested. The operating points, bloose' and bmedium' are optimized such that the efficiency for selecting b-jets is 79% and 72%, respectively. [pdf], [eps]

## Expected performance of the ATLAS b-jet trigger in Run 2

 The ATLAS b-jet trigger will use the same tools as are used for offline reconstruction to select heavy flavour jets during Run 2. This new tagger (MV2c20) uses a BDT to separate b-jets from light and c-jet backgrounds. The expected online performance in terms of light-jet rejection of the MV2c20 tagger (solid black line) is shown together with the expected performance of the IP3D+SV1 tagger in Run 2 (dashed blue line) and the actual performance of the IP3D+SV1 tagger that was achieved during Run 1 (red stars). The improvement in IP3D+SV1 for Run 2 compared to Run 1 is due to several effects: inclusion of the Insertable B-Layer (IBL), improved tracking performance and tagger re-tuning for Run 2 conditions. The b-jet trigger performance in Run 1 was also affected by overly conservative pixel cluster errors used by the tracking that caused a mis-measurement of the track d0 uncertainty. The tuning is performed on ttbar simulation with √s = 13 TeV. Jets used are required to have pT > 55 GeV and |η| < 2.5. The points illustrating the Run 1 performance were derived using ttbar simulation with √s =8 TeV. [pdf], [eps]

 The ATLAS b-jet trigger will use the same tools as are used for offline reconstruction to select heavy flavour jets during Run 2. This new tagger (MV2c20) uses a BDT to separate b-jets from light and c-jet backgrounds. The expected online performance in terms of c-jet rejection of the MV2c20 tagger (solid black line) is shown together with the expected performance of the IP3D+SV1 tagger in Run 2 (dashed blue line). The tuning is performed on ttbar simulation with √s = 13 TeV. Jets used are required to have pT > 55 GeV and |η| < 2.5. [pdf], [eps]

# 2012 @ 8 TeV

## L2 and EF Trigger b-tagging weights in jets containing D* mesons for 2012 data ATL-COM-PHYS-2014-631 (June 26, 2014)

 Comparison between the L2 Trigger $b$-tagging weight distribution on a background-subtracted jets, containing $D^{*+}$ mesons, data sample with the corresponding simulated PYTHIA sample. The statistical uncertainty of the simulation is below a few percent and not shown in this figure. The data sample is collected in 2012 using single jet triggers, and the transverse momentum of the selected jets is required to be above 20 GeV. The beauty to charm jet fraction in the simulation is constrained to the value obtained by a pseudo-proper time fit on data for the $D^0$ mesons arising from the $D^{*+}$ decays. More complete description of the method is available in \href{https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/CONFNOTES/ATLAS-CONF-2012-039/}{ATLAS-CONF-2012-039}. [pdf], [eps]

 Comparison between the EF Trigger $b$-tagging weight distribution on a background-subtracted jets, containing $D^{*+}$ mesons, data sample with the corresponding simulated PYTHIA sample. The statistical uncertainty of the simulation is below a few percent and not shown in this figure. The data sample is collected in 2012 using single jet triggers, and the transverse momentum of the selected jets is required to be above 20 GeV. The beauty to charm jet fraction in the simulation is constrained to the value obtained by a pseudo-proper time fit on data for the $D^0$ mesons arising from the $D^{*+}$ decays. More complete description of the method is available in \href{https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/CONFNOTES/ATLAS-CONF-2012-039/}{ATLAS-CONF-2012-039}. [pdf], [eps]

## b-jet tagger data to simulation comparison using 8 TeV data ATL-COM-DAQ-2013-018 (May 9, 2013)

 Jet weight distribution for the tagger based on the combination of the impact parameter significance and the secondary vertex likelihood-based taggers, calculated from prescaled Level 2 tracks in Level 2 jets with $p_T > 50$ GeV and $|\eta| < 2.5$. Only statistical errors are shown. [pdf], [eps]

 Jet weight distribution for the tagger based on the combination of the impact parameter significance and the secondary vertex likelihood-based taggers, calculated from prescaled Event Filter tracks in Event Filter jets with $p_T > 55$ GeV and $|\eta| < 2.5$. Only statistical errors are shown. [pdf], [eps]

 Jet weight distribution for the tagger based on the combination of the impact parameter significance and the secondary vertex likelihood-based taggers, calculated from prescaled Level 2 tracks in Level 2 jets with $p_T > 50$ GeV and $|\eta| < 2.5$ matched to a muon with $p_T > 4$ GeV to enhance the b-jet component. Only statistical errors are shown. [pdf], [eps]

 Jet weight distribution for the tagger based on the combination of the impact parameter significance and the secondary vertex likelihood-based taggers, calculated from Event Filter tracks in Event Filter jets with $p_T > 55$ GeV and $|\eta| < 2.5$ matched to a muon with $p_T > 4$ GeV to enhance the b-jet component. Only statistical errors are shown. [pdf], [eps]

 Jet weight distribution for the likelihood-ratio tagger based on the longitudinal and transverse impact parameter significance [\href{https://cds.cern.ch/record/1369219/files/ATLAS-CONF-2011-102.pdf}{ATLAS-CONF-2011-102}] of prescaled Level 2 tracks in Level 2 jets with $p_T > 50$ GeV and $|\eta| < 2.5$. Only statistical errors are shown. [pdf], [eps]

 Jet weight distribution for the likelihood-ratio tagger based on the longitudinal and transverse impact parameter significance [\href{https://cds.cern.ch/record/1369219/files/ATLAS-CONF-2011-102.pdf}{ATLAS-CONF-2011-102}] of prescaled Event Filter tracks in Event Filter jets with $p_T > 55$ GeV and $|\eta| < 2.5$. Only statistical errors are shown. [pdf], [eps]

 Jet weight distribution for the likelihood-ratio tagger based on the longitudinal and transverse impact parameter significance [\href{https://cds.cern.ch/record/1369219/files/ATLAS-CONF-2011-102.pdf}{ATLAS-CONF-2011-102}] of prescaled Level 2 tracks in Level 2 jets with $p_T > 50$ GeV and $|\eta| < 2.5$ matched to a muon with $p_T > 4$ GeV to enhance the b-jet component. Only statistical errors are shown. [pdf], [eps]

 Jet weight distribution for the likelihood-ratio tagger based on the longitudinal and transverse impact parameter significance [\href{https://cds.cern.ch/record/1369219/files/ATLAS-CONF-2011-102.pdf}{ATLAS-CONF-2011-102}] of prescaled Event Filter tracks in Event Filter jets with $p_T > 55$ GeV and $|\eta| < 2.5$ matched to a muon with $p_T > 4$ GeV to enhance the b-jet component. Only statistical errors are shown. [pdf], [eps]

 Ratio of energy sum of quality tracks associated with the prescaled Level 2 jets' secondary vertex and the energy sum of all quality tracks in the jet for Level 2 jets with $p_T > 50$ GeV and $|\eta| < 2.5$. Only statistical errors are shown. [pdf], [eps]

 Ratio of energy sum of quality tracks associated with the prescaled Event Filter jets' secondary vertex and the energy sum of all quality tracks in the jet for Event Filter jets with $p_T > 55$ GeV and $|\eta| < 2.5$. Only statistical errors are shown. [pdf], [eps]

 Ratio of energy sum of quality tracks associated with the prescaled Level 2 jets' secondary vertex and the energy sum of all quality tracks in the jet for Level 2 jets with $p_T > 50$ GeV and $|\eta| < 2.5$ matched to a muon with $p_T > 4$ GeV to enhance the b-jet component. Only statistical errors are shown. [pdf], [eps]

 Ratio of energy sum of quality tracks associated with the prescaled Event Filter jets' secondary vertex and the energy sum of all quality tracks in the jet for Event Filter jets with $p_T > 55$ GeV and $|\eta| < 2.5$ matched to a muon with $p_T > 4$ GeV to enhance the b-jet component. Only statistical errors are shown. [pdf], [eps]

 Track multiplicity at the prescaled Level 2 jets' secondary vertex for Level 2 jets with $p_T > 50$ GeV and $|\eta| < 2.5$. Only statistical errors are shown. [pdf], [eps]

 Track multiplicity at the prescaled Event Filter jets' secondary vertex for Event Filter jets with $p_T > 55$ GeV and $|\eta| < 2.5$. Only statistical errors are shown. [pdf], [eps]

 Track multiplicity at the prescaled Level 2 jets' secondary vertex for Level 2 jets with $p_T > 50$ GeV and $|\eta| < 2.5$ matched to a muon with $p_T > 4$ GeV to enhance the b-jet component. Only statistical errors are shown. [pdf], [eps]

 Track multiplicity at the prescaled Event Filter jets' secondary vertex for Event Filter jets with $p_T > 55$ GeV and $|\eta| < 2.5$ matched to a muon with $p_T > 4$ GeV to enhance the b-jet component. Only statistical errors are shown. [pdf], [eps]

 Invariant mass at the prescaled Level 2 jets' secondary vertex for Level 2 jets with $p_T > 50$ GeV and $|\eta| < 2.5$. Only statistical errors are shown. [pdf], [eps]

 Invariant mass at the prescaled Event Filter jets' secondary vertex for Event Filter jets with $p_T > 55$ GeV and $|\eta| < 2.5$. Only statistical errors are shown. [pdf], [eps]

 Invariant mass at the prescaled Level 2 jets' secondary vertex for Level 2 jets with $p_T > 50$ GeV and $|\eta| < 2.5$ matched to a muon with $p_T > 4$ GeV to enhance the b-jet component. Only statistical errors are shown. [pdf], [eps]

 Invariant mass at the Event Filter jets' secondary vertex for Event Filter jets with $p_T > 55$ GeV and $|\eta| < 2.5$ matched to a muon with $p_T > 4$ GeV to enhance the b-jet component. Only statistical errors are shown. [pdf], [eps]

# 2011 @ 7 TeV

## b-Jet triggering in ATLAS ATL-COM-DAQ-2012-050 (May 16, 2012)

 Resolution for the primary vertex Z position estimate as a function of the number of online tracks at L2 and EF. The z coordinate of the primary vertex (PV) is calculated by histogramming the $z_0$ of all selected tracks in the event and using a sliding window algorithm to select the largest local maximum. [pdf], [eps]

## b-Jet Trigger rate plots with 2011 data ATL-COM-DAQ-2011-067 (August 25, 2011)

 Trigger rate for 1b/4j topology. LVL1, LVL2 and EF rate of a b-jet trigger requiring at least four jets in the event and at least one b-tagged jet. The jet thresholds correspond to 10, 25 and 30 GeV at LVL1, LVL2 and EF with energies measured at the electromagnetic scale. The b-jet requirement is applied at LVL2 and EF and is tuned to give 70\% efficiency on a b-tagged jet sample using top MC simulation. [pdf], [eps]

 Trigger rate for 2b/4j topology. LVL1, LVL2 and EF rate of a b-jet trigger requiring at least four jets in the event and at least two b-tagged jet. The jet thresholds correspond to 10, 25 and 30 GeV at LVL1, LVL2 and EF with energies measured at the electromagnetic scale. The b-jet requirement is applied at LVL2 and EF is tuned to give 70\% efficiency on a b-tagged jet sample using top MC simulation. [pdf], [eps]

 Trigger rate for 2b/2j topology. LVL1, LVL2 and EF rate of a b-jet trigger requiring at least two jets in the event and at least one b-tagged jet. The jet thresholds are asymmetric and correspond to 50(10), 70(25) and 75(30) GeV at LVL1, LVL2 and EF for the leading (second leading) jet with energies measured at the electromagnetic scale. The b-jet requirement is applied at LVL2 and EF and is tuned to give 70\% efficiency on a b-tagged jet sample using top MC simulation. [pdf], [eps]

## b-Jet Trigger plots with 2011 data ATL-COM-DAQ-2011-052 (July 19, 2011)

 Signed transverse impact parameter significance of reconstructed tracks at the Event Filter level. Tracks are reconstructed starting from a low pT jet identified by the Level 1 and are requested to fulfill online b-tagging criteria. [pdf], [eps]

 Track multiplicity at the Event Filter level. Tracks are reconstructed starting from a low pT jet identified by the Level 1 and are requested to fulfill online b-tagging criteria. [pdf], [eps]

 Track transverse momentum at the Event Filter level. Tracks are reconstructed starting from a low pT jet identified by the Level 1 and are requested to fulfill online b-tagging criteria. [pdf], [eps]

 Offline JetProb distribution in data and simulation and the same distribution in data when a b-jet requirement is added at the second trigger level. The three working points correspond to 90%, 70% and 50% b-tagging efficiency on a b-tagged jet sample using top MC simulation. [pdf], [eps]

 Offline JetProb distribution in data and simulation and the same distribution in data when a b-jet requirement is added at the trigger level (both Level 2 and Event Filter). The three working points correspond to 90%, 70% and 50% b-tagging efficiency on a b-tagged jet sample using top MC simulation. [pdf], [eps]

 muon-in-jet trigger distribution as a function of the jet transverse momentum in offline muon-in-jet candidates. This class of triggers includes a geometrical matching (Delta R < 0.4) between the muon and the jet objects. Several triggers with various jet thresholds select events in order to collect a sample of muon-in-jet candidates in the entire jet transverse momentum spectrum. [pdf], [eps]

# 2010 @ 7 TeV

## b-Jet Trigger dependence on the Beam Spot Determination in 2010 Data ATL-COM-DAQ-2010-197 (November 4, 2010)

 Probability for the LVL2 tracks to originate from primary vertex before and after the HLT beam spot update. The probability for a track to originate from the primary vertex is computed on the signed transverse impact parameter significance of each selected track in the jet RoI using the resolution function for prompt tracks. A jet probability is then defined by considering the probabilities of all the selected tracks. By construction, it ensures a uniform distribution between 0 and 1 for tracks originating from the primary vertex while tracks from displaced B decays tend to accumulate near 0. This plot demonstrates the usefulness of having the beam spot parameters accessible within the HLT farm in real time since the b-jet triggers rely on the beam spot parameters to compute the track transverse impact parameter significance. Before the update the beam spot position w.r.t. the real position is 45um (170um) away in x (y) while after the update the position is 5um (10um). [pdf], [eps], [jpg]

# FTK Public Results

 Some plots related to the B-jet trigger are included in the FTK public results: https://twiki.cern.ch/twiki/bin/view/AtlasPublic/FTKPublicResults

-- AndreaCoccaro - 06-Jun-2011

Responsible: AndreaCoccaro
Subject: public

Topic attachments
I Attachment History Action Size Date Who Comment
eps BJetTrigg_efficiency_vs_mu-2018.eps r1 manage 99.4 K 2018-08-02 - 14:10 StephenSekula
pdf BJetTrigg_efficiency_vs_mu-2018.pdf r1 manage 23.2 K 2018-08-02 - 14:09 StephenSekula
png BJetTrigg_efficiency_vs_mu-2018.png r1 manage 114.2 K 2018-08-02 - 14:09 StephenSekula
eps BJetTrigg_efficiency_vs_mu-yzoom-2018.eps r1 manage 103.2 K 2018-08-02 - 14:10 StephenSekula
pdf BJetTrigg_efficiency_vs_mu-yzoom-2018.pdf r1 manage 24.0 K 2018-08-02 - 14:10 StephenSekula
png BJetTrigg_efficiency_vs_mu-yzoom-2018.png r1 manage 120.5 K 2018-08-02 - 14:10 StephenSekula
eps BJetTrigg_efficiency_vs_mu.eps r1 manage 21.4 K 2017-12-11 - 22:00 MatthewFeickert The plots from ATL-COM-DAQ-2017-182
pdf BJetTrigg_efficiency_vs_mu.pdf r1 manage 20.0 K 2017-12-11 - 22:00 MatthewFeickert The plots from ATL-COM-DAQ-2017-182
png BJetTrigg_efficiency_vs_mu.png r1 manage 24.6 K 2017-12-11 - 22:00 MatthewFeickert The plots from ATL-COM-DAQ-2017-182
eps BJetTrigg_pass_fraction_vs_mu.eps r1 manage 22.9 K 2017-12-11 - 22:00 MatthewFeickert The plots from ATL-COM-DAQ-2017-182
pdf BJetTrigg_pass_fraction_vs_mu.pdf r1 manage 21.3 K 2017-12-11 - 22:00 MatthewFeickert The plots from ATL-COM-DAQ-2017-182
png BJetTrigg_pass_fraction_vs_mu.png r1 manage 24.0 K 2017-12-11 - 22:00 MatthewFeickert The plots from ATL-COM-DAQ-2017-182
eps HLT_bjet_for_pub.eps r1 manage 11.9 K 2015-09-21 - 16:20 LidijaZivkovic b jet trigger rates
pdf HLT_bjet_for_pub.pdf r1 manage 22.5 K 2015-09-21 - 16:20 LidijaZivkovic b jet trigger rates
png HLT_bjet_for_pub.png r1 manage 23.2 K 2015-09-21 - 16:20 LidijaZivkovic b jet trigger rates
eps ROC_cb.eps r1 manage 22.0 K 2019-05-02 - 10:27 CarloVarni Hybrid tuning performances in 2018
pdf ROC_cb.pdf r1 manage 27.9 K 2019-05-02 - 10:27 CarloVarni Hybrid tuning performances in 2018
png ROC_cb.png r1 manage 102.3 K 2019-05-02 - 10:27 CarloVarni Hybrid tuning performances in 2018
eps ROC_cb_Zprime.eps r1 manage 23.3 K 2019-05-02 - 10:46 CarloVarni Hybrid tuning performances in 2018
pdf ROC_cb_Zprime.pdf r1 manage 27.0 K 2019-05-02 - 10:46 CarloVarni Hybrid tuning performances in 2018
png ROC_cb_Zprime.png r1 manage 93.8 K 2019-05-02 - 10:46 CarloVarni Hybrid tuning performances in 2018
eps ROC_lb.eps r1 manage 24.2 K 2019-05-02 - 10:27 CarloVarni Hybrid tuning performances in 2018
pdf ROC_lb.pdf r1 manage 28.2 K 2019-05-02 - 10:27 CarloVarni Hybrid tuning performances in 2018
png ROC_lb.png r1 manage 93.7 K 2019-05-02 - 10:27 CarloVarni Hybrid tuning performances in 2018
eps ROC_lb_Zprime.eps r1 manage 24.9 K 2019-05-02 - 10:46 CarloVarni Hybrid tuning performances in 2018
pdf ROC_lb_Zprime.pdf r1 manage 27.4 K 2019-05-02 - 10:46 CarloVarni Hybrid tuning performances in 2018
png ROC_lb_Zprime.png r1 manage 110.5 K 2019-05-02 - 10:46 CarloVarni Hybrid tuning performances in 2018
eps bJetTriggerPerf.eps r1 manage 16.7 K 2015-07-17 - 14:18 KatharineLeney
pdf bJetTriggerPerf.pdf r1 manage 58.2 K 2015-07-17 - 14:18 KatharineLeney
png bJetTriggerPerf.png r1 manage 179.6 K 2015-07-17 - 14:25 KatharineLeney
eps bJetTriggerPerf_cRej.eps r1 manage 15.4 K 2015-07-17 - 14:18 KatharineLeney
pdf bJetTriggerPerf_cRej.pdf r1 manage 56.1 K 2015-07-17 - 14:18 KatharineLeney
png bJetTriggerPerf_cRej.png r1 manage 153.0 K 2015-07-17 - 14:25 KatharineLeney
eps charm.eps r1 manage 23.8 K 2017-07-04 - 10:17 RuchiGupta bjet Trigger Expected Performance in 2017
pdf charm.pdf r1 manage 34.2 K 2017-07-04 - 10:17 RuchiGupta bjet Trigger Expected Performance in 2017
png charm.png r1 manage 72.0 K 2017-07-04 - 10:17 RuchiGupta bjet Trigger Expected Performance in 2017
pdf effSyst_offJets70_match_hlt60_jetPt_00-02-00.pdf r1 manage 14.5 K 2017-02-14 - 21:56 LidijaZivkovic efficiency for 2016
png effSyst_offJets70_match_hlt60_jetPt_00-02-00.png r1 manage 21.0 K 2017-02-14 - 21:55 LidijaZivkovic efficiency for 2016
eps light.eps r2 r1 manage 28.0 K 2017-07-04 - 10:21 RuchiGupta
pdf light.pdf r2 r1 manage 35.3 K 2017-07-04 - 10:21 RuchiGupta
png light.png r2 r1 manage 49.2 K 2017-07-04 - 10:21 RuchiGupta
Topic revision: r21 - 2019-05-14 - CarloVarni

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