The challenging environment in which the Large Hadron Collider (LHC) experiments are going to operate implies a sophisticated trigger system, capable of real-time track and vertex reconstruction. In the ATLAS experiment, the first selection stage where these ingredients are available is the software-based High-Level Trigger (HLT), which reduces its 75 kHz input to ~200 Hz in two subsequent steps: the LVL2 and the Event Filter (EF) triggers. In this contribution we present algorithms for fast reconstruction of charged tracks and vertexing in the HLT framework, including common extrapolation and fitting tools. Their application to different trigger selections and in particular to b-jet selections, used to improve the flexibility of the trigger scheme and extend its physics performance, is also discussed. Examples of performance of the presented algorithms on simulation and cosmic-ray data are given. Efficient and robust tracking capabilities are demonstrated to be achievable with average execution times well within the trigger requirements

Coccaro, A

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ATL-DAQ-SLIDE-2009-25611September2009Usage of vertex detectors in theATLAS trigger softwareVertex 2009 - Putten (NL) - September 18, 2009Andrea CoccaroUniversity of Genoa / INFNon behalf of the ATLAS collaborationAndrea Coccaro 1September 18, 2009OutlineIntroductionTracking and vertexingCosmic data-takingConclusionsAndrea Coccaro 2September 18, 2009Introduction Tracking and vertexing Cosmic data-taking ConclusionsThe ATLAS ApparatusAndrea Coccaro 3September 18, 2009Introduction Tracking and vertexing Cosmic data-taking ConclusionsThe ATLAS Tracking SystemInner Detector (ID)The ATLAS experiment is equipped with thefollowing tracking subsystems:I Pixel detector:3 layers and 3 disks of Si pixels;pixel size 50× 400 µm2;maximum distance from beam: ∼ 12 cm.I SemiConductor Tracker (SCT):4 layers and 9 disks of stereo Si strips;strip size 80 µm;maximum distance from beam: ∼ 50 cm.I Transition Radiation Tracker (TRT):straw drift tubes with diameter 4 mm;30 µm sense wire;maximum distance from beam: ∼ 1 m.Andrea Coccaro 4September 18, 2009Introduction Tracking and vertexing Cosmic data-taking ConclusionsThe ATLAS Tracking SystemInner Detector (ID)Andrea Coccaro 5September 18, 2009Introduction Tracking and vertexing Cosmic data-taking ConclusionsThe ATLAS TriggerConceptual designLHC interaction rate is reduced through threesubsequent selection steps:Andrea Coccaro 6September 18, 2009Introduction Tracking and vertexing Cosmic data-taking ConclusionsThe ATLAS TriggerConceptual designLHC interaction rate is reduced through threesubsequent selection steps:Level1 Trigger (LVL1):I hardware based;I latency 2 µs;I input/output rate: from 40 MHz to 75 KHz;I coarse granularity calo and muon data.I regions of interest (RoIs) are selected by LVL1Trigger and passed to the following selectionstep in order to minimize processing time andnetwork traffic.Andrea Coccaro 7September 18, 2009Introduction Tracking and vertexing Cosmic data-taking ConclusionsThe ATLAS TriggerConceptual designLHC interaction rate is reduced through threesubsequent selection steps:Level1 Trigger (LVL1):I hardware based;I latency 2 µs;I input/output rate: from 40 MHz to 75 KHz;I coarse granularity calo and muon data.I regions of interest (RoIs) are selected by LVL1Trigger and passed to the following selectionstep in order to minimize processing time andnetwork traffic.Andrea Coccaro 8September 18, 2009Introduction Tracking and vertexing Cosmic data-taking ConclusionsThe ATLAS TriggerConceptual designLHC interaction rate is reduced through threesubsequent selection steps:High Level Trigger (LVL2+EF):I software based;I full granularity for all subdetectorsLevel2 Trigger (LVL2):I average execution time ∼ 40 msI input/output rate: from 75 KHz to 3 KHz;I RoI driven from LVL1;Event Filter (EF):I average execution time ∼ 4 sI input/output rate: from 3 KHz to ∼ 200 HzI off-line quality algorithms;I data storage: ∼ 300 MB/s.Andrea Coccaro 9September 18, 2009Introduction Tracking and vertexing Cosmic data-taking ConclusionsLVL2 Tracking AlgorithmsOverviewIn the trigger selection, LVL2 is the earliest stage where tracking information can beused.The tight constraint on the mean execution time forces algorithm development to avery delicate balance between time consumption and performance.LVL2 tracking is used for the definition of the following trigger items:I selection of high-pT electrons and muons: tracks reconstructed in the ID areused to match information from the calorimeters and the muon detectors;I reconstruction of tracks from tau decays: tracks are used for both matchinginformation from outer detectors and to apply cuts on track multiplicity;I b-jet tagging: the impact parameters of the reconstructed tracks are used toevaluate the discriminant variables for identifying jet flavor;I B physics: identifying specific B-physics decay channels by using decay vertexreconstruction, mass cuts etc.Andrea Coccaro 10September 18, 2009Introduction Tracking and vertexing Cosmic data-taking ConclusionsLVL2 Tracking AlgorithmsTRT Segment FinderTRT Segment Finder is a TRT-only algorithm with separate implimentations forcosmic running and collisions. It is a wrapped version of the oﬄine tracking and it hasbeen extensively used during cosmic data-taking periods.IDScanIDScan is a silicon hit based algorithm that exploits a space point histogrammingmethod.The determination of the z position of the interaction point along the beam axis is thefirst mandatory step, needed to identify clusters of hits in η-φ space. Then, groups ofhits from these clusters that are consistent in 1/pT -φ space are passed to the trackfitter.SiTrackSiTrack is also a silicon hit based algorithm that implements a combinatorial approachto match space points in order to form full tracks.It looks for seed pairs of hits in the inner layers consistent with beam-line constraints.Couples are then combined and extended with space points from outer layers, ifmatching cuts are opportunely satisfied. Groups of hits consistent with single tracksare merged and passed to the track fitter.Andrea Coccaro 11September 18, 2009Introduction Tracking and vertexing Cosmic data-taking ConclusionsLVL2 Tracking ToolsFit toolsFitting is used to evaluate the parameters of thereconstructed track candidates. All the tools sharea common interface and switching between themcan be easily done via configuration.Andrea Coccaro 12September 18, 2009Introduction Tracking and vertexing Cosmic data-taking ConclusionsLVL2 Tracking ToolsFit toolsFitting is used to evaluate the parameters of thereconstructed track candidates. All the tools sharea common interface and switching between themcan be easily done via configuration.Extension toolsTracks in the silicon detectors can be extrapolatedinto the TRT to provide improved pT resolution.A TRT extension tool, designed to be particularlyrobust given the high occupancy of the detector (upto 50%), is available for operations:I single-pass recursive algorithm with almostlinear execution time;I consists of two main blocks: hit associationand track update.Andrea Coccaro 13September 18, 2009Introduction Tracking and vertexing Cosmic data-taking ConclusionsEF Tracking AlgorithmsOverviewThe EF trigger selection software is based on the idea of reusing as much as possiblethe code developed for off-line reconstruction.To cope with the limited time budget available at the trigger level, some adaptationsare mandatory; among them:I off-line code is executed within wrapper algorithms, which allow the algorithmsto be run multiple times per event processing only the data in the RoIs. This is amajor difference w.r.t. the off-line framework where reconstruction algorithmscan access data from the entire event;I some tuning of the algorithm parameters can be performed to reduce thenumber of iterations and consequent processing time.Andrea Coccaro 14September 18, 2009Introduction Tracking and vertexing Cosmic data-taking ConclusionsHLT VertexingOverviewI vertexing tools are implemented in the HLT framework and available foroperations.Vertexing for b-tagging:I primary vertex reconstruction improves the impact parameter resolution for thereconstructed tracks, consequently improving the discriminant power of theimpact parameter based variables;I reconstruction of secondary vertices enables to implement additional discriminantvariables which are uncorrelated with the ones based on impact parameters (jetmultiplicity, invariant mass and fraction of jet energy associated to the secondaryvertex particles).Vertexing for B-physics:I secondary vertex reconstruction is used to reject fake combinations which don’tcorrespond to a common vertex;I refit of the tracks associated to a given vertex is used to refine the invariantmass constraints applied;Andrea Coccaro 15September 18, 2009Introduction Tracking and vertexing Cosmic data-taking ConclusionsHLT b-taggingOverviewTracking and vertexing are the main ingredients for the online b-jet selection, used toimprove the flexibility of the trigger scheme and extend its physics performance.b-jet tagging can be used to lower the jet trigger thresholds for events containingmore than one b-jet in their final states.Different approaches studied and implemented to ensure trigger flexibility androbustness wrt different working conditions.Selection strategies1. likelihood-ratio approach based on the impact paramter significances (powerfulbut it needs impact parameter distribution of a pure b-jet sample, not easilyaccessible from real data);2. probability for a jet to originate from the primary vertex (more robust than thelikelihood-based approach since it relies on the transverse impact parameterdistribution of prompt tracks in multi-jet events);3. other taggers based on secondary vertex reconstruction are being developed andstudied.Andrea Coccaro 16September 18, 2009Introduction Tracking and vertexing Cosmic data-taking ConclusionsHLT b-taggingLikelihood ratio methodImpact parameter distributionfor tracks coming from b-jets(solid line) and u-jets (dashedline)Likelihood-ratio variable distri-bution for b-jets and u-jetsAndrea Coccaro 17September 18, 2009Introduction Tracking and vertexing Cosmic data-taking ConclusionsHLT b-taggingPerformance on tt MC sampleAndrea Coccaro 18September 18, 2009Introduction Tracking and vertexing Cosmic data-taking ConclusionsHigh-Level Trigger CommissioningOverviewIn order to be ready for collisions, the High-Level Trigger system needs to becommissioned (event steering, event streaming, monitoring framework, algorithmtiming and performance in real conditions, ...).ID Tracking algorithms commissioning:I cosmic muons- real detector alignment, noise occupancy and data preparation;- different event topology wrt collisions.I pass-through triggers to study algorithm efficiency:- LVL1 from muon and calorimeter detectors;- HLT without rejecting events;- HLT running algorithms and streaming events based on LVL1 and HLTinformations.Andrea Coccaro 19September 18, 2009Introduction Tracking and vertexing Cosmic data-taking ConclusionsCosmics data-takingOverviewI ∼ 200 million cosmic events collected by ATLAS during combined running fromSeptember to December ’08;I another ATLAS combined cosmic data-taking in June ’09;I all LVL1 signatures with a confirmed reconstructed LVL2 track fed into adedicated ID cosmic stream;I possible to collect high momentum tracks passing through the ID andilluminating as much as possible the detector barrels (needed to align the ID).Andrea Coccaro 20September 18, 2009Introduction Tracking and vertexing Cosmic data-taking ConclusionsCosmics data-takingSilicon tracking algorithms configurationSilicon tracking algorithms deal in a different way the typically large impact parameterof cosmic tracks.I IDScan uses collision tuning with a preprocessing stage where space points areshifted as orginating from the origin of the ATLAS coordinate frame (trackingperformance is biased by efficiency of the space-points shift);I SiTrack runs without any ad-hoc shift and uses a dedicated tuning with relaxedtrack-pointing constraints (fake tracks are overestimated if compared toperformance with collision mode).Andrea Coccaro 21September 18, 2009Introduction Tracking and vertexing Cosmic data-taking ConclusionsHLT Tracking performance on cosmicsLVL2 event reconstruction efficiency as a function of impact parameter d0I efficiency is defined w.r.t. an eventwith an off-line track;I off-line track is required to have atleast 3 silicon space points in theupper and 3 in the lower part of thesilicon barrel;I TRT readout time window in[−10, 25] ns is required.offline track d0 [mm]-400 -300 -200 -100 0 100 200 300 400L2 event trigger efficiency0.50.60.70.80.911.1ATLAS preliminaryAny L2 ID trackSiTrackIDScanTRTAndrea Coccaro 22September 18, 2009Introduction Tracking and vertexing Cosmic data-taking ConclusionsHLT Tracking performance on cosmicsLVL2 event reconstruction efficiency as a function of track transverse momentum pTI efficiency is defined w.r.t. an eventwith an off-line track;I off-line track is required to have atleast 3 silicon space points in theupper and 3 in the lower part of thesilicon barrel;I TRT readout time window in[−10, 25] ns is required;I |d0| < 200 mm is required;I low efficiency for IDScan at thelowest-PT bin is due to thespacepoint shifter used before thepattern recognition which works withlow efficiency for low momentumtracks. [GeV]TOffline track p0 20 40 60 80 100L2 event trigger efficiency0.50.60.70.80.911.1ATLAS preliminaryAny L2 ID trackSiTrackIDScanTRTAndrea Coccaro 23September 18, 2009Introduction Tracking and vertexing Cosmic data-taking ConclusionsHLT Tracking performance on cosmicsEF track reconstruction efficiency as a function of impact parameter d0I efficiency is defined w.r.t. an event with an off-line track;I loose (tight) selection: 18k (450) tracks, eff. 83.0% (100%).d0 [mm]-600 -400 -200 0 200 400 600EF Efficiency (Loose tracks)00.20.40.60.811.2ATLAS PreliminarySolenoid Ond0 [mm]-60 -40 -20 0 20 40 60EF Efficiency (Tight tracks)0.80.850.90.9511.051.1ATLAS PreliminarySolenoid OnAndrea Coccaro 24September 18, 2009Introduction Tracking and vertexing Cosmic data-taking ConclusionsSummary and outlookI the ATLAS tracking and vertexing trigger is based on mature software which hasbeen well studied and validated on simulated collisions data samples and in realcosmic data-taking;I ID trigger has been actively operated during cosmic running and LVL2 trackingalgorithms have been used to stream cosmic events;I tracking efficiencies have been characterized wrt oﬄine tracking on real cosmicdata, showing very good performance;I more detailed performance studies on cosmic data are still ongoing in the ATLAScommunity;I application to HLT b-tagging has been showed, final commission will be possibleonly with real collisions;I LHC operations will restart this autumn and first collisions are anticipated beforethe end of the year. ID trigger will be ready for first beam, emphasis will be onstable and safe running.Andrea Coccaro 25September 18, 2009

Usage of vertex detectors in the ATLAS trigger software

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