Real Time 2010Monika Wielers (RAL)1 ATLAS e/  /  /jet/E T miss High Level Trigger Algorithms Performance with first LHC collisions Monika Wielers (RAL) on behalf of the ATLAS Collaboration Real Time 2010

Monika Wielers (RAL)2 Introduction Outline Performance of the ATLAS High Level Trigger (HLT=L2 + EF) Electrons and photons Taus Jets E T miss Next commissioning steps Conclusions Will show plots from  s = 900 GeV (~9  b -1 of stable beam data) and  s= 7 TeV collisions (so far >10 nb -1 recorded) Much more 7 TeV collisions data available compared to 900 GeV data. Access to much higher E T values in 7 TeV data Commissioning done currently by running in pass-through mode at HLT starting from low-E T L1 triggers We mainly see ‘fakes’ right now rather than the signals we are ultimately interested in

Real Time 2010Monika Wielers (RAL)3 Electrons and Photons: Overview e/  selection key for many physics analyses J/ψ, B physics  low-p T e  ’s [ 5 – 20 GeV] W, Z, top, Higgs, SUSY, prompt   medium p T e  ’s and  ’s [ 20 – 100 GeV] G, Z’  high p T e  ’s and  ’s [p T >100 GeV] Processing steps Starting point: L1 EM region of interests (RoI) clustering: a bit simplified compared to offline tracking: 3 fast pattern recognition algorithms being evaluated online Use of calo shapes and cluster-track matching variables in selection Use of offline algorithms Run clustering and tracking algorithms Due to timing constraints: no conversion finding, no brem recovery Use of calo shapes and cluster-track matching variables in selection HLT and offline use same variables for signal identification L2 EF

Real Time 2010Monika Wielers (RAL)4 W  e candidate as seen by the trigger aa Electron candidate L1 tower E T in  space As seen by L2As seen by offline p T (e + )=34 GeV  (e + ) = E T miss = 26 GeV M T = 57 GeV

Real Time 2010Monika Wielers (RAL)5 Electrons and Photons: Performance Good trigger performance can be evaluated in terms of Small resolution between trigger and offline selection variables Agreement between data and MC for the selection variable distributions Example: Shower shape in 2 nd EM layer R  =E(3  7)/E(7  7) (cell units, one unit is  =0.025  0.025) Good agreement between trigger and offline found for resolution Data and MC agree reasonably well As selection cuts were derived from MC for start-up a reasonable agreement gives confidence our selection will work online R  distributions for trigger objects matched to offline e  candidates

Real Time 2010Monika Wielers (RAL)6 Tau HLT Performance: Overview Tau’s are key signature for W, Z SM processes H  hh, heavy Higgs Susy searches with a light tau slepton ~65% of  ’s decay hadronically in 1- or 3-prongs (    ,     +n  0 or    3  ,    3   +n  0 ) Requires dedicated trigger looking for “Narrow” jet in calorimeter 1 or 3 associated tracks in tracking detector Identification based on jet isolation, jet narrowness and track multiplicity HLT processing steps similar to electrons Starting point: L1 tau RoI

Real Time 2010Monika Wielers (RAL)7 Tau HLT Performance in 900 GeV collisions Examples: L2 E T spectrum EF EM and hadronic radius (measurement of shower size in  -  :  E cell  R 2 cell /  E cell in  =0.1 x 0.1) Expect small values for tau’s Reasonable agreement between data - MC Gives us confidence that the selections optimised on MC will work!

Real Time 2010Monika Wielers (RAL)8 Jet Performance: Overview Physics Motivation Jet cross section Susy Black hole searches HLT processing Starts from a L1 jet RoI Iterative cone algorithm with R<0.4 at L2 Cone jet algorithm with R<0.7 at EF (use of offline algorithm) Note, other jet algorithms under evaluation

Real Time 2010Monika Wielers (RAL)9 Jets: HLT Performance in 900 GeV collisions Relative energy resolution between L2 and offline jets at EM scale Good agreement between data and MC Small shift in peak position arises from different jet finder used in HLT and offl. Good agreement between data and MC simulations also seen in  -resolution at L2 and EF

Real Time 2010Monika Wielers (RAL)10 Missing E T : Overview Physics motivation Susy searches and searches for extra-dimensions E T miss triggers often combined with jet triggers HLT processing Correct L1 E T miss for muon contribution (can’t read out all calorimeter cell information due to time constraints) Apply E T miss cut in hypothesis step Compute E T miss based on full calorimeter cell information Apply 2  noise cut at cell level Apply simple layer based calibration Apply E T miss cut in hypothesis step L2 EF

Real Time 2010Monika Wielers (RAL)11 Missing E T : HLT Performance in 7 TeV collisions Strong linear correlation between EF and offline Missing E T measurements Some of the high E T miss values arise from “bad” jets (due to noise fluctuations and will be removed in offline) Most of the events with fake E T miss don’t pass the L1 XE10 selection E T miss after XE10 (no offline clean-up)

Real Time 2010Monika Wielers (RAL)12 Missing E T : HLT Performance in 7 TeV collisions Excellent agreement between data and MC bin-by-bin turn-on curves Sharp turn-on curves  minimal distortion of the offline E T miss measurement by trigger Higher threshold is statistically limited The EF Missing E T trigger performs as expected on physics events EF Missing E T > 5 GeV EF Missing E T > 20 GeV

Real Time 2010Monika Wielers (RAL)13 Next commissioning steps for e/  /  /jet/E T miss 1 st commissioning step Deploy the HLT online without active rejection Verify HLT results w.r.t. offline, MC 2 nd commissioning step Start active rejection re-do studies using physics signals Physics running Measure performance on signal enriched sample Tag&Probe for Z and J/ , E T miss trigger for W  l Start optimising triggers for higher luminosities (includes pile-up) HLT rejection Already active for minimum bias triggers since a while HLT rejection for lowest L1 EM thresholds enabled in night from 24 th to the 25 th of May for run with peak luminosity of 2.1  cm -2 s -1 Lowest muon triggers will be the next ones to go in HLT rejection Jets will use mixture of pre-scale and HLT Ultimate goal In progess Just started! 

Real Time 2010Monika Wielers (RAL)14 Summary Data from  s = 900 GeV and 7 TeV LHC collisions have moved the commissioning of the ATLAS HLT one step ahead L1 calorimeter and muon trigger system working reliably HLT algorithms are running routinely online in pass-through mode (no active rejection, but results are created and available for analysis) Lowest threshold e/  triggers just went into rejection! Comparison of trigger quantities with reference offline objects show in general reasonable agreement and performance is reasonably well reproduced by MC simulations We increased our confidence that the selections we set-up will work as expected Trigger system in very good shape and we can face the challenge to select good quality physics data… interesting times lie ahead of us

Real Time 2010Monika Wielers (RAL)15 Backup

Real Time 2010Monika Wielers (RAL)16 The ATLAS Detector

Real Time 2010Monika Wielers (RAL)17 A 3 level trigger system:  Region of Interest ( RoI ) concept: only detector information contained in an angular region ΔηxΔφ= 0.2x0.2 around L1 cluster position are processed by next level (increase speed and reduce network load) ~40 MHz event rate ~75 KHz ~2 KHz ~200 Hz 2 μs 40 ms ~4 s level latency L1  L1Trigger (LVL1):  hardware based  only muon and calo information  reduced granularity  EF (HLT):  software based  full event information available  ‘quasi’ offline algorithms  Level2 (HLT):  software based  all detectors available (RoI approach)  dedicated algorithms and calibration L2 EF on detector The ATLAS Trigger System

Real Time 2010Monika Wielers (RAL)18 Towards the Physics menu at cm -2 s -1 Example: Jet plans Keep running HLT in pass-through (including multijets) Then enable multi jet signature only Then enable HLT Example: Tau plans From HLT pass-through chains (starts from L1 tau RoI > 5 GeV) to…

Real Time 2010Monika Wielers (RAL)19 Electrons and Photons: Performance in 7 TeV collisions Example: Electron identification variable Δη: difference in  between cluster and extrapolated track L2 distribution well described by MC simulations (also observed at EF) Good EF resolution w.r.t. offline L2 resolution is ~ factor 3 worse: due to the completely different tracking algorithm (IdScan) used To be approved