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Electron Identification in ATLAS

Electron Identification in ATLAS. Introduction Calorimeter Reconstruction Inner Detector Reconstruction Combined Reconstruction Rejection/Efficiencies Performance in Rome What needs to be done to be more realistic. Introduction.

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Electron Identification in ATLAS

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  1. Electron Identification in ATLAS • Introduction • Calorimeter Reconstruction • Inner Detector Reconstruction • Combined Reconstruction • Rejection/Efficiencies • Performance in Rome • What needs to be done to be more realistic M. Wielers, RAL

  2. Introduction • E/ Identification important for lots of physics channels such as Higgs(SM, MSSM), new gauge bosons, extra dimensions, SUSY, W, top • Need to cover the full range of pT from few GeV (B-physics) up to ~1TeV (new gauge bosons…) • Examples: • H  Most promising channel for Higgs if mH<130 GeV • H  ZZ*  4e Most promising channel for Higgs if mH>130 GeV • H  WW, ZZ  ejj, ee, eejj Interesting for heavy Higgs mH<1 TeV • New extra gauge bosons: Z ’ ee, W ’  e • tt production • Rare decay modes of one top-quark • Z  ee Important for e.m. calorimeter calibration • + lots of other channels, e.g. Susy M. Wielers, RAL

  3. Calorimeter Energy measurement • Design Goal • Resolution ~10%/E up to 300 GeV • Constant term < 1% • Linearity better than 1% from few GeV up to ~TeV • Layout • 3 layers in depth • main compartment 2nd layer • ‘Small’ cells in eta in 1st layer • Pre-sampler in <1.8 M. Wielers, RAL

  4. How do we measure the energy? Losses between PS and S1 • Cells calibrated at EM scale using optimal filtering • Use longitudinal weights (eta dependent) • Compromise between best resolution and best linearity • Cluster reconstruction • ‘default’: sliding window • Topological clustering strips Middle Back e 50GeV Longitudinal Leakage Upstream Losses Upstream Material Presampler LAr Calorimeter M. Wielers, RAL

  5. Sliding window algorithm • ‘default’ clustering algo in athena for EM particle reconstruction • Sum energy in depth in =0.0250.025 • Cut cells artificially and share energy • Look for ‘local’ maximum using sliding window • Take candidate window • Look at energy in window shifted by one cell to left/right, up/down, vertical and decide if you have local maximum • Move window to next position E EU E>EU E>ED EL E ER E E>EL , E>ER ED M. Wielers, RAL

  6. Sliding window Clustering • Build up cluster in given window (37, 35, 55) • Now use cell information, no longer towers • Correct for S-shapes, eta/phi modulations, leakage outside the cluster… Corrections applied: Dependence SIZE (approx) S shape in eta, middle f(eta,Energy) Phi offset f(eta,Energy) S shape in eta, strips f(eta,Energy) E vs phi local modulation f(eta,Energy) 0.5% E vs eta local modulation f(Energy) 0.2% Gap correction f(eta) Longitudinal weights f(eta) 3-10% • corrections derived from G4 single-e samples (Scott+Stathes) • Longitudinal Weights: E(corr) = Scale(eta)*(Offset(eta)+W0(eta)*EPS+E1+E2+W3(eta)*E3) • Latest iteration of longitudinal weights using 9.0.4 single electrons. Out-of-cone corrections aborbed in Scale(eta) • Some corrections different for 5x5, 3x5 3x7 M. Wielers, RAL

  7. Topological clustering phi • More ‘flexible’ than sliding window as it ‘adapts’ itself to the shower • Cluster is built around a Seed Cell which has an energy above a certain threshold (the Seedthreshold) • The neighbours of the Seed Cell are scanned for their energy and are added to the cluster if this energy is above the neighbour threshold. Then the neighbours of the neighbours are scanned and so on. • The cuts, which are made for the seed and the neighbour, depend on the noise in each cell • Similar cluster corrections needed as for sliding window • S-shape in eta, Phi offset, E vs phi modulation corrections implemented but not applied yet Seed Cell eta M. Wielers, RAL

  8. Linearity Performance DC2 layout (10.0.1) S. Snyder +1% -1% eta eta Erec/Etrue eta eta 1TeV M. Wielers, RAL

  9. Energy Resolution Comparison with TDR G.Unal EM Resolution is up to 25% worse than in the TDR M. Wielers, RAL

  10. 10.0.1 TopoCluster(630) vs 3x7 100 GeV 50 GeV 20 GeV Flores,Mellado,Quayle,Sau Lan Wu (topo and 3x7 recalculation of Long. Weights) topo 3x7 100GeV topo topo 3x7 • Resolution s: similar to 3x7 • Linearity: systematic shift: ~0.4%, maybe more over larger energy range. Needs to be understood. • RMS: improved with TopoCluster. Expected since outliers more likely to be caught by TopoCluster. Needs to be evaluated in realistic environment. 20GeV M. Wielers, RAL

  11. EM shower shape analysis shower shape variables useful for e/jet (/jet) separation • leakage into 1st sampling of had calo • Transverse shower shapes in 2nd sampling • Transverse shower shapes in 1st sampling Few examples 2nd sampling electron jet 1st sampling electron jet M. Wielers, RAL

  12. Complete list of calo shape cuts used in e-ID • Leakage into the hadronic calorimeter in =0.20.2 • Total leakage (typically only done at LVL1) • Leakage into the 1st sampling (lass bias from noise) • Shapes in 2nd sampling • Shape in eta: E(37)/ E(77) • Energy weighted width in 35 cells (corrected for impact within cell) • Shapes in 1st sampling • Fill energies in =0.125, sum over 2 bins in phi • Search for local maximum • cuts • Energy of 2nd maximum above fluctuation • E2nd – Emin above fluctuation • Energy outside core frac73= [E(3s) – E(1s)] / E(3s) • Width in 3 strips (corrected for impact parameter within cell) • Total width in =0.1250.2 • Most powerful cut Most powerful cuts M. Wielers, RAL

  13. Inner Detector Reconstruction • Inner detector • Pixel • SCT • TRT • Several offline reconstruction packages available • xKalman • iPatRec • New tracking à la DELPHI • Not validated yet • Supposed to be the default one in the future • Note: TR information can be used for e/ separation • Cut on number of high threshold TR hits (dependent on eta) • Not possible for Rome productions due to problem at simulation level M. Wielers, RAL

  14. iPatRec track reconstruction • Default in Rome productions • Muons can ‘only’ deal with iPatRec tracks • Track finding Strategy • Primary vertex definition • Use lightweight version of track finding • Simple fit to 2-point candidates (pT>3GeV) • Z-coordinates stored for high quality tracks • Primary track finding • 2 space points + ‘loose’ transverse vertex or 3 space points from different layers • Picks up all tracks from the vertex incl. those a few mm off (e.g. from B-decays) and track segments before 2ndary interactions • If possible associate TRT by extrapolation + histogramming algorithm for tracks confirmed in outer SCT layer • Secondary track finding • 3 SCT points with very loose vertex requirement. • Track confirmed if TRT associated • Attempt back extrapolation towards vertex M. Wielers, RAL

  15. iPatRec track reconstruction • final track fit • Includes material effects • energy loss correction via scattering centres • Less ‘aggressive’ weighting for hits shared by more than one track • Single brem point may be allocated to allow for significant curvature increase in the TRT prolongation • minimise gaps due to missing hits along track • tracks truncated if track following fails (as in case of brem) • Note: electrons with hard brem frequently found as 2 tracks • Short primary one from vertex • secondary one which fails back extrapolation • Cluster-track match in egammaRec if • E/p < 4 • Position match after TrackExtrapolator:  < 0.025 and  < 0.025 (relax to 0.05 for brem tail side) M. Wielers, RAL

  16. xKalman track reconstruction • Primary track finding starting with Pixel/SCT • Original idea start from TRT and go inwards • Now very similar to iPatRec • Do Kalman filtering, extrapolate track to TRT • Final track fit (e-fit possible (TrackModel=61 in jobO.) • Track accepted if • No. of prec. Hits  6 (reduces fakes) • N(straw hits)/{N(straw hits)+N(empty straws crossed)} > 0.7-0.8 • N(drift time hits)/N(straw hits) > 0.5-0.7 • Maximise number of hits along track • Cluster-track match in egammaRec if • E/p < 4 • Position match after TrackExtrapolator:  < 0.025 and  < 0.025 (relax to 0.05 for brem tail side) M. Wielers, RAL

  17. Track Quality cuts • Used in the past for e/jet separation using xKalman • No of B-layer hits  1 (ensure track comes from vertex) • No of pixel+SCT hits  7 (6 for Rome layout) • Transverse impact parameter  2mm • For Rome data using iPatRec • Transverse impact parameter |A0| 2mm • All other cuts can’t be applied as tracks might be truncated • Though, kind of works w/o pile-up added M. Wielers, RAL

  18. Track-Cluster Match • Match in E/p • Match in  and  • Extrapolate tracks into calo • Info not in AOD! • Cuts eta dependent • Average shower depth not well parametrised in athena now • Work in progress • Matching in eta/phi should improve soon xKalman iPatRec xKalman iPatRec xKalman iPatRec Brem tail M. Wielers, RAL

  19. Jet Rejection (from DC1 data) • Rejection for e25i at L= 2·1033 cm-2s-1 • LVL1 looks at ET, EM and hadronic isolation using trigger towers (=0.10.1) • In principle add LVL2 which works very similar as offline • However, there were problem in LVL2 tracking at time of DC1 • Apply ET>22 GeV in offline (to be efficiently selecting electrons with ET>25GeV) • Tuning done in various eta bins to get flat response • <0.8, 0.8<<1.37, 1.52<<1.8, 1.8<<2.0,<2.47 • Nothing special done in barrel/EC crack besides not applying cuts in 1st sampling as there are no strips (would need special study esp. for energy reconstruction • Jet rejection normalised to the number of jets reconstructed in ATLFAST • Advantage: rejections can be easily ported to ATLFAST • A bit more realistic due to initial/final state radiation M. Wielers, RAL

  20. Jet rejection at L= 2·1033 cm-2s-1 • the 29 remaining events : • 21 originating from early conversion • Even with conversion reconstruction package probably difficult to find these conversions (at least 1 hit in b-layer!) • This tuning is implemented in athena (isem flag) M. Wielers, RAL

  21. Jet rejection at L= 1034 cm-2s-1 • Optimised for electrons with ET>30GeV • Again reflects inclusive single electron trigger e30i • ET(EF calo) > 27 GeV • 91 events remaining in final event sample • 49 from conversions M. Wielers, RAL

  22. Possible improvements: better Brem recovery • Better brem-recovery  better E/p measurement • DC1, Single electrons, ET=20GeV, no pile-up, xKalman • TDR data, single e, ET=30GeV, high lumi pileup in calo only • Noone looked at it for a long time now… due to additional material compared to physTDR gain might be small Nice brem recovery… M. Wielers, RAL

  23. Material distribution changes • Major change since physTDR is insertable pixel layout • Barrel: • ~35% more conversions (= brem) before 3rd pixel layer • ~30% more conversions before 1st SCT layer • End-cap • ~2 times more material in EC (2.0 <<2.5) • reduced radial pixel lever arm: precision 4-6 times worse • Some more material will certainly be present in ‘real’ detector… with all little bolts, supports, cables…. 0 Rome-final vs. physTDR Rome-final / DC1 physTDR  M. Wielers, RAL

  24. Possible improvements • Early conversion reconstruction • Has been neglected as well for a long time • Problem here are fake conversions esp. in presence of pile-up • Conversions near the beam-pipe are hard to reconstruct in inner detector, so probably one can’t do very much • Work on conversion reconstruction about to re-start seriously • Phone meeting tomorrow afternoon • In addition • Use beam spot to “recover” pixel lever arm • Depends on physics • Some correction for radiation in early layers to give correct mean of E/p for calibration • Use isolation around the cluster • Depends on physics, difficult in the presence of pile-up • Combine energy measurement in calo and tracker for non-hard brem electrons • Due to material combining tracker+calo energy measurement per definition probably very difficult M. Wielers, RAL

  25. Other methods to do jet rejection • Likelihoods, neural network, decision tree • However, not really suited for day-1 as detailed understanding of the detector performance is needed • Need to ensure works well together with the trigger selection • Have to result in flat distribution in eta • See talk at Rome by K. Benslama for details • Don’t do into details here as no ‘robust’ results available yet M. Wielers, RAL

  26. What needs to be done for Rome data • New tuning for electron identification which are flat in eta • Preferably include at least LVL1 trigger • Unfortunately, not all cuts are available in AOD’s… • Crack region should be better understood • Currently 1st try • Tunings should be deduced from Zee events which can be done on real data =86% =82% M. Wielers, RAL

  27. What needs to be done to be more ‘realistic’ • Extract efficiencies from real data using Zee (à la CDF) • similar as done in trigger (Gomes-Diaz) • Use Zee events with at least one electron triggered by e25i, use other electron for the estimate • Reconstruct Z-peak using e25i events and 2e25i events • Number of Z events obtained from fitting procedure for e25i given by • With from true Z event and from fake ones • For 2e25i: • Thus: • Needs to be done as a function of  and pT and luminosity M. Wielers, RAL

  28. Some trigger related issues e25i for ET>22 GeV • Efficiencies after each step (LVL1, LVL2, EF, offline) need to be extracted and errors understood. • Fold out effects from overlaps of different trigger items e.g. e25i, 2e15i • Setup new trigger menu items, e.g. e60 with 90% electron efficiencies M. Wielers, RAL

  29. Influence on luminosity • Use cuts optimised using single electrons without pileup • Use same cuts for electrons with pileup (L= 1033 cm-2s-1)added • Try to do tuning using cuts which are less sensitive, study different lumi scenarios to find out when tunings should be changed and study effect on systematics Tuning without pileup Applied to pile-up sample Loss partly due to shower shapes, partly due to ‘missing’ tracks in sample with pile-up added Total loss: ~4% Some strange dips… M. Wielers, RAL

  30. InterCalibration in-situ using Zee N.Kerschen, M.Boonekamp, F.Djama • Intercalibration needed to make uniform the response of different regions of the calorimeter • Residual non-uniformities from mechanical deformations, temperature gradient… • Possible with Zee (TDR) • Several Methods have been proposed • ATL-LARG-2004-08 (FD) • 0.3% Uniformity was found in DC1 for the Barrel • Method for independent phi/eta intercalibration (NK,MB) • Phi with min-bias, We and +Jet • Eta with Zee • 0.2% Uniformity is found with full sim • Caution on material effects: ATL-LARG-2004-016 (SP) • Monitor calorimeter linearity with Zee • Use reference Zee distribution which includes resolution effects (NK,MB) • But what about higher energies… M. Wielers, RAL

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