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Jet Reconstruction in Athena

Jet Reconstruction in Athena. Outline : Introduction Athena : The Atlas Software Framework Jet Reconstruction Tau Reconstruction Energy Flow Summary. Atlas Calorimeter Energy Calibration Workshop Ringberg Castle 23/07/02 Ambreesh Gupta, University of Chicago.

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Jet Reconstruction in Athena

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  1. Jet Reconstruction in Athena Outline : Introduction Athena : The Atlas Software Framework Jet Reconstruction Tau Reconstruction Energy Flow Summary Atlas Calorimeter Energy CalibrationWorkshop Ringberg Castle 23/07/02 Ambreesh Gupta, University of Chicago

  2. People Involved The group of people involved in jet reconstruction in the atlas software framework - Martine, Monika, Peter, Ed, Srini, Tom, Hong, Jim, Jon, Frank, Frank… People interested can join the JetRec bi-weekly telephone meeting at 5 pm CERN time on Wednesdays.

  3. q Initial Parton in Hard Scattering p p q ISR/FSR Fragmentation Underlying Event Event Properties LHC Design s = 14 TeV L = 10-34 cm-2 sec-1 Collision Every 25ns 23 Interaction/Collision e/ Dead material Magnetic field Electronic Noise Pile Up Detector Effects The challenge is to identify reconstructed jet with the initial parton:Nontrivial Reconstructed Jet in Calorimeter

  4. Goal of Hadron Calorimetry: . Resolution 50%/E  3%. . Jet Scale 1%.

  5. The Steps of Jet Measurement Jet Measurement can be broadly divided in three steps Jet Reconstruction Energy deposit in the calorimeter is clustered by Jet reconstruction algorithms, e.g Cone and Kt. Energy Calibration Energy of a Jet is calibrated for non-compensation, dead material, magnetic field, etc. Flavor Identification b-tag, -tag, etc.

  6. Athena : The Atlas Software Framework Framework Provides Services: Execute, Monitor and Output From Algorithms. Framework Philosophy: Data Classes are Stable, Algorithms Change. User Can Plug in New Algorithms. Transient Event Data Store Transient Detector and Calibration Data Store Algorithm A Algorithm B Algorithm C

  7. Jet Reconstruction Package Many Kind of Input Data Many Kind of Algorithms Abstract The Inputs For Algs Cone CalCell CaloTower Kt Cluster CaloCluster ProtoJet ….. Tracks ProtoJet Class: . Can be created for different sub system. . Homogenous. Energy Flow MC Truth

  8. Jet Reconstruction Package Jet Reconstruction Algorithms Energy Correction Algorithms Sampling Based Correction Cone Jet Jet Kt Cluster H1 Style Correction ….. Working with data abstractions is great but the information lost is required by correction Algorithms….. Jet Class: . Jets are composed of ProtoJet . Provide Mechanism to deal with Overlapped Jets. . Provide mechanism to deal with Recombination Schemes.

  9. Navigating Composite Objects Problem Statement : Given a Jet made of ProtoJet – How do I know what kind of CaloCell (LAr,Tile ,etc.) are they made of ? Record structural pattern in Tokens. Access them through special processor classes. Jet JetToken PJet PJetToken PJet PJet Tower TowerToken Tower Cell Cell Cell CellToken

  10. Available Tools and Packages • There are many ‘Packages’ that help in Jet Reconstruction. The three listed below are the typical names associated with, jet, tau and energy flow reconstruction - . JetRec . tauRec . eflowRec

  11. JetRec

  12. KT jet Cone jet Jet Algorithms Jet algorithms are employed to map final states, both in QCD pert. theory and in the data, onto jets. The motivating idea is that these jets are surrogates for the underlying energetic parton. Clustering - hadrons, Calorimeter Cells,Towers etc., for nearness… Nearness in angle => Cone Algorithm. Nearness in relative transverse momentum => Kt algorithm. Recombination Scheme - The momentum addition rule of particles in a jet. Historically hadron collider use cone algorithms : easier calibration

  13. Cone Algorithm Cluster particles within a radius R = 2 2. Cone iterated until a stable Et weightedcone is achieved. Possible to produce overlapped cones – Needs a Split-Merge step. Various version of cone algorithm dealing with issues of speed and theoretical uncertainty. Implementation of a seedlesscone and split-merge algorithm in JetRec. Configurable through jobOption file. /E Chrono services provided by Athena time profile algorithms. Average reconstruction time for 1 GeV jet ~ 0.7 sec. 1/E GeV-2

  14. Kt Algorithm Cluster “particles” in order of increasing relative transverse momentum. Requires a method to terminate clustering. No overlapping Jets. Theoretically well behaved by design. The algorithm is O(n3) => Pre-Cluster particles. Implementation of one Pre-Cluster and kT algorithm in JetRec. Reco time for 1000 GeV jet . Apply an Et cut of 100 MeV => 200 input ProtoJet. Average time ~1 sec. . Not Et cut => 800 input ProtoJet. Average time ~ 1 min. /E 1/E GeV-2

  15. Energy Correction Algorithms Various method of energy correction used by Atlas in studies – Parametric minimization, in-situ, e/h. • Setup of example algorithm to do energy correction • - weights taken from earlier studies (Lefevre & Santoni, Martine) • iterative procedure to approximate the true energy for parameters • use Navigation packages. The correction improves both linearity and resolution, but not in exact match with earlier studies. /E New studies with H1 style calibration … see later 1/E GeV-2

  16. tauRec

  17. Tau Reconstruction Tau’s have unique importance in SUSY searches. Reconstruction Steps : A Sliding Window algorithm with window 0.5x0.5 Identify Cluster Three highest pT tracks with pT > 2 GeV And within R < 0.4 Associate Tracks to Cluster 1) H1 style calibration 2) Hadronic calibration Calibrate Cluster Variables to distinguish hadronic tau decays from QCD jets. 20 fold reduction in bkg. with 50% acceptance for SUSY signal. Tau ID cuts

  18. Hadronic Calibration (Used in Physics TDR) Weights derived from single pion Corrects the overall energy scale but does not give optimal resolution.

  19. H1 Type Calibration Makes use of the fact that hadronic showers are more localized than EM showers. Derive weights in bins of Et for each layer - EM3, Tile and HEC No weights for EM1, EM2. Minimize function : With constraint : Samples of single pion with pT = 10, 20, 40, 80, 160 GeV were used.

  20. Calibrated ET/PT (p) vs h (H1 weights) Pion resolution with H1 weights much better than fixed weights. /E = 38.56%/E  3.56% Applying the H1 weights to the reconstructed tau events gave significant improvement in both the average response and the resolution.

  21. Extending H1 style Tau Calibration to Jets. Jet35 sample was chosen for this study. 35 GeV dijet sample with : . pT ( hard.scat. ) > 35 GeV. . Electron rich trigger. Calibration weights re-derived for this sample. Since sample is dominated by low Et jets, f(ET) chosen to be – f(ET) = ET2 To compare to MC, same jet reconstruction algorithm applied to ProtoJets generated from Truth in ATLFAST.

  22. Comparing Et/Et,MC kt Jet : Mean close to 1. Increases with Jet Et cut. Resolution curves show tails in gaussian fit. Fits fairly well with double gaussian. cone Jet : Mean close to 1. Similar to above, increases with jet Et cut. Tails, substantially reduced compared to above.

  23. ET/ET(MC) after calibration ( kT jet )

  24. Fitted H1 weights for Jets ET Bins = 1/32, 1/16, 1/8, … 16 GeV Cryo term = 0.471* (EEM3 * ETile) Gap weight = 0.915

  25. Investigating the Tails Possible effect of miss measurement of jet energy due to magnetic field studied by taking in to account of tracks that sweep in(out) of the jet. No noticeable change. Cleaner Jet sample ( without electron rich trigger ) for several ET bins being requested for further studies on the tails.

  26. eflowRec

  27. Energy Flow Concept Basic Idea : The well measured particle momentum substitutes random fluctuation of energy in the calorimeter => better resolution. Introduced first by LEP experiments led to significant improvement in jet energy resolution. The idea is simple but challenge to realize – requires building the particle ID associated with the track. This starts running in to difficulties in high track multiplicity environment and coarse calorimeter granularity.

  28. Energy Flow Package eflowRec is a first attempt to combine calorimeter, tracking and PID information to improve energy resolution for jet and ETMiss Algorithm flow : ECAL HCAL Loop over tracks Tracks and cluster matched in  and  ( neutral particle ) and using helix parameter of tracks ( charged particles ) to form topologically connected eflow objects. neutral neutral charged Subtract expected energy deposit in EM and Had cluster. Estimated based on the particle ID hypothesis. Loop over the remaining EM clusters and subtract expected energy in HCAL clusters

  29. Plans : eflowRec Limitation at present due the availability of full PID packages. Combined muon algorithm as input. Brem recovery and conversion finding. scalar sum pT missing pT PID internal to eflowRec. 3D cluster reconstruction. Various choices to be made: Need MC studies to constraint. y-comp. of missing pT x-comp. missing pT

  30. Summary • The jet reconstruction setup in Athena is fairly mature. • The important things that should be on the priority are – documentation and a setup to test and validate released code in a simple way. • New studies in calibration studies driving the software setup to produce the right tools. • Progress in tau reconstruction and H1 style calibration. • First implementations of an Energy Flow package available in Athena. Please send comment/suggestions to the jets-combined mailing list or join the bi-weekly phone meetings for further details.

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