1 Jet Energy Correction With Cell Energy Density and Layer Weighting † The Way from Rome -> Costa Brava -> Milano -> Now -> First Data Iacopo Vivarelli On behalf of ‡ T. Costin, A. Gupta, P. Francavilla, F. Merritt, M. Oreglia, F. Paige, J. Proudfoot, C. Roda, B. Salvachua, I.Vivarelli † JetCalib: the package used to determine weights used in the so-called Global (H1- style) Jet Calibration ‡ Thanks to Peter Loch, Rolf Seuster, Peter Van-Gemmeren, Hong Ma and Scott Snyder for help with software issues.

2 Outline Reminder – Factoring Jet Energy Correction Basis for comparison (Release 12) vs today (Release 14, GEANT Scale changed) Performance on 12 and 14 and some validation checks Software layout Work in Progress –Integrate cell density and layer weighting –Layer weights and layer weighting on AOD –Scale using inversion technique Conclusions

3 Factoring Jet Energy Correction – H1-style Step 1: Correction based on cell energy density - expected to be mainly sensitive to e/h (but not entirely separated). Correction for dead materials (material upstream of calorimeter, cryostat, tile gap regions) - These corrections are derived from chi-square min. for jets in good calorimeter region. Step2: Jet Et and eta dependent scale correction for cracks and gaps in the calorimeter Step3: Additional physics related corrections for e.g correction based on fraction of energy in LAr calorimeter (Fem) will be sensitivity to MC hadronization model. CellWt( E/V, Layers ) MatWt(PreSamp, EM1) CryoWt( EM3,Tile1) GapCalWt( TileGaps ) JetScale( JetEt, JetEta ) JetEnergy( fem ) JetEnergy(Ftrk) …

4  Multi-jet event - Cone Use towers that include only those cells that belong to topoclusters - H1 weights. Un calibrated (barrel region) Calibrated (towers from topos) Calibrated (standard towers) From: 12x: H1-Style & QGSP (Reminder from Costa Brava) Our reference to assess improvements in the method and software

5 Athena 14x (H1-style equivalent to12x.) With new weights, linearity shows similar performance to that obtained in Rel 12. QGSP_BERT -> 5% Scale shift wrt EMV New Weights now available by JobOption

6 Test Using Independent Physics Sample TTbar Herwig vs Pythia di-jet: different in physics process, generator and fragmentation See response deviation from unity at the 1-4% level when applying weights to Cone7 and Cone4topo jets: is this physics or calibration? Note: Test done using our full chain: drop new weights into database, run full reconstruction chain, produce plots via JetPerformance Pkg + 2% - 2%

7 Testbeam vs Testbeam MC: Performance on energy linearity MC (QGSP_EMV) and Data are within HAD Scale Redoing the exercise for QGSP_BERT References: Atlas SM meeting 22 March 2007 Hadronic Calibration workshop Milano, April 2008 CSC Note - Jet, Missing Et and Tau Combined Performance: Detector Level Jet Corrections H1 Weights derived on MC simulation of single pions with the CTB04 geometry and applied both to MC and real data. Obtain linearity and resolution. Compare performance between MC and real data.

8 JetCalib Software and JetSampling from Athena 12.x to 14x In release 13 –EM, H1, Pisa, Sampling integrated into a common framework –Level 2 calibration based on JetSampling approach Release 14 development –Changes to accommodate ESD, AOD, DPD, Truth, Reco in common framework –Improved algorithms to determine weights and global scale –Performance

9 ESD, AOD Calibrated Jets Cells, Layers Truth Jets Jet Sampling Collection ASCII Calibration constants Calibrated Jet Sampling Collection BuildJetSamplingAlg JetFit H1Calibrator, LayerCalibratorAlg CBNTAA_JetSampling Calibration Constants in DataBase JetAlgorithm BuildJetSamplingTool JetCellCalibratorTool JetLayerCalibratorTool JetClassifier ROOT Jet Sampling ntuple ROOT macros JetPerformance ESD, AOD Calibrated Jets Cells, Layers Truth Jets Software Layout used for H1-style/Sampling Calibration

New developments 1.Exploiting the best from combining ideas from Cell and Layer weighting strategies 2.Using Layer calibration strategy from AOD 3.Inversion technique to improve performance at low pt 10

11 Combining H1-Style and (Layer) Sampling Methods New development 1  H1-style uses cell E/V to discriminate EM and HAD showers. Weights are independent of jet energy. Sampling method uses the longitudinal shower profile. Weights are a function of jet energy and eta.  These ideas can be combined - Regroup the cells in finer longitudinal layers. -- Also avoid mixing CaloCells of different physical/readout sizes. - Use fem bins etc., at JetScale stage or later.

12 Athena 14x (combining H1 & Sampling) Better optimization of H1 Fit Regions Separate EM2 from EM3 (EMB is already separated in two layer at |  |=0.8. EME at |  |=2.5) Fit HEC(0,1) and HEC(2,3) in separate E/V bins (HEC layers divided in two layers at |  |=2.5, since the readout size changes). Fit Tile0 and Tile1 in E/V bins separately. Fit Tile2 as a single layer. First attempts: Linearity looks good Some improvement in resolution Further improvement possible at JetScale stage: Better use of Gap scintillators Apply CellWt to cells above a noise threshold

13 Derive and Apply Layer Weights at AOD level- Release Cone4Tower Jets Resolution: Code works – now looking at performance Calorimeter longitudinal layers combined together depending on the jet pseudorapidity Jets classification based on calorimeter em fraction and jet pseudorapidity Layer weights depend on the jet energy. Determined minimizing the resolution

14 Current status of layer correction: Linearity Cone4Tower 2008 AOD Cone4Tower 2006 ESD ATL-COM-PHYS Work in Progress Under Study

15 Current status of layer correction: Resolution 0.04 Cone4Tower 2008 AOD Cone4Tower 2006 ESD ATL-COM-PHYS Strange poor resolution improvement on |  |  (1.5,2.5) under study Work in Progress

16 Revisit an old idea: Truth->Reco inversion technique New development 3 ( also being used in more recent work, for example Use following conditional probability to estimate corrected jet energy. P(ET | ER) = P(ER | ET )*P( ET )/(Normalization) ER is reconstructed jet energy. ET is true jet energy. P( ET ) is input jet spectrum. We know P(ER | ET ) from MC - This is the response of the detector to a truth jet of certain energy and eta. Want to Determine Jet Scale based on Reconstructed Jet Energy Dips in the sample are associated with Jx thresholds Weighting by cross section improves linearity but not completely and introduces other issues

17 Inversion Technique: Apply to EM Scale Jets des&confId=41483 Response function built from em-scale Jets. - Truth jet pT >20 GeV. - Only five eta bins , , Applied to E_reco with matched truth pT >30 GeV. Ideally we would want to build response function in finer bins of eta and energy. As noted by others, the resolution is also improved 0< |  |<0.7

18 Apply Also to H1-Style Calibrated Jets Here the response functi- on is built from jet applied with CellWt(E/V). Strong improvement in re- solution compared to em-scale jets (last slide). Note: For comparison the constant terms are fixed in the lower fits to upper ones. 0< |  |<0.7

19 Conclusions… QGSP_BERT simulation agrees better with test beam. Gives O(5%) shift in jet scale compared to QGSP_EMV. Is simulation converging? Then worthwhile to redo jet energy scale. Define jet scale by comparison with same jet algorithm(s) on MC truth. (Need additional corrections for physics results). First simply repeated H1 and layer weights fits with QGSP_BERT. Agreement for pythia QCD jets and MCAtNLO top samples comparable to earlier results. But higher statistics  few percent effects clearly visible. Still workong to resolve these.

…and next steps Work in progress –Integrate cell density and layer weighting –Layer weights and weighting on AOD –Scale using inversion techniques Crucial to develop in situ techniques to verify/improve jet simulation and response Should extend work to new jet algorithms Software now more integrated and better organized. It should be more usable by others. Still need more documentations

21 Backup

22 Performance on energy linearity Hadronic scale EM scale MC Is the performance on linearity consistent between MC and data ?

23 Next steps Redo this analysis using the latest data analysis and MC with QGSP_BERT Use this method to evaluate the performances of other calibration strategies: Sampling, LC … Understand the possibility of using E/p in ATLAS to do the same analysis.

24 Software location: Reconstruction/Jet JetEvent: Definition of JetSampling and JetSampling Collection. JetSampling class contains the energy in layers, energy density in cells, radial profiles, truth jet (PIC and NTJ), uncalibrated and calibrated jet. All this information is needed by JetFit algorithm to calculate the calibration constants JetEventAthenaPool: Transient/persistent converters of JetSamplingCollection, JetSamplingCollectionCnv JetEventTPCnv: Persistent representation of JetSampling and JetSamplingCollection –JetSampling_p1 and JetSamplingCnv_p1 class –JetSamplingCollection_p1 and JetSamplingCollectionCnv_p1 JetUtils: Definition of JetClassifier class. JetClassifier implements all methods to fill JetSampling. JetRecTools: Contains the tools to calibrate the jets. –JetSamplingCalibTool: Calibrateds jets using Layer method, it will change name to JetLayerCalibratorTool –JetCellCalibratorTool: Calibrates jets using H1-style method JetRecTools used to hold the algorithm to create JetSampling from ESD (JetSamplingCalibAlg) now this algorithm has been moved to JetCalib with the name BuildJetSamplingAlg

25 Software location: Reconstruction/Jet JetCalib: Main package for the calibration. –BuildJetSamplingTool and BuildJetSamplingAlg: Algorithm and Tool used to create the JetSampling collection from any pool file. –JetFit: Athena algorithm that implements the calculation of the calibration weights. Several configurations are foreseen. Different eta ranges Different energy binning Different calibration methods, the most used: –FitSampleIn2E3FemBin: Layer or Sampling method ( atl-com-phys ) –FitCellDenH1Style: H1-style method (cell energy density) –FitCellDenInLayers: new H1-layer style method (cell energy densisty in layers) –H1Calibrator and LayerCalibratorAlg: Athena algorithms that read the calibration from ascii files and apply it to a JetSampling collection. –CBNTAA_JetSampling + macros: Athena algorithm to create a ROOT ntuple from a JetSampling collection. In the share directory there are several macros to produce linearity and resolution plots. JetPerformance: Used to produce histograms used for CSC note and test the performance of the calibration

26 JetSampling Collection: Contents Summary of all the variables accessible from JetSampling: –Kinematics of the reconstructed jet at EM scale –Kinematics of the Nearest-truth-jet –Kinematics of the Particle-in-cone jet –Kinematics of the calibrated jets: H1 PISA SAMPLING –Distantance of the 1st and 2nd Nearest-truth-jet –Jet layer information: PreSamplerB, PreSamplerE EMB1, EMB2, EMB3 EME1, EME2, EME3 TileBar0, TileBar1, TileBar2 TileExt0, TileExt1, TileExt2 TileGap1, TileGap2, TileGap3 HEC0, HEC1, HEC2, HEC3 FCAL0, FCAL1, FCAL2 –Jet energy in the EM calorimeters –Jet energy in the HAD calorimeters –JetSums –JetECS (still needs some cleanup0 –Energy in cone radii: Radial profiles

27 JetSampling from ESD, AOD, DPD ESD: –Reconstructed jet –Layers, Radial profiles, Energy density –Truth information AOD: –Reconstructed jet –Layers –Nearest-truth-jet –Particle-In-cone (from GEN_AOD instead of TruthEvent?) DPD ( from data ): –Reconstructed jet –Layers –Radial profile –Energy density –… Same as ESD except for the truth

28 Job Options for use in Applying Weights to EM Scale jets

29 Combined Test Beam 2004 data may be used to investigate the performance of any hadron calibration on real single hadrons. Method: H1 Weights derived on MC simulation of single pions with the CTB04 geometry and applied both to MC and real data. Obtained (for example) the linearity and the resolution. Compare performance between MC and real data. Work done up to now: Used MC QGSP-GN Hadron Calibration: Standard H1 cell calibration Single Pions at η=0.35, 20 → 350 GeV; Quality cuts on the beam position; Cuts to reject muon and electron contaminations; Estimate of the effect of the proton contamination (for π + ). Monte Carlo Simulation: Comparison to Testbeam

30 Layer weighting on AOD New Development 2. Region selection: –|  |  (0.0, 1.5) Layer 0 = PreSampler + LArEM1 Layer 1 = LArEM2 Layer 2 = EM2 + Tile1 Layer 3 = Tile2 + Tile3 –|  |  (1.5, 3.2) Layer 0 = PreSampler + EM1 + EM2 Layer 1 = EM3 + HCAL + FCAL –|  |  (3.2,4.4) Layer 0 = Full jet energy Jets classified in bin of , energy and fractional energy (fem) Dependence on the energy as: Minimization function: