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20-Dec-04 F. Merritt 1 Jet Reconstruction and Calibration in ATLAS Frank Merritt / Ambreesh Gupta University of Chicago North American Atlas Physics Conference.

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Presentation on theme: "20-Dec-04 F. Merritt 1 Jet Reconstruction and Calibration in ATLAS Frank Merritt / Ambreesh Gupta University of Chicago North American Atlas Physics Conference."— Presentation transcript:

1 20-Dec-04 F. Merritt 1 Jet Reconstruction and Calibration in ATLAS Frank Merritt / Ambreesh Gupta University of Chicago North American Atlas Physics Conference Tucson, Arizona December 20, 2004 (version 3.0)

2 20-Dec-04 F. Merritt 2 JetEtMiss Group Co-conveners: D. Cavalli, A. Gupta, F. Merritt Work reported here: Kyle Cranmer, Ambreesh Gupta, Peter Loch, Sanjay Padhi, Frank Paige Other U.S./N.A. Contributors: A. Farbin, J.Proudfoot, T. LeCompte, M. Hurwitz, S. Rajagopolan, Irene Vichou; M.Weilers, R. Mazini Key European participants: Donatella Cavalli, Sylvia Resconte, Chira Roda, Iocopo Vivarelli, Martine Bosman, Caroline Deluca, Sasha Solodkov, Dan Tovey, David Rousseau

3 20-Dec-04 F. Merritt 3 Main areas of activity in 2004 Jet-Finding Algorithms: –Cone –Seedless cone –Kt Integration of TileCell, LArCell into CaloCell, etc. Extensive changes in response to RTF recommendations on jet classes, navigation. New Tools Topological cluster-finding (Sven Mencke) Calibration: –BNL (Frank Paige, Sanjay Padhi) –Chicago (Ambreesh Gupta; A. Farbin, F. Merritt ) –Pisa (Chiara Roda, Iacopo Vivarelli) Extensive work of Hadron Calibration group Major test-beam effort (data-taking plus software development)

4 20-Dec-04 F. Merritt 4 General algorithm and tool features: General algorithm and tool features: only one algorithm JetAlgorithm for all jet finding strategies: owns the tools performing the actual job; retrieves the input collection (generic INavigable4MomentumCollection ) and converts objects in this collection into Jet s held in a optimized internal collection; … but algorithm does not fail if no input data to be retrieved -> special tool can load in special data object collection(s) which cannot be addressed as INavigable4Momentum, like truth particles, for specific internal conversion; algorithm and tools are protected from specifics of the internal collection ( JetUtils/JetCollectionHelper provides all memory management etc.); extensive use of all kinds of other helpers by algorithm and tools -> avoid duplication of code and ensures coherent behaviour: JetUtils/JetSorters for up or down sorting by various kinematic variables; JetUtils/JetDistances for distances in r, eta, and phi (actually uses FourMom helpers); JetUtils/JetSelectors for jet selection by cuts in kinematic variables ( in/out ranges, above/below thresholds); From Peter Loch: SW week, May 2004

5 20-Dec-04 F. Merritt 5 Example Kt detector jet finder algorithm flow (8.2.0): Example Kt detector jet finder algorithm flow (8.2.0): Kt JetAlgorithm Transient Event Store JetSignalSelectionTool JetPreClusterTool JetKtFinderTool JetCellCalibTool JetSignalSelectionTool convert to Jets record JetCollection INavigable4MomentumCollection JetCollection symLinked to concrete CaloTowerContainer, CaloClusterContainer, CaloCellContainer, TrackParticleContainer, eflowObjectContainer,… KtTowerJet_jobOptions.py

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8 20-Dec-04 F. Merritt 8 Main problem areas Calorimetry effects: –Non-compensation of Atlas calorimeters –Cracks and dead material –Boundaries between calorimeters Definition of “truth” –Can apply reco algorithms to MC particle list to obtain MC “jets”. But is this truth? Clustering is different, propagation is different. –Can sum all MC particles in cone around reco jet. Noise. –Want to reject cells with no real energy,but… –Also want to avoid bias. Reject E +300 GeV bias per event!

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10 20-Dec-04 F. Merritt 10 Three Weighting Schemes Being Studied “Pseudo-H1 weighting” [Frank Paige] –Estimates weight for each CaloCell depending on energy density in cell. Independent of Jet energy. Weight by Sampling Layer [Ambreesh Gupta] –Estimates weight for each sampling layer in the calorimeter depending on Jet energy (but not on cell energy). Pisa weights [C. Roda, I. Vivarelli] –Estimates weight for each CaloCell depending on both cell energy and jet energy (and parameterized in terms of Et rather than E).

11 20-Dec-04 F. Merritt 11 “Pseudo-H1 fitting” (Frank Paige) Weight for each cell depends only energy density of that cell and calorimeter type. Cells with larger energy are more probable to have a larger EM fraction, so they get smaller weight This method allows cell energy to be determined BEFORE jet-finding is executed. In the figure, blue is energy at EM scale (uncalibrated), red is reconstructed using H1 weights.

12 20-Dec-04 F. Merritt 12 Resolution is comparable to that of the TDR (0.5/  E). But no noise Ideal cells Note: resolution worsens with higher . Blue is uncalibrated (EM scale) energy Red is after H1 weights.  Fairly uniform energy calibration over three decades of E.

13 20-Dec-04 F. Merritt 13 Comparison with jet-finding applied to topological clusters:

14 20-Dec-04 F. Merritt 14 Sampling Layers EM Cal  LAr calorimeter HAD Cal  Tile+HCAL+FCAL No noise added Calibration weights derived in three eta region - 0.0 - 0.7, 0.7 - 1.5, 1.5 - 2.5, 2.5 - 3.2 The weights have reasonable behavior in all eta region. “Sampling Weights” (Ambreesh Gupta)

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17 20-Dec-04 F. Merritt 17  /E = (97% /  E)  4%  /E = (127% /  E)  0%  /E = (114% /  E)  8%  /E = (68% /  E)  3% Scale & Resolution Sampling Weights

18 20-Dec-04 F. Merritt 18  /E = (75% /  E)  1%  /E = (115% /  E)  3%  /E = (138% /  E)  0%  /E = (271% /  E)  0% Scale & Resolution H1 Style Weights Different definition of truth, compared to those used in deriving the weights

19 20-Dec-04 F. Merritt 19 Jet Calibration/Testing A Jet calibration scheme specifies the following 1.Definition of truth Jet. 2.Treatment of noise - (a)symmetric cut on CaloCell, higher objects, more sophisticated noise algorithm... 3.Input to Jet clustering algorithm - CaloTower, CaloCluster... 4. Type of Jet clustering algorithm - cone (0.7,0.4), kt... 5. Calibration Weights to be applied to the Jet - H1-Style, H1, Sampling...

20 20-Dec-04 F. Merritt 20 Testing a calibration scheme 1. Using the definition of truth specified by the calibration scheme, some basic quantities can be estimated - Linearity of Jet energy scale and error - Change in calibrated Jet resolution (EM scale resolution should be same for different schemes) - Effect on symmetry-asymmetry, skewness of energy distribution - Effect on angle 2. Effect of calibration weights on - samples with different event topology ttbar, dijet - samples with different content of quark and gluon jets 3. Jet energy scale can tested independent of truth definition with the ‘in situ’ samples - Z/  + Jet,W  jj in ttbar sample (can be done in both data and MC) - Hadronic decay of Z in Monte Carlo - bootstrap to higher energies Jet Calibration/Testing

21 20-Dec-04 F. Merritt 21 Different groups/methods working in Jet calibration utilise different information in the detector. This helps in an optimal use of all information and cross checks. In order to compare results, a simple strategy could be to 1. Apply a symmetric noise cut on CaloCell 2. Choose one Jet Algorithm - cone(0.7) 3. Choose one definition of truth Jet - truth particle within the Jet. Calibration weights derived with this will not be the optimal one, but it would be a good strating point to understand differences between algorithms. Comparing Jet calibration schemes

22 20-Dec-04 F. Merritt 22 Ongoing work and plans for next two months (in preparation for Rome) 1.Pisa wieghts are in the process of being put into JetRec for comparison to H1 and sample weighting. 2.Will introduce a top-level calibration selector tool in JetRec that can be switched through jobOpt. 3.Will carry out comparisons in January with the goal of establishing a benchmark calibration by early February. 4.Produce new DC2 weights by mid-February (already in progress; F.P. and S.P.) 5.Extend calibration to different cone sizes (R=0.4 and R=0.7). 6.Plan to write a few standard jet selections to ESD (e.g., R=0.7, R=0.4 cone, Kt) 7.Investigate other improvements in jet-finding and jet calibration if time permits. improved definition of truth. improved noise suppression techniques. more extensive studies of jet-finding with topological clusters. additional parameters in sample weighting.

23 20-Dec-04 F. Merritt 23 New method of studying truth definition (S. Padhi, F. Paige) Select a single jet from dijet production using MC “truth” jets, and propagate only those particles into the detector. This separates energy calibration/resolution from difference in clusterization between truth and reconstruction. Initial results look promising.

24 20-Dec-04 F. Merritt 24 Kyle Cranmer: Local noise suppression using neighboring cell energy:

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27 20-Dec-04 F. Merritt 27 Improving sampling wt’s (A. Gupta) Using sampling weight for each calorimeter layer is not very useful -- large fluctuation in a single layer. But using fraction of energy deposited in EM and HAD have useful information on how jets develops. To make weights use energy fraction information in EM and HAD calorimeter. 25 GeV 100 GeV 400 GeV 1000 GeV Fraction of Jet energy in EM and HAd

28 20-Dec-04 F. Merritt 28 Energy fraction LAr/(LAr+Tile) < 0.67 Energy fraction LAr/(LAr+Tile) > 0.67

29 20-Dec-04 F. Merritt 29 Study variations in calibration for different physics processes (F.P.)

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