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Jet Reconstruction and Calibration in Athena US ATLAS Software Workshop BNL, 27/08/03 Ambreesh Gupta, for the JetRec Group University of Chicago Outline: Jet Reconstruction Setup and Algorithms Jet Calibration Some Highlights from JetRec Meeting in Barcelona.
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Jet Physics at Hadron Colliders Final state partons produce a collimated jet of particles. An ideal algorithm will map jets with final state partons. Jet algorithms sensitive to experimental and theoretical uncertainty.
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The Steps of Jet Measurement Jet Measurement can be broadly divided in three steps Jet Reconstruction Energy Calibration Flavor Identification Energy deposit in the calorimeter is clustered by Jet reconstruction algorithms, e.g Cone and Kt. Energy of a Jet is calibrated for non-compensation, dead material, magnetic field, etc. b-tag, -tag, etc.
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Jet Algorithms Jet algorithms have two steps Clustering hadrons, calorimeter tower etc., for “nearness” Nearness in Angle => Cone Algorithm Nearness in Momentum => Kt Algorithm Recombination Scheme is the momentum addition rule for the set of particles that have been identified with a jet. Cone jetK T jet 4-vector addition Snow mass scheme...
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Jet Reconstruction Packages CalCell CaloCluster Tracks CaloTower Many Kind of Input Data MC Truth Many Kind of Algorithms Cone Kt Cluster ….. ProtoJet Energy Flow Abstract The Inputs For Algs ProtoJet Class:. Can be created for different sub system.. Homogenous.
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Writing Multiple Jets to the N-tuple CaloTower ProtoJet Truth Particle ProtoJet Cone Algorithm Seed less Cone Pre-Cluster Kt AlgorithmSplit-MergeParticleInJet CBNT All algorithms can be configured to run in the same job Multiple algorithm output can be stored with different names
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Jet Samples Used The results reported use the following two samples - Multi-jet sample produced with different Pt cut values ( 35,70,.., 540 GeV) -SUSY data challenge sample. Run 002315.
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Cone KTKT Seed Less Cone Number of Jets Comparing Jet Multiplicity Mean = 6.0 Mean = 5.6 Mean = 5.7
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Transverse Energy Cone KTKT Seed Less Cone Comparing Jet Transverse Energy Mean = 114 GeV Mean = 118 GeV Mean = 126 GeV
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Reconstructed jet energy In bins if true energy at eight energy points. Good fits to gaussians.
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Cone Kt Cluster ….. Jet Jet Reconstruction Algorithms Energy Correction Algorithms Sampling Based Correction H1 Style Correction Jet Jet Energy Calibration To account for non-compensation, dead material etc., - H1 scheme (Implemented by Frank Paige) - Sampling based corrections (in development)
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Jet algorithms work with abstractions like ProtoJet. In order to do energy correction, the navigating the composition of Jet needed. Jet PJet Tower Cell JetToken PJetToken TowerToken CellToken Navigating Composite Objects
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H1 Calibration H1 Strategy: EM showers are much denser than hadronic ones. Weight high E cell with EM scale and low E cell with hadronic scale. H1 weights derived from di-jet samples. Same weights improve missing Et resolution.
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Jet Calibration: Further developments Further studies to develop tools for calculating and storing weights. - requires reading large number (~100K) of jets - calculate weights (interface to a minimizer) - store the weights to be accessed for calibration. Sampling based weights - calculated in different eta region of the detector. - first set of weights obtained. Not yet in accessible in Athena.
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Sampling based Correction
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Evolution of JetRec: Barcelona Meeting Since, the last JetRec working group meeting in Tucson (Aug 2001), a lot of progress made in Jet reconstruction software. Another working group meeting was held in Barcelona ( July 2003 ) to review the RTF recommendations. Attended by Present: Frank Merritt, Tom LeCompte, Peter Loch, Srini Rajagopalan, Martine Bosman. By Phone: F. Paige, D. Cavalli, S. Resconi, D. Rousseau, A. Gupta, S. George, P. Calafiura. Initial draft of meeting report can be found at JetRec meetings minutes web-page ( date: 30 th of July)
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Barcelona Meeting: Highlights EnergyCluster class will replace the existing ProtoJet class. The nature of abstraction remains the same, as far as Jet algorithms are concerned, but it conforms to the general Athena reconstruction setup and hence will utilize common services. The Jet Data Objects will conform to the general Athena scheme to navigate composite objects. Associating information with Jets, e.g, tracks in jet, will be provided by extending the present Jet class interface with the help of a templated method for the type of association requested.
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Summary Jet Reconstruction packages are in fully functional state. There have been various user feedback using dc1 dataset – bugs hunted and fixed. With the availability of “noise” and “pileup”, a more realistic test of jet algorithms possible. RTF recommendations reviewed at Barcelona meeting. Structural changes proposed to conform with general Athena reconstruction setup.
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