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Michel Lefebvre, 2007/03/02Optimal Jet Finder1 Optimal Jet Finder work in progress Physics and Astronomy University of Victoria British Columbia, Canada.

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Presentation on theme: "Michel Lefebvre, 2007/03/02Optimal Jet Finder1 Optimal Jet Finder work in progress Physics and Astronomy University of Victoria British Columbia, Canada."— Presentation transcript:

1 Michel Lefebvre, 2007/03/02Optimal Jet Finder1 Optimal Jet Finder work in progress Physics and Astronomy University of Victoria British Columbia, Canada LAPP, 02 March 2007 Michel Lefebvre Laboratoire d’Annecy-le- vieux de physique des particules, France

2 Michel Lefebvre, 2007/03/02Optimal Jet Finder2 Jets: from partons to detector signatures M. Dobbs and J.B. Hansen, Comput. Phys. Commun. 134 (2001) 41. QCD partons Jets of hadons Detector signals

3 Michel Lefebvre, 2007/03/02Optimal Jet Finder3 Infrared and collinear safety hep-ex/0005012 Quark and gluon jets (identified to partons) can be compared to detector jets, if jet algorithms respect collinear and infrared safety (Sterman & Weinberg, 1977)

4 Michel Lefebvre, 2007/03/02Optimal Jet Finder4 Optimal Jet Finder (OJF) Documentation  proposed by Fyodor Tkachov  short introduction: Phys. Rev. Lett. 91, 061801 (2003)  authors webpage (with links to source code, etc.) http://www.inr.ac.ru/~ftkachov/projects/jets/welcome.html Implementation in Athena in progress  Rolf Seuster, Damir Lelas  Discussions with Peter Loch and others ongoing

5 Michel Lefebvre, 2007/03/02Optimal Jet Finder5 Optimal Jet Finder (OJF) HEP event: list of particles (partons hadrons calorimeter cells towers preclusters) result: list of jets recombination matrix the 4-momentum q j of the j-th jet expressed by 4-momenta p a of the particles E. Jankowski

6 Michel Lefebvre, 2007/03/02Optimal Jet Finder6 Recombination matrix i.e. no more than 100% of each particle is assigned to jets the fraction of the energy of the a-th particle that does not go into any jet the 4-momentum q j of the j-th jet expressed by 4-momenta p a of the particles (a=1,2,...,n parts ) the fraction of the energy of the a-th particle can be positive only E. Jankowski Fixes the jet configuration

7 Michel Lefebvre, 2007/03/02Optimal Jet Finder7 Soft energy and fuzzyness Cylindrical kinematics fuzziness transverse energy left outside the jets

8 Michel Lefebvre, 2007/03/02Optimal Jet Finder8 Final jet configuration The recombination matrix is found by minimizing R weights the relative contributions For cylindrical kinematics

9 Michel Lefebvre, 2007/03/02Optimal Jet Finder9 Algorithm: fixed n jets case 1.Start with some (random) value of the recombination matrix z aj and minimize  with respect to z aj for some given p a 2.Repeat this a few times, each time starting with a different (random) z aj 3.The value of z aj that corresponds to the smallest of the minima of  is the final jet configuration for the required (fixed) number of jets

10 Michel Lefebvre, 2007/03/02Optimal Jet Finder10 Algorithm: general case If the number of jets is to be determined 1.Start with n jets = 1 2.Find the corresponding n jets configuration 3.Check if  <  cut is fulfilled; if yes stop here 4.If not, increase n jets by one and repeat at 2 The parameter  cut is some small positive number, analogous to the jet resolution parameter of conventional recombination algorithms

11 Michel Lefebvre, 2007/03/02Optimal Jet Finder11 A few comments OJF is infrared and collinear safe the authors claim it is based on an optimal jet definition that solves the problem of jet definition in general conventional jet algorithms have z aj equal to 0 or 1, i.e. a particle either entirely belongs to some jet or does not belong to that jet at all  Hadronization is always effect of interaction of at least two hard partons evolving into two jets, so some hadrons that emerge in this process can belong partially to both jets (continuous z aj for OJF) need to assess the robustness of the algorithm  small changes in input particle kinematics  pileup

12 Michel Lefebvre, 2007/03/02Optimal Jet Finder12 Optimal Jet Finder (OJF) OJF evaluation ongoing  comparison with other jet algorithms speed issues  observables number of jets kinematics distributions event shape variables  robustness In the following slides, some results I have recently obtained looking at fully hadronic ttbar events  an “excuse” to play with OJF

13 Michel Lefebvre, 2007/03/02Optimal Jet Finder13 Samples Generated by Damir Lelas Sample 5204  MC@NLO ttbar  6jets, full simulation, Athena 11.0.42 AOD produced using Athena 12.0.5  1050 events, E T > 7 GeV filter on jets  ParticleJetContainer each produced which its own H1 weights –Cone4TowerParticleJetsCone4TopoParticleJets –ConeTowerParticleJetsConeTopoParticleJets –Kt4TowerParticleJetsKt4TopoParticleJets –Kt6TowerParticleJetsKt6TopoParticleJets  JetCollection using OJF no proper H1 weights available: use Cone weights number of jets fixed to 6 R parameter set to 0.7 –FuzzyTowerJetsFuzzyClusterJets

14 Michel Lefebvre, 2007/03/02Optimal Jet Finder14 Jet-parton matching Study events with “true” jet hypothesis Matching criteria  For each parton, look for a matching jet restrict search in a region limited by  R max = 0.2 keep the closest jet in this region  Demand that a jet be matched only once matching efficiencies depends on interparton distances –same top combinations  R(u-b)  = 2.220 ± 0.002  R(ubar-bbar)  = 2.219 ± 0.002  R(u-dbar)  = 2.008 ± 0.002  R(ubar-d)  = 2.010 ± 0.002  R(dbar-b)  = 2.037 ± 0.002  R(d-bbar)  = 2.036 ± 0.002 –other 9 combinations  R   2.40

15 Michel Lefebvre, 2007/03/02Optimal Jet Finder15 Parton matching efficiencies no p T jet cuts applied

16 Michel Lefebvre, 2007/03/02Optimal Jet Finder16 Normalized distributions: towers corresponds to events after requiring at least 6 jets in |eta| < 3

17 Michel Lefebvre, 2007/03/02Optimal Jet Finder17 Normalized distributions: topo clusters

18 Michel Lefebvre, 2007/03/02Optimal Jet Finder18 Normalized p T distributions: towers

19 Michel Lefebvre, 2007/03/02Optimal Jet Finder19 Normalized E distributions: towers

20 Michel Lefebvre, 2007/03/02Optimal Jet Finder20 Normalized p T distributions: topo clusters

21 Michel Lefebvre, 2007/03/02Optimal Jet Finder21 Normalized E distributions: topo clusters

22 Michel Lefebvre, 2007/03/02Optimal Jet Finder22 Comments Very encouraging results for OFJ  here fixed number of jet mode!  should try with number of jets not fixed adjust  parameter  more events would allow mass plots requires 6 matched partons 1 to 1  more systematic tests being discussed compare with particle truth need larger samples explore various OJF parameter values

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