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Polaroid jetography an album of jet physics measurements and searches at the ATLAS experiment Caterina Doglioni 1 1 University of Geneva HEP Seminar, University of Virginia - 17/09/13 Introduction Why jets? Large Hadron Collider: quark and

  1. Polaroid jetography an album of jet physics measurements and searches at the ATLAS experiment Caterina Doglioni 1 1 University of Geneva HEP Seminar, University of Virginia - 17/09/13

  2. Introduction Why jets? Large Hadron Collider: quark and gluon ( → jet) factory Use jets for measurements: 1 understand QCD (backgrounds), test reconstruction and calibration performance Use jets for searches: 2 probes for new physics Why jetography? Main message of the day: there’s many ways to make a jet (see G. Salam’s primer) Why polaroid? I only have limited time... This talk: quick snapshots of large ATLAS jet physics program C. Doglioni - 17/09/2013 - UVa Seminar 2 / 48 �

  3. Outline Overview of jet reconstruction : jet finding, calibration, performance Selected ATLAS results on jet physics : measurements and exotics searches Jet energy scale uncertainty 1 Overview of the ATLAS detector Jet resolution 2 Introduction to jets 5 Standard Model jet results Introduction to jet algorithms Jet triggers Jet Algorithms in ATLAS Measurement of jet properties 3 Jet substructure Jets, dijets and multijets Introduction 6 Searches with jets Jet substructure performance Dijet analysis 4 Jet performance Photon+jet analysis Jet calibration Mono-X analyses for dark matter C. Doglioni - 17/09/2013 - UVa Seminar 3 / 48 �

  4. The ATLAS detector C. Doglioni - 17/09/2013 - UVa Seminar 4 / 48 �

  5. Overview of the ATLAS detector The ATLAS Detector in 2012 Excellent performance of the LHC and of the ATLAS experiment: 5 and 21 fb − 1 of pp data recorded in the 7 and 8 TeV runs + heavy ion / p − P b data (not covered here) 263 papers published, 530 public notes and counting 50 Peak interactions per crossing 45 s = 7 TeV s = 7 TeV s = 8 TeV ATLAS 40 Online Luminosity 35 30 2012 challenge: high luminosity 25 Multiple interactions per bunch 20 15 crossing 10 → optimize trigger, object 5 reconstruction 0 Apr Apr Apr Jan Jul Oct Jan Jul Oct Jan Jul Oct Month in 2010 Month in 2011 Month in 2012 C. Doglioni - 17/09/2013 - UVa Seminar 5 / 48 �

  6. Overview of the ATLAS detector The ATLAS Detector For the measurements described in this talk: inner detector, calorimeter system C. Doglioni - 17/09/2013 - UVa Seminar 6 / 48 �

  7. Overview of the ATLAS detector The ATLAS inner detector and calorimeters Inner detector Pixel detectors, semiconductor tracker (SCT), transition radiation tracker ≈ 87M readout channels, coverage up to | η | < 2.5 Immersed in 2T magnetic field from solenoid Electromagnetic and hadronic calorimeters Subsystem technology and granularity ↔ shower characteristics transverse and longitudinal sampling very fine granularity: ≈ 200 000 readout cells up to | η | < 4.9 Energy deposits grouped in noise-suppressed 3D topological clusters noise definition includes pile-up and electronic noise C. Doglioni - 17/09/2013 - UVa Seminar 7 / 48 �

  8. Jet algorithms: basics C. Doglioni - 17/09/2013 - UVa Seminar 8 / 48 �

  9. Introduction to jets – Introduction to jet algorithms Chaos from order, order from chaos? A high-p T dijet event: how we see it ...from the back of an envelope... C. Doglioni - 17/09/2013 - UVa Seminar 9 / 48 �

  10. Introduction to jets – Introduction to jet algorithms Chaos from order, order from chaos? A high-p T dijet event: how we see it ...according to QCD from a MC generator... I cheated: this is a semileptonic t ¯ t event from MCViz, but you get the idea C. Doglioni - 17/09/2013 - UVa Seminar 9 / 48 �

  11. Introduction to jets – Introduction to jet algorithms Chaos from order, order from chaos? A high-p T dijet event: how we see it ...in the ATLAS calorimeter... Note: some ’cleaning’ already performed: ATLAS topological clustering algorithm C. Doglioni - 17/09/2013 - UVa Seminar 9 / 48 �

  12. Introduction to jets – Introduction to jet algorithms Chaos from order, order from chaos? A high-p T dijet event: how we see it ...after applying a jet algorithm . Need algorithms to define jets out of underlying constituents C. Doglioni - 17/09/2013 - UVa Seminar 9 / 48 �

  13. Introduction to jets – Introduction to jet algorithms Jet algorithms: basics Goal: kinematics of jet ↔ kinematics of underlying physics objects Use a jet algorithm to cluster objects into a jet Apply same jet definition to objects on different levels: Partons 1 Particles 2 → Truth Jets (only particles from the hard scattering) Calorimeter objects 3 (ATLAS: Towers, Topoclusters) → Reconstructed Jets Tracks 4 → Track Jets From M. Cacciari, MPI@LHC08 C. Doglioni - 17/09/2013 - UVa Seminar 10 / 48 �

  14. Introduction to jets – Introduction to jet algorithms Jet algorithms: basics Goal: kinematics of jet ↔ kinematics of underlying physics objects Use a jet algorithm to cluster objects into a jet Apply same jet definition to objects on different levels: Partons 1 Particles 2 → Truth Jets (only particles from the hard scattering) Calorimeter objects 3 (ATLAS: Towers, Topoclusters) → Reconstructed Jets Tracks 4 From G. Salam, MCNet School 2008 → Track Jets C. Doglioni - 17/09/2013 - UVa Seminar 10 / 48 �

  15. Introduction to jets – Introduction to jet algorithms Wishlist for jet finding algorithms No right jet algorithm Different processes ↔ different algorithms / parameters (we’ll see more of this later...) Requirements: 1. Theoretically well behaved → no α s dependence of jet configuration: Infrared safety Collinear safety 2. Computationally feasible → fast 3. Detector independent C. Doglioni - 17/09/2013 - UVa Seminar 11 / 48 �

  16. Introduction to jets – Introduction to jet algorithms More safety warnings Crucial to analyse data with infrared / collinear safe jet algorithm! Theory matters: From G. Salam, MCNet School 08 C. Doglioni - 17/09/2013 - UVa Seminar 12 / 48 �

  17. Introduction to jets – Introduction to jet algorithms Implementation of jet algorithms Goal: kinematics of jet ↔ kinematics of underlying physics objects Use a jet algorithm to cluster objects into a jet Basic algorithm: event display + physicist “Everyone knows a jet when they see it” Note: don’t try this at home when the LHC is running ...but what is really needed for communicating results: full specification of algorithm and parameters → how to group objects 1 recombination scheme → how to merge objects characteristics 2 treatment of overlapping jets (if any) → how to avoid double counting 3 C. Doglioni - 17/09/2013 - UVa Seminar 13 / 48 �

  18. Introduction to jets – Jet Algorithms in ATLAS Jet algorithms available in ATLAS Cone-based algorithms Sequential recombination algorithms Cone in y − φ space around object Group objects based on minimum momentum vector relative distance Jet = objects in cone Jet = grouped objects Available on the (ATLAS) market: Available on the (ATLAS) market: ATLAS Cone unsafe! K t Seedless Infrared Safe Cone (SISCone) Cambridge-Aachen Anti- K t What algorithms for data? From G. Salam, MCNet School 2008 C. Doglioni - 17/09/2013 - UVa Seminar 14 / 48 �

  19. Introduction to jets – Jet Algorithms in ATLAS Sequential recombination algorithms ( k t -like) Algorithm specification: k t T,i ) ∆ R 2 Idea: d i,j = min ( p 2 T,i , p 2 ; D 2 p p a) b) d i,Beam = p 2 d = d T min A,B T T,i D : algorithm parameter ( ≈ weight for d = d min C,Beam angular distance ∆ R ) jet Iterate: A B C AB C y y For every pair of objects i, j calculate 1 d = d d min = min ( d i,j , d i,beam ) min AB, Beam p p c) d) If d min = d i,j recombine objects T T 2 Else i is a jet, remove it from list a Recombination starts from soft objects jet a ATLAS default: inclusive algorithm AB C AB C y y C. Doglioni - 17/09/2013 - UVa Seminar 15 / 48 �

  20. Introduction to jets – Jet Algorithms in ATLAS Sequential recombination algorithms ( k t -like) Algorithm specification: Cambridge- Idea: Aachen d i,j = ∆ R 2 p p a) b) ; d i,Beam = 1 d = d T T min A,B D 2 d = 1 D : algorithm parameter min Iterate: jet For every pair of objects i, j calculate 1 A B C AB C y y d min = min ( d i,j , d i,beam ) If d min = d i,j recombine objects 2 p p c) d) Else i is a jet, remove it from list a d = 1 T T min Distance-based recombination jet a ATLAS default: inclusive algorithm AB C AB C y y C. Doglioni - 17/09/2013 - UVa Seminar 16 / 48 �

  21. Introduction to jets – Jet Algorithms in ATLAS Sequential recombination algorithms ( k t -like) Algorithm specification: Anti- k t ) ∆ R 2 1 1 Idea: d i,j = min ( , ; p 2 p 2 D 2 T,i T,i p p a) b) 1 d = d T min A,B T d i,Beam = d = d min AB,Beam p 2 T,i D : algorithm parameter jet Iterate: A B C AB C y y For every pair of objects i, j calculate 1 d min = min ( d i,j , d i,beam ) p p c) d) If d min = d i,j recombine objects T T 2 Else i is a jet, remove it from list a d = d min C, Beam Recombination starts from hard objects jet a ATLAS default: inclusive algorithm AB C AB C y y C. Doglioni - 17/09/2013 - UVa Seminar 17 / 48 �

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