Resonance Searches with an Updated Top Tagger G. Kasieczka, T. - PowerPoint PPT Presentation

Resonance Searches with an Updated Top Tagger G. Kasieczka, T. Plehn, T.S., T. Strebler, G. P. Salam [arXiv:1503.05921] Torben Schell Institute for Theoretical Physics, Heidelberg University Pheno 2015 May 4, 2015 T. Schell (ITP – U Heidelberg) HEPTopTagging Pheno 2015 1 / 12

HEPTopTagger HEPTopTagging reconstruction of boosted hadronic tops 3 bjj R Δ 3 10 collimated decay products → fat jets 2 → reduced combinatorial problems 2 10 1 10 SM: number of top quarks vs. collimation 0 1 0 200 400 600 P [GeV] [Plehn et al. arXiv:1006.2833] T substructure analysis based on subjet masses T. Schell (ITP – U Heidelberg) HEPTopTagging Pheno 2015 2 / 12

HEPTopTagger HEPTopTagger – Algorithm fat jet : C/A R = 1 . 5, p T > 200 GeV 0 hard substructures : 1 mass drop f drop = 0 . 8, m i < m sub = 30 GeV filtering : 2 filter triplets of hard substructures → 3 jets ( j 1 , j 2 , j 3 ) 150 GeV < m 123 < 200 GeV m ij mass plane cuts : 0 . 85 m W m 123 < 1 . 15 m W m t < 3 m t m 23 ≈ m W : 0 . 2 < arctan m 13 m 23 m 12 < 1 . 3; else m 123 > 0 . 35 triplet selection : choose triplet closest to m t 4 consistency: p ( tag ) > 200 GeV 5 T [arXiv:1006.2833] T. Schell (ITP – U Heidelberg) HEPTopTagging Pheno 2015 3 / 12

HTT Resonance Reconstruction Resonance Reconstruction Heavy neutral Z ′ –gauge bosons decaying to top quarks at LHC run II Event generation: Pythia8, LHC √ s = 13 TeV signal: Z ′ → t h ¯ t h , m Z ′ = 1500 GeV, Γ( Z ′ ) = 65 GeV background: QCD-dijet & t h ¯ t h , both p T > 400 GeV no detector simulation Event selection: 2 hardest C/A, R = 1 . 5 fat jets ( FastJet ) require p T , fat > 400 GeV and | y fat | < 2 . 5 T. Schell (ITP – U Heidelberg) HEPTopTagging Pheno 2015 4 / 12

HTT Resonance Reconstruction Decay Kinematics HTT working point + m tt window Z ′ → t ¯ t ¯ t t QCD 10 5 (1.76 pb) 8 · 10 6 (1.93 nb) 10 5 generator level 6 . 7 · 10 6 (1.62 nb) ≥ 2 fat jets with p T > 400 GeV and | y | < 2 . 5 69142 85284 (1.50 pb) hardest 2 fat jets HTT [JHEP1010] tagged 9679 11706 (0.21 pb) 4426 (1.07 pb) m tt ∈ [1200 , 1600] GeV 7031 2817 (0.05 pb) 978 (0.24 pb) include additional kinematic variables in BDT analysis decay kinematics well described by { m tt , p T , j } σ 1 d B ε 1 / σ ∆ 0.15 d| y| HTT[JHEP1010] m tt ∆ m + | y| 5 tt 10 m + p tt T,i ∆ m + p + | y| Z' tt T,i 0.1 t t QCD 4 10 0.05 3 10 0 0 2 4 0 0.05 0.1 0.15 0.2 ε ∆ | y| S T. Schell (ITP – U Heidelberg) HEPTopTagging Pheno 2015 5 / 12

HTT Resonance Reconstruction Final State Radiation W HTT reconstructs on–shell tops → misses final state radiation b → sizeable tail in m tt distribution Z ′ W consider HTT tagged fat jets b σ d 1 σ d 1 σ dm GeV 0.15 σ dm GeV tt ff Z' Z' 0.1 t t t t QCD Filtering: 0.1 QCD R=0.3, N=5 0.05 0.05 0 0 500 1000 1500 2000 500 1000 1500 2000 m [GeV] m [GeV] tt ff T. Schell (ITP – U Heidelberg) HEPTopTagging Pheno 2015 6 / 12

HTT Resonance Reconstruction Final State Radiation & Variable Masses filtered fat jet momenta instead of reconstructed tops → no improvement add filtered fat jet information: { m tt , p T , j , m (filt) , p (filt) T , f j } ff going beyond HTT working point: variable masses in HTT cuts + corresponing variables in BDT { m tt , p T , j , m (filt) , p (filt) T , f j , m min rec , m max rec , f max rec } , f rec = min | ( m ij / m rec ) / ( m W / m t ) − 1 | ff ij B B ε ε HTT[JHEP1010] 1 / 1 / HTT[JHEP1010] decay kinematics (2) 5 filtered fat jets (3) 10 filtered fat jets (3) variable masses (4) 5 10 4 10 QCD 3 10 4 10 2 10 3 10 t t 10 0 0.05 0.1 0.15 0.2 0 0.05 0.1 0.15 0.2 0.25 ε ε S S T. Schell (ITP – U Heidelberg) HEPTopTagging Pheno 2015 7 / 12

HTT Resonance Reconstruction OptimalR Mode there is an optimal fat jet size R opt reduce R until leaving top mass plateau | m ( R ) ( R opt ) ( R opt ) rec − m | < 0 . 2 m → R opt rec rec estimate as R (calc) → additional variable R opt − R (calc) opt opt OptimalR { m tt , p T , j , m (filt) , p (filt) rec , max( R opt − R (calc) T , f j , m min rec , m max rec , f max ) } opt ff bjj B R ε 1 / s = 13 TeV 5 10 2 4 10 1 HTT[JHEP1010] filtered fat jets (3) variable masses (4) 3 10 optimalR (6) p >200, 400, 600 GeV combined T 0 200 400 600 800 1000 0 0.05 0.1 0.15 0.2 0.25 ε p S T,filt T. Schell (ITP – U Heidelberg) HEPTopTagging Pheno 2015 8 / 12

HTT Resonance Reconstruction N –Subjettiness optimalR working point B ε 1 / s = 13 TeV m rec ∈ [150 , 200] GeV , f rec < 0 . 175 , 5 10 R opt − R (calc) < 0 . 3 opt two different filterings and BDT analyses 4 10 passed: R filt = 0 . 3, N filt = 3 HTT[JHEP1010] filtered fat jets (3) rejected: R filt = 0 . 2, N filt = 5 variable masses (4) optimalR (6) 3 10 N-subjettiness (8) 0 0.05 0.1 0.15 0.2 0.25 N –Subjettiness [Thaler, Van Tilburg] ε S 1 � τ N = p T , k min (∆ R 1 , k , · · · , ∆ R N , k ) � R 0 k p T , k k rec , R opt − R (calc) , τ f i , N , τ (filt) BDTs: { m tt , m ff , p T , t 1 , p T , t 2 , p T , f 1 , p T , f 2 , m min rec , m max rec , f max f i , N } opt T. Schell (ITP – U Heidelberg) HEPTopTagging Pheno 2015 9 / 12

HTT Resonance Reconstruction Qjets [Ellis, Hornig, Roy, Krohn, Schwartz] deterministic clustering → set of weighted histories B ε 1 / s = 13 TeV 5 each possible merging ( ij ) gets a weight 10 � − α d ij − d min � ω ( α ) ij = exp 4 10 ij d min ij 3 clustering history weight 10 HTT[JHEP1010] filtered fat jets (3) �� α � � � − d ij − d min variable masses (4) Ω ( α ) = � ω ( α ) ij = exp 2 optimalR (6) 10 ij d min N-subjettiness (8) ij Qjets (11, 0.1x0.1 cells) mergings mergings 0 0.2 0.4 0.6 ε S use leading tagged Qjets history + statistical information from tagged histories { m tt , m ff , p T , t 1 , p T , t 2 , p T , f 1 , p T , f 2 , m min rec , m max rec , f max rec , R opt − R (calc) , { τ N } , ε min Qjets , { m Qjets rec } } opt T. Schell (ITP – U Heidelberg) HEPTopTagging Pheno 2015 10 / 12

HTT Resonance Reconstruction Comparison Event Deconstruction [Soper, Spannowsky] likelihoods based on up to 9 C/A microjets per fat jet ( R = 0 . 2 and p T > 10 GeV) soft and/or collinear approximation of QCD event classification based on likelihood ratio B ε 1 / s = 14 TeV 5 10 4 10 3 10 ED[PRD89] HTT[JHEP1010] filtered fat jets (3) variable masses (4) 2 optimalR (6) 10 N-subjettiness (8) Qjets (11, 0.1x0.1 cells) 0 0.2 0.4 0.6 ε S T. Schell (ITP – U Heidelberg) HEPTopTagging Pheno 2015 11 / 12

Summary Summary Resonance search with an updated HEPTopTagger and additional kinematic variables: fat jet kinematics to account for final state radiation algorithmically optimized size of used fat jets and its prediction (optimalR) N–subjettiness probing more general substructures inside the fat jet Qjets with a global picture of the most likely clustering histories giving a top tag → factor 30 improvement compared to the previous HEPTopTagger version T. Schell (ITP – U Heidelberg) HEPTopTagging Pheno 2015 12 / 12