Joint ICTP-INFN-SISSA Conference: Topical Issues in LHC Physics. 29 June - 2 July, Jet Physics at the LHC

Transcription

1 Joint ICTP-INFN-SISSA Conference: Topical Issues in LHC Physics 29 June - 2 July, 2009 Jet Physics at the LHC Gavin SALAM LPTHE, Universites Paris VI, France

2 Towards Jetography Gavin Salam LPTHE, CNRS and UPMC (Univ. Paris 6) Based on work with Jon Butterworth, Matteo Cacciari, Mrinal Dasgupta, Adam Davison, Lorenzo Magnea, Juan Rojo, Mathieu Rubin & Gregory Soyez Topical Issues in LHC Physics Joint ICTP-INFN-SISSA Conference, Trieste, Italy, June 2009

3 Towards Jetography, G. Salam (p. 2) Introduction Parton fragmentation quark Gluon emission: de dθ α s E θ 1 At low scales: α s 1

4 Towards Jetography, G. Salam (p. 2) Introduction Parton fragmentation quark θ gluon Gluon emission: de dθ α s E θ 1 At low scales: α s 1

5 Towards Jetography, G. Salam (p. 2) Introduction Parton fragmentation quark Gluon emission: de dθ α s E θ 1 At low scales: α s 1

6 Towards Jetography, G. Salam (p. 2) Introduction Parton fragmentation quark non perturbative hadronisation Gluon emission: de dθ α s E θ 1 At low scales: α s 1

7 Towards Jetography, G. Salam (p. 2) Introduction Parton fragmentation quark non perturbative hadronisation π + K L π 0 K + π Gluon emission: de dθ α s E θ 1 At low scales: α s 1

8 Towards Jetography, G. Salam (p. 2) Introduction Parton fragmentation quark non perturbative hadronisation π + K L π 0 K + π Gluon emission: de dθ α s E θ 1 At low scales: α s 1 This is a jet

9 Towards Jetography, G. Salam (p. 3) Introduction Seeing v. defining jets Jetsarewhatwesee. Clearly(?) 2 jets here How many jets do you see? Do you really want to ask yourself this question for 10 9 events?

10 Towards Jetography, G. Salam (p. 3) Introduction Seeing v. defining jets q q Jetsarewhatwesee. Clearly(?) 2 jets here How many jets do you see? Do you really want to ask yourself this question for 10 9 events?

11 Towards Jetography, G. Salam (p. 3) Introduction Seeing v. defining jets Jetsarewhatwesee. Clearly(?) 2 jets here How many jets do you see? Do you really want to ask yourself this question for 10 9 events?

12 Towards Jetography, G. Salam (p. 3) Introduction Seeing v. defining jets Jetsarewhatwesee. Clearly(?) 2 jets here How many jets do you see? Do you really want to ask yourself this question for 10 9 events?

13 Towards Jetography, G. Salam (p. 3) Introduction Seeing v. defining jets Jetsarewhatwesee. Clearly(?) 2 jets here How many jets do you see? Do you really want to ask yourself this question for 10 9 events?

14 Towards Jetography, G. Salam (p. 3) Introduction Seeing v. defining jets Jetsarewhatwesee. Clearly(?) 2 jets here How many jets do you see? Do you really want to ask yourself this question for 10 9 events?

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16 Towards Jetography, G. Salam (p. 5) Introduction Jets as projections p π π φ K LO partons NLO partons parton shower hadron level Jet Def n Jet Def n Jet Def n Jet Def n jet 1 jet 2 jet 1 jet 2 jet 1 jet 2 jet 1 jet 2 Projection to jets should be resilient to QCD effects

17 Towards Jetography, G. Salam (p. 6) Introduction QCD jets flowchart Jet (definitions) provide central link between expt., theory and theory And jets are an input to almost all analyses

18 Towards Jetography, G. Salam (p. 6) Introduction QCD jets flowchart Jet (definitions) provide central link between expt., theory and theory And jets are an input to almost all analyses

19 Towards Jetography, G. Salam (p. 7) Two broad classes What jet algorithms are out there? 2 broad classes: 1. sequential recombination bottom up, e.g. k t, preferred by many theorists 2. cone type top down, preferred by many experimenters

20 Towards Jetography, G. Salam (p. 8) Two broad classes Sequential recombination algorithms k t algorithm Catani, Dokshizter, Olsson, Seymour, Turnock, Webber Find smallest of all d ij =min(kti 2, k2 tj )ΔR2 ij /R2 and d ib = ki 2 Recombine i, j (if ib: i jt) jet) Repeat Bottom-up jets: Sequential recombination Ellis, Soper 93 (attempt to invert QCD NB: hadron branching) collider variables ΔR 2 +(yy i y j ) 2 ij =(φ i φ j ) 2 E +pp zi E i pp zi +p rapidity y zi i = 1 2 ln E i ΔR ij is boost invariant angle R sets minimal interjet angle

21 Towards Jetography, G. Salam (p. 8) Two broad classes Sequential recombination algorithms k t algorithm Catani, Dokshizter, Olsson, Seymour, Turnock, Webber Ellis, Soper 93 Find smallest of all d ij =min(k 2 ti, k2 tj )ΔR2 ij /R2 and d ib = k 2 i Recombine i, j (if ib: i jet) Repeat NB: hadron collider variables ΔR 2 ij =(φ i φ j ) 2 +(y i y j ) 2 rapidity y i = 1 2 ln E i +p zi E i p zi ΔR ij is boost invariant angle R sets minimal interjet angle

22 Towards Jetography, G. Salam (p. 8) Two broad classes Sequential recombination algorithms k t algorithm Catani, Dokshizter, Olsson, Seymour, Turnock, Webber Ellis, Soper 93 Find smallest of all d ij =min(k 2 ti, k2 tj )ΔR2 ij /R2 and d ib = k 2 i Recombine i, j (if ib: i jet) Repeat NB: hadron collider variables ΔR 2 ij =(φ i φ j ) 2 +(y i y j ) 2 rapidity y i = 1 2 ln E i +p zi E i p zi ΔR ij is boost invariant angle R sets minimal interjet angle

23 Towards Jetography, G. Salam (p. 8) Two broad classes Sequential recombination algorithms k t algorithm Catani, Dokshizter, Olsson, Seymour, Turnock, Webber Ellis, Soper 93 Find smallest of all d ij =min(k 2 ti, k2 tj )ΔR2 ij /R2 and d ib = k 2 i Recombine i, j (if ib: i jet) Repeat NB: hadron collider variables ΔR 2 ij =(φ i φ j ) 2 +(y i y j ) 2 rapidity y i = 1 2 ln E i +p zi E i p zi ΔR ij is boost invariant angle R sets minimal interjet angle

24 Towards Jetography, G. Salam (p. 8) Two broad classes Sequential recombination algorithms k t algorithm Catani, Dokshizter, Olsson, Seymour, Turnock, Webber Ellis, Soper 93 Find smallest of all d ij =min(k 2 ti, k2 tj )ΔR2 ij /R2 and d ib = k 2 i Recombine i, j (if ib: i jet) Repeat NB: hadron collider variables ΔR 2 ij =(φ i φ j ) 2 +(y i y j ) 2 rapidity y i = 1 2 ln E i +p zi E i p zi ΔR ij is boost invariant angle R sets minimal interjet angle

25 Towards Jetography, G. Salam (p. 8) Two broad classes Sequential recombination algorithms k t algorithm Catani, Dokshizter, Olsson, Seymour, Turnock, Webber Ellis, Soper 93 Find smallest of all d ij =min(k 2 ti, k2 tj )ΔR2 ij /R2 and d ib = k 2 i Recombine i, j (if ib: i jet) Repeat NB: hadron collider variables ΔR 2 ij =(φ i φ j ) 2 +(y i y j ) 2 rapidity y i = 1 2 ln E i +p zi E i p zi ΔR ij is boost invariant angle R sets minimal interjet angle

26 Towards Jetography, G. Salam (p. 8) Two broad classes Sequential recombination algorithms k t algorithm Catani, Dokshizter, Olsson, Seymour, Turnock, Webber Ellis, Soper 93 Find smallest of all d ij =min(k 2 ti, k2 tj )ΔR2 ij /R2 and d ib = k 2 i Recombine i, j (if ib: i jet) Repeat NB: hadron collider variables ΔR 2 ij =(φ i φ j ) 2 +(y i y j ) 2 rapidity y i = 1 2 ln E i +p zi E i p zi ΔR ij is boost invariant angle R sets minimal interjet angle

27 Towards Jetography, G. Salam (p. 8) Two broad classes Sequential recombination algorithms k t algorithm Catani, Dokshizter, Olsson, Seymour, Turnock, Webber Ellis, Soper 93 Find smallest of all d ij =min(k 2 ti, k2 tj )ΔR2 ij /R2 and d ib = k 2 i Recombine i, j (if ib: i jet) Repeat Δ R ij > R NB: hadron collider variables ΔR 2 ij =(φ i φ j ) 2 +(y i y j ) 2 rapidity y i = 1 2 ln E i +p zi E i p zi ΔR ij is boost invariant angle R sets minimal interjet angle

28 Towards Jetography, G. Salam (p. 8) Two broad classes Sequential recombination algorithms k t algorithm Catani, Dokshizter, Olsson, Seymour, Turnock, Webber Ellis, Soper 93 Find smallest of all d ij =min(k 2 ti, k2 tj )ΔR2 ij /R2 and d ib = k 2 i Recombine i, j (if ib: i jet) Repeat Δ R ij > R NB: hadron collider variables ΔR 2 ij =(φ i φ j ) 2 +(y i y j ) 2 rapidity y i = 1 2 ln E i +p zi E i p zi ΔR ij is boost invariant angle R sets minimal interjet angle NB: d ij distance QCD branching probability α s dk 2 tj dr2 ij d ij

29 Towards Jetography, G. Salam (p. 9) Two broad classes Cones with Split Merge (SM) Tevatron & ATLAS cone algs have two main steps: Find some/all stable cones cone pointing in same direction as the momentum of its contents Found by iterating from some initial seed directions Resolve cases of overlapping stable cones Top-down jets: cone algorithms (energy flow conserved by QCD) By running a split merge procedure

30 Towards Jetography, G. Salam (p. 9) Two broad classes Cones with Split Merge (SM) Tevatron & ATLAS cone algs have two main steps: Find some/all stable cones cone pointing in same direction as the momentum of its contents Found by iterating from some initial seed directions Resolve cases of overlapping stable cones By running a split merge procedure

31 Towards Jetography, G. Salam (p. 9) Two broad classes Cones with Split Merge (SM) Tevatron & ATLAS cone algs have two main steps: Find some/all stable cones cone pointing in same direction as the momentum of its contents Found by iterating from some initial seed directions Resolve cases of overlapping stable cones By running a split merge procedure

32 Towards Jetography, G. Salam (p. 9) Two broad classes Cones with Split Merge (SM) Tevatron & ATLAS cone algs have two main steps: Find some/all stable cones cone pointing in same direction as the momentum of its contents Found by iterating from some initial seed directions Resolve cases of overlapping stable cones By running a split merge procedure

33 Towards Jetography, G. Salam (p. 9) Two broad classes Cones with Split Merge (SM) Tevatron & ATLAS cone algs have two main steps: Find some/all stable cones cone pointing in same direction as the momentum of its contents Found by iterating from some initial seed directions Resolve cases of overlapping stable cones By running a split merge procedure

34 Towards Jetography, G. Salam (p. 9) Two broad classes Cones with Split Merge (SM) Tevatron & ATLAS cone algs have two main steps: Find some/all stable cones cone pointing in same direction as the momentum of its contents Found by iterating from some initial seed directions Resolve cases of overlapping stable cones By running a split merge procedure

35 Towards Jetography, G. Salam (p. 9) Two broad classes Cones with Split Merge (SM) Tevatron & ATLAS cone algs have two main steps: Find some/all stable cones cone pointing in same direction as the momentum of its contents Found by iterating from some initial seed directions Resolve cases of overlapping stable cones By running a split merge procedure

36 Towards Jetography, G. Salam (p. 9) Two broad classes Cones with Split Merge (SM) Tevatron & ATLAS cone algs have two main steps: Find some/all stable cones cone pointing in same direction as the momentum of its contents Found by iterating from some initial seed directions Resolve cases of overlapping stable cones By running a split merge procedure

37 Towards Jetography, G. Salam (p. 9) Two broad classes Cones with Split Merge (SM) Tevatron & ATLAS cone algs have two main steps: Find some/all stable cones cone pointing in same direction as the momentum of its contents Found by iterating from some initial seed directions Resolve cases of overlapping stable cones By running a split merge procedure

38 Towards Jetography, G. Salam (p. 9) Two broad classes Cones with Split Merge (SM) Tevatron & ATLAS cone algs have two main steps: Find some/all stable cones cone pointing in same direction as the momentum of its contents Found by iterating from some initial seed directions Resolve cases of overlapping stable cones By running a split merge procedure

39 Towards Jetography, G. Salam (p. 9) Two broad classes Cones with Split Merge (SM) Tevatron & ATLAS cone algs have two main steps: Find some/all stable cones cone pointing in same direction as the momentum of its contents Found by iterating from some initial seed directions Resolve cases of overlapping stable cones By running a split merge procedure

40 Towards Jetography, G. Salam (p. 9) Two broad classes Cones with Split Merge (SM) Tevatron & ATLAS cone algs have two main steps:

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42 Towards Jetography, G. Salam (p. 10) Two broad classes Iterative Cone [with progressive removal] Procedure: Find one stable cone Call it a jet; remove its particles from the event; repeat By iterating from hardest seed particle

43 Towards Jetography, G. Salam (p. 10) Two broad classes Iterative Cone [with progressive removal] Procedure: Find one stable cone Call it a jet; remove its particles from the event; repeat By iterating from hardest seed particle

44 Towards Jetography, G. Salam (p. 10) Two broad classes Iterative Cone [with progressive removal] Procedure: Find one stable cone Call it a jet; remove its particles from the event; repeat By iterating from hardest seed particle

45 Towards Jetography, G. Salam (p. 10) Two broad classes Iterative Cone [with progressive removal] Procedure: Find one stable cone Call it a jet; remove its particles from the event; repeat By iterating from hardest seed particle

46 Towards Jetography, G. Salam (p. 10) Two broad classes Iterative Cone [with progressive removal] Procedure: Find one stable cone Call it a jet; remove its particles from the event; repeat By iterating from hardest seed particle

47 Towards Jetography, G. Salam (p. 10) Two broad classes Iterative Cone [with progressive removal] Procedure: Find one stable cone Call it a jet; remove its particles from the event; repeat By iterating from hardest seed particle

48 Towards Jetography, G. Salam (p. 10) Two broad classes Iterative Cone [with progressive removal] Procedure: Find one stable cone Call it a jet; remove its particles from the event; repeat By iterating from hardest seed particle

49 Towards Jetography, G. Salam (p. 10) Two broad classes Iterative Cone [with progressive removal] Procedure: Find one stable cone Call it a jet; remove its particles from the event; repeat By iterating from hardest seed particle

50 Towards Jetography, G. Salam (p. 10) Two broad classes Iterative Cone [with progressive removal] Procedure: Find one stable cone Call it a jet; remove its particles from the event; repeat By iterating from hardest seed particle

51 Towards Jetography, G. Salam (p. 10) Two broad classes Iterative Cone [with progressive removal] Procedure: Find one stable cone Call it a jet; remove its particles from the event; repeat By iterating from hardest seed particle

52 Towards Jetography, G. Salam (p. 10) Two broad classes Iterative Cone [with progressive removal] Procedure: Find one stable cone Call it a jet; remove its particles from the event; repeat By iterating from hardest seed particle

53 Towards Jetography, G. Salam (p. 10) Two broad classes Iterative Cone [with progressive removal] Procedure: Find one stable cone Call it a jet; remove its particles from the event; repeat By iterating from hardest seed particle

54 Towards Jetography, G. Salam (p. 10) Two broad classes Iterative Cone [with progressive removal] Procedure: By iterating from hardest seed particle Find one stable cone Call it a jet; remove its particles from the event; repeat Iterative Cone with Progressive Removal (IC-PR) e.g. CMS it. cone, [Pythia Cone, GetJet],... NB: not same type of algorithm as Atlas Cone, MidPoint, SISCone

55 Towards Jetography, G. Salam (p. 11) Snowmass Readying jet technology for the LHC era [a.k.a. satisfying Snowmass]

56 Towards Jetography, G. Salam (p. 12) Snowmass Snowmass Accord (1990): Snowmass accords

57 Towards Jetography, G. Salam (p. 12) Snowmass Snowmass accords Snowmass Accord (1990): Property 1 speed. (+other aspects) LHC events may have up to N = 4000 particles (at high-lumi) Sequential recombination algs. (k t )slow, N 3 60s for N = 4000 k t not practical for O ( 10 9) events

58 Towards Jetography, G. Salam (p. 12) Snowmass Snowmass accords Snowmass Accord (1990): Property 4 Infrared and Collinear (IRC) Safety. It helps ensure: Soft (low-energy) emissions & collinear splittings don t change jets Each order of perturbation theory is smaller than previous (at high p t ) Wasn t satisfied by the cone algorithms

59 Towards Jetography, G. Salam (p. 13) Snowmass Speeding up k t Computing and k t Trivial computational issue: for N particles: N 2 d ij searched through N times = N particles (or calo cells): 1 minute Heavy Ions: particles: 10 hours/event NB: often study events ( CPU years) Snowmass issue #1 The k t algorithm and its speed As far as possible physics choices should not be limited by computing. Even if we re clever about repeating the full search each time, westill have O ( N 2) d ij stoestablish?

60 Towards Jetography, G. Salam (p. 13) Snowmass Speeding up k t Computing and k t Trivial computational issue: for N particles: N 2 d ij searched through N times = N particles (or calo cells): 1 minute NB: often study events ( CPU years) Heavy Ions: particles: 10 hours/event As far as possible physics choices should not be limited by computing. Even if we re clever about repeating the full search each time, we still have O ( N 2) d ij s to establish?

61 Towards Jetography, G. Salam (p. 13) Snowmass Speeding up k t Computing and k t Trivial computational issue: for N particles: N 2 d ij searched through N times = N particles (or calo cells): 1 minute NB: often study events ( CPU years) Heavy Ions: particles: 10 hours/event As far as possible physics choices should not be limited by computing. Even if we re clever about repeating the full search each time, we still have O ( N 2) d ij s to establish? No! The FastJet trick: separate momentum & ( easy ) geometry: [ ] [ min min(k 2 ti, ktj 2 ij] i,j )ΔR2 min kti 2 min ΔRij 2 i j Allows for N ln N implementation. Cacciari & GPS 05 + CGAL

62 Towards Jetography, G. Salam (p. 14) Snowmass Speeding up k t t / s R=0.7 KtJet k t (old N 3 implementation) CDF JetClu (IR unsafe, fast cone) k t algorithm speed: old & new LHC lo-lumi LHC hi-lumi LHC Pb-Pb N

63 Towards Jetography, G. Salam (p. 14) Snowmass Speeding up k t t / s R=0.7 KtJet k t (old N 3 implementation) CDF JetClu (IR unsafe, fast cone) k t algorithm speed: old & new FastJet k t LHC lo-lumi LHC hi-lumi LHC Pb-Pb N ln N N Factorisation of momentum & geometry 2 3 orders of magnitude gain in speed! Speed competitive with fast cone algorithms

64 Towards Jetography, G. Salam (p. 15) Snowmass Cone IR issues JetClu (& Atlas Cone) in NLO jet jet W Snowmass issue #4 Cone algorithms and IR safety α 2 s α EW α 3 s α EW α 3 s α EW 1-jet + 2-jet O (1) 0 With these (& most) cone algorithms, perturbative infinities fail to cancel at some order IR unsafety

65 Towards Jetography, G. Salam (p. 15) Snowmass Cone IR issues JetClu (& Atlas Cone) in NLO jet jet W α 2 s α EW α 3 s α EW α 3 s α EW 1-jet + 2-jet O (1) 0 With these (& most) cone algorithms, perturbative infinities fail to cancel at some order IR unsafety

66 Towards Jetography, G. Salam (p. 15) Snowmass Cone IR issues JetClu (& Atlas Cone) in NLO jet jet jet jet soft divergence W W α 2 s α EW α 3 s α EW α 3 s α EW 1-jet + 2-jet O (1) 0 With these (& most) cone algorithms, perturbative infinities fail to cancel at some order IR unsafety

67 Towards Jetography, G. Salam (p. 15) Snowmass Cone IR issues JetClu (& Atlas Cone) in NLO jet jet jet jet jet soft divergence W W W α 2 s α EW α 3 s α EW α 3 s α EW 1-jet + 2-jet O (1) 0 With these (& most) cone algorithms, perturbative infinities fail to cancel at some order IR unsafety

68 Towards Jetography, G. Salam (p. 15) Snowmass Cone IR issues JetClu (& Atlas Cone) in NLO jet jet jet jet jet soft divergence W W W α 2 s α EW α 3 s α EW α 3 s α EW 1-jet + 2-jet O (1) 0 With these (& most) cone algorithms, perturbative infinities fail to cancel at some order IR unsafety

69 Towards Jetography, G. Salam (p. 16) Snowmass Cone IR issues IRC safety & real-life Real life does not have infinities, but pert. infinity leaves a real-life trace α 2 s + α3 s + α4 s α2 s + α3 s + α4 s ln p t/λ α 2 s + α3 s + α3 s }{{} BOTH WASTED Among consequences of IR unsafety: Last meaningful order JetClu, ATLAS MidPoint CMS it. cone Known at cone [IC-SM] [IC mp -SM] [IC-PR] Inclusive jets LO NLO NLO NLO ( NNLO) W /Z +1jet LO NLO NLO NLO 3jets none LO LO NLO [nlojet++] W /Z +2jets none LO LO NLO [MCFM] m jet in 2j + X none none none LO NB: 50,000,000$/ /CHF/e investment in NLO Multi-jet contexts much more sensitive: ubiquitous at LHC And LHC will rely on QCD for background double-checks extraction of cross sections, extraction of parameters

70 Towards Jetography, G. Salam (p. 16) Snowmass Cone IR issues IRC safety & real-life Real life does not have infinities, but pert. infinity leaves a real-life trace α 2 s + α3 s + α4 s α2 s + α3 s + α4 s ln p t/λ α 2 s + α3 s + α3 s }{{} BOTH WASTED Among consequences of IR unsafety: Last meaningful order JetClu, ATLAS MidPoint CMS it. cone Known at cone [IC-SM] [IC mp -SM] [IC-PR] Inclusive jets LO NLO NLO NLO ( NNLO) W /Z +1jet LO NLO NLO NLO 3jets none LO LO NLO [nlojet++] W /Z +2jets none LO LO NLO [MCFM] m jet in 2j + X none none none LO NB: 50,000,000$/ /CHF/e investment in NLO Multi-jet contexts much more sensitive: ubiquitous at LHC And LHC will rely on QCD for background double-checks extraction of cross sections, extraction of parameters

71 Towards Jetography, G. Salam (p. 16) Snowmass Cone IR issues IRC safety & real-life Real life does not have infinities, but pert. infinity leaves a real-life trace α 2 s + α3 s + α4 s α2 s + α3 s + α4 s ln p t/λ α 2 s + α3 s + α3 s }{{} BOTH WASTED Among consequences of IR unsafety: Last meaningful order JetClu, ATLAS MidPoint CMS it. cone Known at cone [IC-SM] [IC mp -SM] [IC-PR] Inclusive jets LO NLO NLO NLO ( NNLO) W /Z +1jet LO NLO NLO NLO 3jets none LO LO NLO [nlojet++] W /Z +2jets none LO LO NLO [MCFM] m jet in 2j + X none none none LO NB: 50,000,000$/ /CHF/e investment in NLO Multi-jet contexts much more sensitive: ubiquitous at LHC And LHC will rely on QCD for background double-checks extraction of cross sections, extraction of parameters

72 Towards Jetography, G. Salam (p. 17) Snowmass Cone IR issues Does lack of IRC safety matter? I do searches, not QCD. Why should I care about IRC safety? Are you looking for a mass-peak? you needn t care much Are you looking for an excess over bkgd? you need control samples, validated against QCD W+1,2,3 jets }{{} NLO v. data W+n jets }{{} LO, LO+MC v. data new-physics search }{{} LO+MC v. data

73 Towards Jetography, G. Salam (p. 17) Snowmass Cone IR issues Does lack of IRC safety matter? I do searches, not QCD. Why should I care about IRC safety? Are you looking for a mass-peak? you needn t care much Are you looking for an excess over bkgd? you need control samples, validated against QCD W+1,2,3 jets }{{} NLO v. data W+n jets }{{} LO, LO+MC v. data new-physics search }{{} LO+MC v. data

74 Towards Jetography, G. Salam (p. 17) Snowmass Cone IR issues Does lack of IRC safety matter? I do searches, not QCD. Why should I care about IRC safety? Are you looking for a mass-peak? you needn t care much Are you looking for an excess over bkgd? you need control samples, validated against QCD W+1,2,3 jets }{{} W+n jets }{{} new-physics search }{{} NLO v. data LO, LO+MC v. data LO+MC v. data IR safe alg. IR safe alg. IR safe alg.

75 Towards Jetography, G. Salam (p. 17) Snowmass Cone IR issues Does lack of IRC safety matter? I do searches, not QCD. Why should I care about IRC safety? Are you looking for a mass-peak? you needn t care much Are you looking for an excess over bkgd? you need control samples, validated against QCD W+1,2,3 jets }{{} W+n jets }{{} / new-physics search }{{} NLO v. data LO, LO+MC v. data LO+MC v. data IR safe alg. IR safe alg. IR unsafe alg.

76 Towards Jetography, G. Salam (p. 18) Snowmass Cone IR issues Two directions GPS & Soyez 07 Same family as Tev. Run II alg Cacciari, GPS & Soyez 08

77 Towards Jetography, G. Salam (p. 19) Snowmass Cone IR issues Essential characteristic of cones? Cone (ICPR)

78 Towards Jetography, G. Salam (p. 19) Snowmass Cone IR issues Cone (ICPR) Essential characteristic of cones? (Some) cone algorithms give circular jets in y φ plane Much appreciated by experiments e.g. for acceptance corrections

79 Towards Jetography, G. Salam (p. 19) Snowmass Cone IR issues Cone (ICPR) Essential characteristic of cones? (Some) cone algorithms give circular jets in y φ plane Much appreciated by experiments e.g. for acceptance corrections k t alg.

80 Towards Jetography, G. Salam (p. 19) Snowmass Cone IR issues Cone (ICPR) Essential characteristic of cones? (Some) cone algorithms give circular jets in y φ plane Much appreciated by experiments e.g. for acceptance corrections k t alg. k t jets are irregular Because soft junk clusters together first: d ij =min(k 2 ti, k2 tj )ΔR2 ij Regularly held against k t

81 Towards Jetography, G. Salam (p. 19) Snowmass Cone IR issues Cone (ICPR) Essential characteristic of cones? (Some) cone algorithms give circular jets in y φ plane Much appreciated byexperiments e.g. for acceptance corrections k t jets are irregular cone-based way of getting k t alg. Because soft junk clusters together first: d =min(k ij ti 2, k2 tj )ΔR2 ij Regularly held against k t Istheresomeother,non circular jets?

82 Towards Jetography, G. Salam (p. 20) Snowmass Cone IR issues Adapting seq. rec. to give circular jets Soft stuff clusters with nearest neighbour k t : d ij =min(k 2 ti, k2 tj )ΔR2 ij anti-k t: d ij = ΔR 2 ij max(k 2 ti, k2 tj ) Hard stuff clusters with nearest neighbour Privilege collinear divergence over soft divergence Cacciari, GPS & Soyez 08

83 Towards Jetography, G. Salam (p. 20) Snowmass Cone IR issues Adapting seq. rec. to give circular jets Soft stuff clusters with nearest neighbour k t : d ij =min(k 2 ti, k2 tj )ΔR2 ij anti-k t: d ij = ΔR 2 ij max(k 2 ti, k2 tj ) Hard stuff clusters with nearest neighbour Privilege collinear divergence over soft divergence Cacciari, GPS & Soyez 08

84 Towards Jetography, G. Salam (p. 20) Snowmass Cone IR issues Adapting seq. rec. to give circular jets Soft stuff clusters with nearest neighbour k t : d ij =min(k 2 ti, k2 tj )ΔR2 ij anti-k t: d ij = ΔR 2 ij max(k 2 ti, k2 tj ) Hard stuff clusters with nearest neighbour Privilege collinear divergence over soft divergence Cacciari, GPS & Soyez 08

85 Towards Jetography, G. Salam (p. 20) Snowmass Cone IR issues Adapting seq. rec. to give circular jets Soft stuff clusters with nearest neighbour k t : d ij =min(k 2 ti, k2 tj )ΔR2 ij anti-k t: d ij = ΔR 2 ij max(k 2 ti, k2 tj ) Hard stuff clusters with nearest neighbour Privilege collinear divergence over soft divergence Cacciari, GPS & Soyez 08

86 Towards Jetography, G. Salam (p. 20) Snowmass Cone IR issues Adapting seq. rec. to give circular jets Soft stuff clusters with nearest neighbour k t : d ij =min(k 2 ti, k2 tj )ΔR2 ij anti-k t: d ij = ΔR 2 ij max(k 2 ti, k2 tj ) Hard stuff clusters with nearest neighbour Privilege collinear divergence over soft divergence Cacciari, GPS & Soyez 08

87 Towards Jetography, G. Salam (p. 20) Snowmass Cone IR issues Adapting seq. rec. to give circular jets Soft stuff clusters with nearest neighbour k t : d ij =min(k 2 ti, k2 tj )ΔR2 ij anti-k t: d ij = ΔR 2 ij max(k 2 ti, k2 tj ) Hard stuff clusters with nearest neighbour Privilege collinear divergence over soft divergence Cacciari, GPS & Soyez 08

88 Towards Jetography, G. Salam (p. 20) Snowmass Cone IR issues Adapting seq. rec. to give circular jets Soft stuff clusters with nearest neighbour k t : d ij =min(k 2 ti, k2 tj )ΔR2 ij anti-k t: d ij = ΔR 2 ij max(k 2 ti, k2 tj ) Hard stuff clusters with nearest neighbour Privilege collinear divergence over soft divergence Cacciari, GPS & Soyez 08

89 Towards Jetography, G. Salam (p. 20) Snowmass Cone IR issues Adapting seq. rec. to give circular jets Soft stuff clusters with nearest neighbour k t : d ij =min(k 2 ti, k2 tj )ΔR2 ij anti-k t: d ij = ΔR 2 ij max(k 2 ti, k2 tj ) Hard stuff clusters with nearest neighbour Privilege collinear divergence over soft divergence Cacciari, GPS & Soyez 08

90 Towards Jetography, G. Salam (p. 20) Snowmass Cone IR issues Adapting seq. rec. to give circular jets Soft stuff clusters with nearest neighbour k t : d ij =min(k 2 ti, k2 tj )ΔR2 ij anti-k t: d ij = ΔR 2 ij max(k 2 ti, k2 tj ) Hard stuff clusters with nearest neighbour Privilege collinear divergence over soft divergence Cacciari, GPS & Soyez 08

91 Towards Jetography, G. Salam (p. 20) Snowmass Cone IR issues Adapting seq. rec. to give circular jets Soft stuff clusters with nearest neighbour k t : d ij =min(k 2 ti, k2 tj )ΔR2 ij anti-k t: d ij = ΔR 2 ij max(k 2 ti, k2 tj ) Hard stuff clusters with nearest neighbour Privilege collinear divergence over soft divergence Cacciari, GPS & Soyez 08

92 Towards Jetography, G. Salam (p. 20) Snowmass Cone IR issues Adapting seq. rec. to give circular jets Soft stuff clusters with nearest neighbour k t : d ij =min(k 2 ti, k2 tj )ΔR2 ij anti-k t: d ij = ΔR 2 ij max(k 2 ti, k2 tj ) Hard stuff clusters with nearest neighbour Privilege collinear divergence over soft divergence Cacciari, GPS & Soyez 08

93 Towards Jetography, G. Salam (p. 20) Snowmass Cone IR issues Adapting seq. rec. to give circular jets Soft stuff clusters with nearest neighbour k t : d ij =min(k 2 ti, k2 tj )ΔR2 ij anti-k t: d ij = ΔR 2 ij max(k 2 ti, k2 tj ) Hard stuff clusters with nearest neighbour Privilege collinear divergence over soft divergence Cacciari, GPS & Soyez 08

94 Towards Jetography, G. Salam (p. 20) Snowmass Cone IR issues Adapting seq. rec. to give circular jets Soft stuff clusters with nearest neighbour k t : d ij =min(k 2 ti, k2 tj )ΔR2 ij anti-k t: d ij = ΔR 2 ij max(k 2 ti, k2 tj ) Hard stuff clusters with nearest neighbour Privilege collinear divergence over soft divergence Cacciari, GPS & Soyez 08 anti-k t gives cone-like jets without using stable cones

95 Towards Jetography, G. Salam (p. 21) Snowmass A collection of algs A full set of IRC-safe jet algorithms Generalise inclusive-type sequential recombination with d ij =min(k 2p ti, k 2p tj )ΔR 2 ij/r 2 d ib = k 2p ti Alg. name Comment time p =1 k t Hierarchical in rel. k t CDOSTW 91-93; ES 93 N ln N exp. p =0 Cambridge/Aachen Hierarchical in angle Dok, Leder, Moretti, Webber 97 Scan multiple R at once N ln N Wengler, Wobisch 98 QCD angular ordering p = 1 anti-k t Cacciari, GPS, Soyez 08 Hierarchy meaningless, jets reverse-k t Delsart like CMS cone (IC-PR) N 3/2 SC-SM SISCone Replaces JetClu, ATLAS GPS Soyez 07 + Tevatron run II 00 MidPoint (xc-sm) cones N 2 ln N exp. All these algorithms [& much more] coded in (efficient) C++ at (Cacciari, GPS & Soyez 05-09)

96 Towards Jetography, G. Salam (p. 22) Snowmass A collection of algs Evolution since 2005 Algorithm Type IRC status Evolution exclusive k t SR p=1 OK N 3 N ln N inclusive k t SR p=1 OK N 3 N ln N Cambridge/Aachen SR p=0 OK N 3 N ln N Run II Seedless cone SC-SM OK SISCone CDF JetClu IC r -SM IR 2+1 [ SISCone] CDF MidPoint cone IC mp -SM IR 3+1 SISCone CDF MidPoint searchcone IC se,mp -SM IR 2+1 [ SISCone] D0 Run II cone IC mp -SM IR 3+1 SISCone [with p t cut?] ATLAS Cone IC-SM IR 2+1 SISCone PxCone IC mp -SD IR 3+1 [little used] CMS Iterative Cone IC-PR Coll 3+1 anti-k t PyCell/CellJet (from Pythia) FC-PR Coll 3+1 anti-k t GetJet (from ISAJET) FC-PR Coll 3+1 anti-k t SR = seq.rec.; IC = it.cone; FC = fixed cone; SM = split merge; SD = split drop; PR = progressive removal

97 Towards Jetography, G. Salam (p. 23) Beyond Snowmass Snowmass is solved But it was a problem from the 1990s What are the problems we should be trying to solve for LHC?

98 Towards Jetography, G. Salam (p. 24) Beyond Snowmass Which jet definition(s) for LHC? Choice of algorithm (k t, SISCone,...) Choice of parameters (R,...) Can we address this question scientifically? Jetography

99 Towards Jetography, G. Salam (p. 24) Beyond Snowmass Which jet definition(s) for LHC? Choice of algorithm (k t, SISCone,...) Choice of parameters (R,...) Can we address this question scientifically? Jetography

100 Towards Jetography, G. Salam (p. 25) Physics of jets Jet def n differences Jet definitions }{{} alg + R differ mainly in: 1. How close two particles must be to end up in same jet [discussed in the 90s, e.g. Ellis & Soper] 2. How much perturbative radiation is lost from a jet [indirectly discussed in the 90s (analytic NLO for inclusive jets)] 3. How much non-perturbative contamination (hadronisation, UE, pileup) a jet receives [partially discussed in 90s Korchemsky & Sterman 95, Seymour 97]

101 Towards Jetography, G. Salam (p. 25) Physics of jets Jet def n differences Jet definitions }{{} alg + R differ mainly in: 1. How close two particles must be to end up in same jet [discussed in the 90s, e.g. Ellis & Soper] 2. How much perturbative radiation is lost from a jet [indirectly discussed in the 90s (analytic NLO for inclusive jets)] 3. How much non-perturbative contamination (hadronisation, UE, pileup) a jet receives [partially discussed in 90s Korchemsky & Sterman 95, Seymour 97]

102 Towards Jetography, G. Salam (p. 25) Physics of jets Jet def n differences Jet definitions }{{} alg + R differ mainly in: 1. How close two particles must be to end up in same jet [discussed in the 90s, e.g. Ellis & Soper] 2. How much perturbative radiation is lost from a jet [indirectly discussed in the 90s (analytic NLO for inclusive jets)] 3. How much non-perturbative contamination (hadronisation, UE, pileup) a jet receives [partially discussed in 90s Korchemsky & Sterman 95, Seymour 97]

103 Towards Jetography, G. Salam (p. 26) Physics of jets Perturbative Δp t Jet p t v. parton p t : perturbatively? The question s dangerous: a parton is an ambiguous concept Three limits can help you: Threshold limit e.g. de Florian & Vogelsang 07 Parton from color-neutral object decay (Z ) Small-R (radius) limit for jet One simple result p t,jet p t,parton p t = α s π ln R { 1.01CF quarks 0.94C A +0.07n f gluons + O (α s ) only O (α s ) depends on algorithm & process cf. Dasgupta, Magnea & GPS 07

104 Towards Jetography, G. Salam (p. 27) Physics of jets Non-perturbative Δp t Jet p t v. parton p t :hadronisation? Hadronisation: the parton-shower hadrons transition Method: infrared finite α s prediction based on e + e event shape data àladokshitzer&webber 95 could have been deduced from old work Korchemsky & Sterman 95 Seymour 97 Main result p t,jet p t,parton shower 0.4 GeV R { CF quarks gluons C A cf. Dasgupta, Magnea & GPS 07 coefficient holds for anti-k t ; see Dasgupta & Delenda 09 for k t alg.

105 Towards Jetography, G. Salam (p. 28) Physics of jets Non-perturbative Δp t Underlying Event (UE) Naive prediction (UE colour dipole between pp): { Δp t 0.4 GeV R2 2 CF q q dipole gluon dipole C A DWT Pythia tune or ATLAS Jimmy tune tell you: Δp t GeV R2 2 This big coefficient motivates special effort to understand interplay between jet algorithm and UE: jet areas How does coefficient depend on algorithm? How does it depend on jet p t? How does it fluctuate? cf. Cacciari, GPS & Soyez 08

106 Towards Jetography, G. Salam (p. 28) Physics of jets Non-perturbative Δp t Underlying Event (UE) Naive prediction (UE colour dipole between pp): { Δp t 0.4 GeV R2 2 CF q q dipole gluon dipole C A DWT Pythia tune or ATLAS Jimmy tune tell you: Δp t GeV R2 2 This big coefficient motivates special effort to understand interplay between jet algorithm and UE: jet areas How does coefficient depend on algorithm? How does it depend on jet p t? How does it fluctuate? cf. Cacciari, GPS & Soyez 08

107 Towards Jetography, G. Salam (p. 29) Physics of jets Non-perturbative Δp t E.g. SISCone jet area 1. One hard particle, many soft Jet area = Measure of jet s susceptibility to uniform soft radiation Depends on details of an algorithm s clustering dynamics. SISCone, any R, f 0.391

108 Towards Jetography, G. Salam (p. 29) Physics of jets Non-perturbative Δp t E.g. SISCone jet area 2. One hard stable cone, area = πr 2 Jet area = Measure of jet s susceptibility to uniform soft radiation Depends on details of an algorithm s clustering dynamics. SISCone, any R, f 0.391

109 Towards Jetography, G. Salam (p. 29) Physics of jets Non-perturbative Δp t E.g. SISCone jet area 3. Overlapping soft stable cones Jet area = Measure of jet s susceptibility to uniform soft radiation Depends on details of an algorithm s clustering dynamics. SISCone, any R, f 0.391

110 Towards Jetography, G. Salam (p. 29) Physics of jets Non-perturbative Δp t E.g. SISCone jet area 4. Split the overlapping parts Jet area = Measure of jet s susceptibility to uniform soft radiation Depends on details of an algorithm s clustering dynamics. SISCone, any R, f 0.391

111 Towards Jetography, G. Salam (p. 29) Physics of jets Non-perturbative Δp t E.g. SISCone jet area 5. Final hard jet (reduced area) Jet area = Measure of jet s susceptibility to uniform soft radiation Depends on details of an algorithm s clustering dynamics. SISCone s area (1 hard particle) = 1 4 πr2 Small area low sensitivity to UE & pileup SISCone, any R, f 0.391

112 Towards Jetography, G. Salam (p. 30) Physics of jets Jet-properties summary Jet algorithm properties: summary k t Cam/Aachen anti-k t SISCone reach R R R (1 + p t2 p t2 )R Δp t,pt α sc i π ln R ln R ln R ln 1.35R Δp t,hadr 0.4 GeVC i R 0.7? 1? area = πr ± ± πr 2 C i πb 0 ln α s(q 0 ) α s (Rp t ) In words: 0.52 ± ± ± 0.07 k t : area fluctuates a lot, depends on p t (bad for UE) Cam/Aachen: area fluctuates somewhat, depends less on p t anti-k t : area is constant (circular jets) SISCone: reaches far for hard radiation (good for resolution, bad for multijets), area is smaller (good for UE)

113 Towards Jetography, G. Salam (p. 31) Physics with jets Can we benefit from this understanding in our use of jets?

114 Towards Jetography, G. Salam (p. 32) Physics with jets Dijet resonances Jet momentum significantly affected by R So what R should we choose? Examine this in context of reconstruction of dijet resonance

115 Towards Jetography, G. Salam (p. 33) Physics with jets Dijet resonances What R is best for an isolated jet? PT radiation: q : Δp t α sc F π p t ln R E.g. to reconstruct m X (p tq + p t q ) q Hadronisation: q : Δp t C F R 0.4 GeV p q X q p Underlying event: q, g : Δp t R GeV q Minimise fluctuations in p t Use crude approximation: Δpt 2 Δp t 2 in small-r limit (?!) cf. Dasgupta, Magnea & GPS 07

116 Towards Jetography, G. Salam (p. 33) Physics with jets Dijet resonances What R is best for an isolated jet? PT radiation: q : Δp t α sc F π Hadronisation: q : Δp t C F R Underlying event: q, g : Δp t R2 2 p t ln R 0.4 GeV GeV Minimise fluctuations in p t Use crude approximation: Δpt 2 Δp t 2 δp t 2 pert + δp t 2 h + δp t 2 UE [GeV2 ] GeV quark jet LHC quark jets p t = 50 GeV δp t 2 h δp t 2 pert R δp t 2 UE in small-r limit (?!) cf. Dasgupta, Magnea & GPS 07

117 Towards Jetography, G. Salam (p. 33) Physics with jets Dijet resonances What R is best for an isolated jet? PT radiation: q : Δp t α sc F π Hadronisation: q : Δp t C F R Underlying event: q, g : Δp t R2 2 p t ln R 0.4 GeV GeV δp t 2 pert + δp t 2 h + δp t 2 UE [GeV2 ] LHC quark jets p t = 1 TeV 1 TeV quark jet δp t 2 pert δp t 2 UE Minimise fluctuations in p t Use crude approximation: Δpt 2 Δp t R in small-r limit (?!) cf. Dasgupta, Magnea & GPS 07

118 Towards Jetography, G. Salam (p. 33) Physics with jets Dijet resonances What R is best for an isolated jet? PT radiation: q : Δp t α sc F π p t ln R Hadronisation: q : Δp t C F R Underlying event: q, g : Δp t R GeV 2 δp t 2 pert + δp t 2 h + δp t 2 UE [GeV2 ] LHC quark jets p t = 1 TeV 1 TeV quark jet At low p t, small R limits relative impact of UE At high p t, perturbative effects dominate over 0.4 GeV non-perturbative R best δp t 2 pert δp t 2 UE p t Minimise fluctuations in p t Use crude approximation: Δp t 2 Δp t R in small-r limit (?!) cf. Dasgupta, Magnea & GPS 07

119 Towards Jetography, G. Salam (p. 34) Physics with jets Dijet resonances 1/N dn/dbin / R=0.3 qq, M = 100 GeV SISCone, R=0.3, f=0.75 Q w f=0.24 = 24.0 GeV Dijet mass: scan over R [Pythia 6.4] arxiv: p Resonance X dijets q q X q p dijet mass [GeV] q

120 Towards Jetography, G. Salam (p. 34) Physics with jets Dijet resonances 1/N dn/dbin / R=0.3 qq, M = 100 GeV SISCone, R=0.3, f=0.75 Q w f=0.24 = 24.0 GeV Dijet mass: scan over R [Pythia 6.4] arxiv: p Resonance X dijets jet q q X q p dijet mass [GeV] jet q

121 Towards Jetography, G. Salam (p. 34) Physics with jets Dijet resonances 1/N dn/dbin / R=0.4 qq, M = 100 GeV SISCone, R=0.4, f=0.75 Q w f=0.24 = 22.5 GeV Dijet mass: scan over R [Pythia 6.4] arxiv: p Resonance X dijets jet q q X q p dijet mass [GeV] jet q

122 Towards Jetography, G. Salam (p. 34) Physics with jets Dijet resonances 1/N dn/dbin / R=0.5 qq, M = 100 GeV SISCone, R=0.5, f=0.75 Q w f=0.24 = 22.6 GeV Dijet mass: scan over R [Pythia 6.4] arxiv: p Resonance X dijets jet q q X q p dijet mass [GeV] jet q

123 Towards Jetography, G. Salam (p. 34) Physics with jets Dijet resonances 1/N dn/dbin / R=0.6 qq, M = 100 GeV SISCone, R=0.6, f=0.75 Q w f=0.24 = 23.8 GeV Dijet mass: scan over R [Pythia 6.4] arxiv: p Resonance X dijets jet q q X q p dijet mass [GeV] jet q

124 Towards Jetography, G. Salam (p. 34) Physics with jets Dijet resonances 1/N dn/dbin / R=0.7 qq, M = 100 GeV SISCone, R=0.7, f=0.75 Q w f=0.24 = 25.1 GeV Dijet mass: scan over R [Pythia 6.4] arxiv: p Resonance X dijets jet q q X q p dijet mass [GeV] jet q

125 Towards Jetography, G. Salam (p. 34) Physics with jets Dijet resonances 1/N dn/dbin / R=0.8 qq, M = 100 GeV SISCone, R=0.8, f=0.75 Q w f=0.24 = 26.8 GeV Dijet mass: scan over R [Pythia 6.4] arxiv: p Resonance X dijets jet q q X q p dijet mass [GeV] jet q

126 Towards Jetography, G. Salam (p. 34) Physics with jets Dijet resonances 1/N dn/dbin / R=0.9 qq, M = 100 GeV SISCone, R=0.9, f=0.75 Q w f=0.24 = 28.8 GeV Dijet mass: scan over R [Pythia 6.4] arxiv: p Resonance X dijets jet q q X q p dijet mass [GeV] jet q

127 Towards Jetography, G. Salam (p. 34) Physics with jets Dijet resonances 1/N dn/dbin / R=1.0 qq, M = 100 GeV SISCone, R=1.0, f=0.75 Q w f=0.24 = 31.9 GeV Dijet mass: scan over R [Pythia 6.4] arxiv: p Resonance X dijets jet q q X q p dijet mass [GeV] jet q

128 Towards Jetography, G. Salam (p. 34) Physics with jets Dijet resonances 1/N dn/dbin / R=1.1 qq, M = 100 GeV SISCone, R=1.1, f=0.75 Q w f=0.24 = 34.7 GeV Dijet mass: scan over R [Pythia 6.4] arxiv: p Resonance X dijets jet q q X q p dijet mass [GeV] jet q

129 Towards Jetography, G. Salam (p. 34) Physics with jets Dijet resonances 1/N dn/dbin / R=1.2 qq, M = 100 GeV SISCone, R=1.2, f=0.75 Q w f=0.24 = 37.9 GeV Dijet mass: scan over R [Pythia 6.4] arxiv: p Resonance X dijets jet q q X q p dijet mass [GeV] jet q

130 Towards Jetography, G. Salam (p. 34) Physics with jets Dijet resonances 1/N dn/dbin / R=1.3 qq, M = 100 GeV SISCone, R=1.3, f=0.75 Q w f=0.24 = 42.3 GeV Dijet mass: scan over R [Pythia 6.4] arxiv: p Resonance X dijets jet q q X q p dijet mass [GeV] jet q

131 Towards Jetography, G. Salam (p. 34) Physics with jets Dijet resonances 1/N dn/dbin / R=1.3 qq, M = 100 GeV SISCone, R=1.3, f=0.75 Q w f=0.24 = 42.3 GeV Dijet mass: scan over R [Pythia 6.4] arxiv: ρ L from Q w f= qq, M = 100 GeV SISCone, f=0.75 arxiv: dijet mass [GeV] R After scanning, summarise quality v. R. Minimum BEST picture not so different from crude analytical estimate

132 Towards Jetography, G. Salam (p. 35) Physics with jets Dijet resonances Scan through q q mass values ρ L from Q w f= m qq = 100 GeV qq, M = 100 GeV SISCone, f=0.75 arxiv: R Best R is at minimum of curve Best R depends strongly on mass of system Increases with mass, just like crude analytical prediction NB: current analytics too crude BUT: so far, LHC s plans involve running with fixed smallish R values e.g. CMS arxiv: NB: 100,000 plots for various jet algorithms, narrow qq and gg resonances from Cacciari, Rojo, GPS & Soyez 08

133 Towards Jetography, G. Salam (p. 35) Physics with jets Dijet resonances Scan through q q mass values ρ L from Q w f= m qq = 150 GeV qq, M = 150 GeV SISCone, f=0.75 arxiv: R Best R is at minimum of curve Best R depends strongly on mass of system Increases with mass, just like crude analytical prediction NB: current analytics too crude BUT: so far, LHC s plans involve running with fixed smallish R values e.g. CMS arxiv: NB: 100,000 plots for various jet algorithms, narrow qq and gg resonances from Cacciari, Rojo, GPS & Soyez 08

134 Towards Jetography, G. Salam (p. 35) Physics with jets Dijet resonances Scan through q q mass values ρ L from Q w f= m qq = 200 GeV qq, M = 200 GeV SISCone, f=0.75 arxiv: R Best R is at minimum of curve Best R depends strongly on mass of system Increases with mass, just like crude analytical prediction NB: current analytics too crude BUT: so far, LHC s plans involve running with fixed smallish R values e.g. CMS arxiv: NB: 100,000 plots for various jet algorithms, narrow qq and gg resonances from Cacciari, Rojo, GPS & Soyez 08

135 Towards Jetography, G. Salam (p. 35) Physics with jets Dijet resonances Scan through q q mass values ρ L from Q w f= m qq = 300 GeV qq, M = 300 GeV SISCone, f=0.75 arxiv: R Best R is at minimum of curve Best R depends strongly on mass of system Increases with mass, just like crude analytical prediction NB: current analytics too crude BUT: so far, LHC s plans involve running with fixed smallish R values e.g. CMS arxiv: NB: 100,000 plots for various jet algorithms, narrow qq and gg resonances from Cacciari, Rojo, GPS & Soyez 08

136 Towards Jetography, G. Salam (p. 35) Physics with jets Dijet resonances Scan through q q mass values ρ L from Q w f= m qq = 500 GeV qq, M = 500 GeV SISCone, f=0.75 arxiv: R Best R is at minimum of curve Best R depends strongly on mass of system Increases with mass, just like crude analytical prediction NB: current analytics too crude BUT: so far, LHC s plans involve running with fixed smallish R values e.g. CMS arxiv: NB: 100,000 plots for various jet algorithms, narrow qq and gg resonances from Cacciari, Rojo, GPS & Soyez 08

137 Towards Jetography, G. Salam (p. 35) Physics with jets Dijet resonances Scan through q q mass values ρ L from Q w f= m qq = 700 GeV qq, M = 700 GeV SISCone, f=0.75 arxiv: R Best R is at minimum of curve Best R depends strongly on mass of system Increases with mass, just like crude analytical prediction NB: current analytics too crude BUT: so far, LHC s plans involve running with fixed smallish R values e.g. CMS arxiv: NB: 100,000 plots for various jet algorithms, narrow qq and gg resonances from Cacciari, Rojo, GPS & Soyez 08

138 Towards Jetography, G. Salam (p. 35) Physics with jets Dijet resonances Scan through q q mass values ρ L from Q w f= m qq = 1000 GeV qq, M = 1000 GeV SISCone, f=0.75 arxiv: R Best R is at minimum of curve Best R depends strongly on mass of system Increases with mass, just like crude analytical prediction NB: current analytics too crude BUT: so far, LHC s plans involve running with fixed smallish R values e.g. CMS arxiv: NB: 100,000 plots for various jet algorithms, narrow qq and gg resonances from Cacciari, Rojo, GPS & Soyez 08

139 Towards Jetography, G. Salam (p. 35) Physics with jets Dijet resonances Scan through q q mass values ρ L from Q w f= m qq = 2000 GeV qq, M = 2000 GeV SISCone, f=0.75 arxiv: R Best R is at minimum of curve Best R depends strongly on mass of system Increases with mass, just like crude analytical prediction NB: current analytics too crude BUT: so far, LHC s plans involve running with fixed smallish R values e.g. CMS arxiv: NB: 100,000 plots for various jet algorithms, narrow qq and gg resonances from Cacciari, Rojo, GPS & Soyez 08

140 Towards Jetography, G. Salam (p. 35) Physics with jets Dijet resonances Scan through q q mass values ρ L from Q w f= m qq = 4000 GeV qq, M = 4000 GeV SISCone, f=0.75 arxiv: R Best R is at minimum of curve Best R depends strongly on mass of system Increases with mass, just like crude analytical prediction NB: current analytics too crude BUT: so far, LHC s plans involve running with fixed smallish R values e.g. CMS arxiv: NB: 100,000 plots for various jet algorithms, narrow qq and gg resonances from Cacciari, Rojo, GPS & Soyez 08

141 Towards Jetography, G. Salam (p. 35) Physics with jets Dijet resonances Scan through q q mass values ρ L from Q w f= m qq = 4000 GeV qq, M = 4000 GeV SISCone, f=0.75 arxiv: R Best R is at minimum of curve Best R depends strongly on mass of system Increases with mass, just like crude analytical prediction NB: current analytics too crude BUT: so far, LHC s plans involve running with fixed smallish R values e.g. CMS arxiv: NB: 100,000 plots for various jet algorithms, narrow qq and gg resonances from Cacciari, Rojo, GPS & Soyez 08

142 Towards Jetography, G. Salam (p. 35) Physics with jets Dijet resonances Scan through q q mass values ρ L from Q w f= m qq = 4000 GeV qq, M = 4000 GeV SISCone, f=0.75 arxiv: R Best R is at minimum of curve Best R depends strongly on mass of system Increases with mass, just like crude analytical prediction NB: current analytics too crude BUT: so far, LHC s plans involve running with fixed smallish R values e.g. CMS arxiv: NB: 100,000 plots for various jet algorithms, narrow qq and gg resonances from Cacciari, Rojo, GPS & Soyez 08

143 Towards Jetography, G. Salam (p. 36) Physics with jets Dijet resonances

144 Towards Jetography, G. Salam (p. 37) Physics with jets Boosted heavy particles How about task of resolving separate jets from separate partons? Illustrate in context of boosted H b b reconstruction

145 Towards Jetography, G. Salam (p. 38) Physics with jets Boosted heavy particles E.g.: WH/ZH search LHC Signal is W lν, H b b. Backgrounds include Wb b, t t lνb bjj,... Studied e.g. in ATLAS TDR Difficulties, e.g. gg t t has lνb b with same intrinsic mass scale, but much higher partonic luminosity Need exquisite control of bkgd shape H b Try a long shot? Go to high p t (p th, p tv > 200 GeV) Lose 95% of signal, but more efficient? Maybe kill t t &gainclarity? W e,μ b ν

146 Towards Jetography, G. Salam (p. 38) Physics with jets Boosted heavy particles E.g.: WH/ZH search LHC Signal is W lν, H b b. Backgrounds include Wb b, t t lνb bjj,... Studied e.g. in ATLAS TDR Difficulties, e.g. gg t t has lνb b with same intrinsic mass scale, but much higher partonic luminosity Need exquisite control of bkgd shape pp WH lνb b +bkgds ATLAS TDR H b Try a long shot? Go to high p t (p th, p tv > 200 GeV) Lose 95% of signal, but more efficient? Maybe kill t t &gainclarity? W e,μ b ν

147 Towards Jetography, G. Salam (p. 38) Physics with jets Boosted heavy particles E.g.: WH/ZH search LHC Signal is W lν, H b b. Backgrounds include Wb b, t t lνb bjj,... Studied e.g. in ATLAS TDR Difficulties, e.g. gg t t has lνb b with same intrinsic mass scale, but much higher partonic luminosity Need exquisite control of bkgd shape pp WH lνb b +bkgds ATLAS TDR b b H Try a long shot? Go to high p t (p th, p tv > 200 GeV) Lose 95% of signal, but more efficient? Maybe kill t t &gainclarity? e,μ W ν

148 Towards Jetography, G. Salam (p. 38) Physics with jets Boosted heavy particles E.g.: WH/ZH search LHC Signal is W lν, H b b. Backgrounds include Wb b, t t lνb bjj,... Studied e.g. in ATLAS TDR pp WH lνb b +bkgds ATLAS TDR Difficulties, e.g. gg t t has lνb b with same intrinsic mass scale, but much higher partonic luminosity Question: What s the best strategy to identify the two-pronged structure of the boosted Higgs decay? Need exquisite control of bkgd shape b b H Try a long shot? Go tohigh p t (p th, p tv > 200 GeV) Lose 95% of signal, but more efficient? Maybe kill t t &gainclarity? e,μ W ν

149 Towards Jetography, G. Salam (p. 39) Physics with jets Boosted heavy particles Past methods Use k t alg. s distance measure (rel. trans. mom.) to cut out QCD bkgd: Use k t jet-algorithm s hierarchy to split the jets d k t ij Y-splitter =min(p 2 ti, p2 tj )ΔR2 ij only partially correlated with mass

150 Towards Jetography, G. Salam (p. 39) Physics with jets Boosted heavy particles Past methods Use k t alg. s distance measure (rel. trans. mom.) to cut out QCD bkgd: Use k t jet-algorithm s hierarchy to split the jets d k t ij Y-splitter =min(p 2 ti, p2 tj )ΔR2 ij only partially correlated with mass

151 Towards Jetography, G. Salam (p. 40) Physics with jets Boosted heavy particles Our tool The Cambridge/Aachen jet alg. Dokshitzer et al 97 Wengler & Wobisch 98 Work out ΔR 2 ij =Δy 2 ij +Δφ2 ij between all pairs of objects i, j; Recombine the closest pair; Repeat until all objects separated by ΔR ij > R. [in FastJet] Gives hierarchical view of the event; work through it backwards to analyse jet

152 Towards Jetography, G. Salam (p. 40) Physics with jets Boosted heavy particles Our tool The Cambridge/Aachen jet alg. Dokshitzer et al 97 Wengler & Wobisch 98 Work out ΔR 2 ij =Δy 2 ij +Δφ2 ij between all pairs of objects i, j; Recombine the closest pair; Repeat until all objects separated by ΔR ij > R. [in FastJet] Gives hierarchical view of the event; work through it backwards to analyse jet k t algorithm Cam/Aachen algorithm Allows you to dial the correct R to keep perturbative radiation, but throw out UE

153 Towards Jetography, G. Salam (p. 41) Physics with jets Boosted heavy particles pp ZH ν νb H =115GeV Herwig Jimmy FastJet 2.3 SIGNAL Zbb BACKGROUND Cluster event, C/A, R=1.2 Butterworth, Davison, Rubin & GPS 08 arbitrary norm.

154 Towards Jetography, G. Salam (p. 41) Physics with jets Boosted heavy particles pp ZH ν νb H =115GeV Herwig Jimmy FastJet 2.3 SIGNAL Zbb BACKGROUND Fill it in, show jets more clearly Butterworth, Davison, Rubin & GPS 08 arbitrary norm.

155 Towards Jetography, G. Salam (p. 41) Physics with jets Boosted heavy particles pp ZH ν νb H =115GeV Herwig Jimmy FastJet SIGNAL 200 < p tz < 250 GeV m H [GeV] Zbb BACKGROUND < p tz < 250 GeV Consider hardest jet, m = 150 GeV Butterworth, Davison, Rubin & GPS m H [GeV] arbitrary norm.

156 Towards Jetography, G. Salam (p. 41) Physics with jets Boosted heavy particles pp ZH ν νb H =115GeV Herwig Jimmy FastJet SIGNAL 200 < p tz < 250 GeV m H [GeV] Zbb BACKGROUND < p tz < 250 GeV split: m = 150 GeV, max(m 1,m2) m =0.92 repeat Butterworth, Davison, Rubin & GPS m H [GeV] arbitrary norm.

157 Towards Jetography, G. Salam (p. 41) Physics with jets Boosted heavy particles pp ZH ν νb H =115GeV Herwig Jimmy FastJet SIGNAL 200 < p tz < 250 GeV m H [GeV] Zbb BACKGROUND < p tz < 250 GeV split: m = 139 GeV, max(m 1,m2) m =0.37 mass drop Butterworth, Davison, Rubin & GPS m H [GeV] arbitrary norm.

158 Towards Jetography, G. Salam (p. 41) Physics with jets Boosted heavy particles pp ZH ν νb H =115GeV Herwig Jimmy FastJet SIGNAL 200 < p tz < 250 GeV m H [GeV] Zbb BACKGROUND < p tz < 250 GeV check: y 12 p t2 p t1 0.7 OK + 2 b-tags (anti-qcd) Butterworth, Davison, Rubin & GPS m H [GeV] arbitrary norm.

159 Towards Jetography, G. Salam (p. 41) Physics with jets Boosted heavy particles pp ZH ν νb H =115GeV Herwig Jimmy FastJet SIGNAL 200 < p tz < 250 GeV m H [GeV] Zbb BACKGROUND < p tz < 250 GeV R filt =0.3 Butterworth, Davison, Rubin & GPS m H [GeV] arbitrary norm.

160 Towards Jetography, G. Salam (p. 41) Physics with jets Boosted heavy particles pp ZH ν νb H =115GeV Herwig Jimmy FastJet SIGNAL 200 < p tz < 250 GeV m H [GeV] Zbb BACKGROUND < p tz < 250 GeV R filt =0.3: take 3 hardest, m = 117 GeV Butterworth, Davison, Rubin & GPS m H [GeV] arbitrary norm.

161 Towards Jetography, G. Salam (p. 42) Physics with jets Boosted heavy particles Jet-alg comparison Cross section for signal and the Z +jets background in the leptonic Z channel for 200 < p TZ /GeV < 600 and 110 < m J /GeV < 125, with perfect b-tagging; shown for our jet definition (C/A MD-F), and other standard ones close to their optimal R values. Jet definition σ S /fb σ B /fb S/ B fb C/A, R =1.2, MD-F k t, R =1.0, y cut SISCone, R = anti-k t, R =

162 Towards Jetography, G. Salam (p. 43) Physics with jets Boosted heavy particles combine HZ and HW, p t > 200 GeV Take Z l + l, Z ν ν, W lν l = e,μ p tv, p th > 200 GeV η V, η H < 2.5 Assume real/fake b-tag rates of 0.6/0.02. Some extra cuts in HW channels to reject t t. Assume m H = 115 GeV. At 5σ for 30 fb 1 this looks like a competitive channel for light Higgs discovery. A powerful method! Currently under study in the LHC experiments