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 C. N. Yang Institute for Theoretical Physics Stony Brook 9 February 2010
Towards Jetography, G. Salam (p. 2) Introduction Startup (again) for LHC
Towards Jetography, G. Salam (p. 2) Introduction Startup (again) for LHC October 2009
Towards Jetography, G. Salam (p. 2) Introduction Startup (again) for LHC 7 November: first beam (picture: CMS) October 2009
Towards Jetography, G. Salam (p. 2) Introduction Startup (again) for LHC December: 10 6 collisions at October s = 900 GeV2009
Towards Jetography, G. Salam (p. 2) Introduction Startup (again) for LHC December: collisions at s = October 2360 GeV2009
Towards Jetography, G. Salam (p. 3) Introduction Parton fragmentation quark Gluon emission: de dθ α s E θ 1 At low scales: α s 1
Towards Jetography, G. Salam (p. 3) Introduction Parton fragmentation quark θ gluon Gluon emission: de dθ α s E θ 1 At low scales: α s 1
Towards Jetography, G. Salam (p. 3) Introduction Parton fragmentation quark Gluon emission: de dθ α s E θ 1 At low scales: α s 1
Towards Jetography, G. Salam (p. 3) Introduction Parton fragmentation quark non perturbative hadronisation Gluon emission: de dθ α s E θ 1 At low scales: α s 1
Towards Jetography, G. Salam (p. 3) 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
Towards Jetography, G. Salam (p. 3) 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
Towards Jetography, G. Salam (p. 4) Introduction Seeing v. defining jets Jets are what we see. Clearly(?) 2 jets here How many jets do you see? Do you really want to ask yourself this question for 10 9 events?
Towards Jetography, G. Salam (p. 4) Introduction Seeing v. defining jets q q Jets are what we see. Clearly(?) 2 jets here How many jets do you see? Do you really want to ask yourself this question for 10 9 events?
Towards Jetography, G. Salam (p. 4) Introduction Seeing v. defining jets Jets are what we see. Clearly(?) 2 jets here How many jets do you see? Do you really want to ask yourself this question for 10 9 events?
Towards Jetography, G. Salam (p. 4) Introduction Seeing v. defining jets Jets are what we see. Clearly(?) 2 jets here How many jets do you see? Do you really want to ask yourself this question for 10 9 events?
Towards Jetography, G. Salam (p. 4) Introduction Seeing v. defining jets Jets are what we see. Clearly(?) 2 jets here How many jets do you see? Do you really want to ask yourself this question for 10 9 events?
Towards Jetography, G. Salam (p. 4) Introduction Seeing v. defining jets Jets are what we see. Clearly(?) 2 jets here How many jets do you see? Do you really want to ask yourself this question for 10 9 events?
Towards Jetography, G. Salam (p. 6) 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
Towards Jetography, G. Salam (p. 7) Introduction QCD jets flowchart Jet (definitions) provide central link between expt., theory and theory And jets are an input to almost all analyses
Towards Jetography, G. Salam (p. 7) Introduction QCD jets flowchart Jet (definitions) provide central link between expt., theory and theory And jets are an input to almost all analyses
Towards Jetography, G. Salam (p. 8) Introduction Talk Structure The different kinds of jet algorithm The historical problems with them ( Snowmass criteria ) and some of the solutions Speed, infrared safety Understanding the physics of jet algorithms the momentum of a jet v. the momentum of a parton Doing better physics with jets Dijet mass reconstruction Low-mass Higgs-boson search
Towards Jetography, G. Salam (p. 9) 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
Towards Jetography, G. Salam (p. 10) Two broad classes Sequential recombination algorithms k t algorithm Catani, Dokshizter, Olsson, Seymour, Turnock, Webber 91 93 Ellis, Soper 93 Find smallest of all d ij = min(kti 2,k2 tj ) R2 ij /R2 and d ib = ki 2 Recombine i,j (if ib: i jet) Repeat Bottom-up jets: Sequential recombination (attempt to invert QCD NB: hadron branching) collider variables Rij 2 = (φ i φ j ) 2 + (y i y j ) 2 rapidity y i = 1 2 ln E i+p zi E i p zi R ij is boost invariant angle R sets minimal interjet angle
Towards Jetography, G. Salam (p. 10) Two broad classes Sequential recombination algorithms k t algorithm Catani, Dokshizter, Olsson, Seymour, Turnock, Webber 91 93 Ellis, Soper 93 Find smallest of all d ij = min(k 2 ti,k2 tj ) R2 ij /R2 and d ib = k 2 i Recombine i,j (if ib: i jet) Repeat NB: hadron collider variables R 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 R ij is boost invariant angle R sets minimal interjet angle
Towards Jetography, G. Salam (p. 10) Two broad classes Sequential recombination algorithms k t algorithm Catani, Dokshizter, Olsson, Seymour, Turnock, Webber 91 93 Ellis, Soper 93 Find smallest of all d ij = min(k 2 ti,k2 tj ) R2 ij /R2 and d ib = k 2 i Recombine i,j (if ib: i jet) Repeat NB: hadron collider variables R 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 R ij is boost invariant angle R sets minimal interjet angle
Towards Jetography, G. Salam (p. 10) Two broad classes Sequential recombination algorithms k t algorithm Catani, Dokshizter, Olsson, Seymour, Turnock, Webber 91 93 Ellis, Soper 93 Find smallest of all d ij = min(k 2 ti,k2 tj ) R2 ij /R2 and d ib = k 2 i Recombine i,j (if ib: i jet) Repeat NB: hadron collider variables R 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 R ij is boost invariant angle R sets minimal interjet angle
Towards Jetography, G. Salam (p. 10) Two broad classes Sequential recombination algorithms k t algorithm Catani, Dokshizter, Olsson, Seymour, Turnock, Webber 91 93 Ellis, Soper 93 Find smallest of all d ij = min(k 2 ti,k2 tj ) R2 ij /R2 and d ib = k 2 i Recombine i,j (if ib: i jet) Repeat NB: hadron collider variables R 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 R ij is boost invariant angle R sets minimal interjet angle
Towards Jetography, G. Salam (p. 10) Two broad classes Sequential recombination algorithms k t algorithm Catani, Dokshizter, Olsson, Seymour, Turnock, Webber 91 93 Ellis, Soper 93 Find smallest of all d ij = min(k 2 ti,k2 tj ) R2 ij /R2 and d ib = k 2 i Recombine i,j (if ib: i jet) Repeat NB: hadron collider variables R 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 R ij is boost invariant angle R sets minimal interjet angle
Towards Jetography, G. Salam (p. 10) Two broad classes Sequential recombination algorithms k t algorithm Catani, Dokshizter, Olsson, Seymour, Turnock, Webber 91 93 Ellis, Soper 93 Find smallest of all d ij = min(k 2 ti,k2 tj ) R2 ij /R2 and d ib = k 2 i Recombine i,j (if ib: i jet) Repeat NB: hadron collider variables R 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 R ij is boost invariant angle R sets minimal interjet angle
Towards Jetography, G. Salam (p. 10) Two broad classes Sequential recombination algorithms k t algorithm Catani, Dokshizter, Olsson, Seymour, Turnock, Webber 91 93 Ellis, Soper 93 Find smallest of all d ij = min(k 2 ti,k2 tj ) R2 ij /R2 and d ib = k 2 i Recombine i,j (if ib: i jet) Repeat R ij > R NB: hadron collider variables R 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 R ij is boost invariant angle R sets minimal interjet angle
Towards Jetography, G. Salam (p. 10) Two broad classes Sequential recombination algorithms k t algorithm Catani, Dokshizter, Olsson, Seymour, Turnock, Webber 91 93 Ellis, Soper 93 Find smallest of all d ij = min(k 2 ti,k2 tj ) R2 ij /R2 and d ib = k 2 i Recombine i,j (if ib: i jet) Repeat R ij > R NB: hadron collider variables R 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 R 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
Towards Jetography, G. Salam (p. 11) 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 Top-down jets: cone algorithms (energy flow conserved by QCD)
Towards Jetography, G. Salam (p. 11) 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
Towards Jetography, G. Salam (p. 11) 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
Towards Jetography, G. Salam (p. 11) 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
Towards Jetography, G. Salam (p. 11) 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
Towards Jetography, G. Salam (p. 11) 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
Towards Jetography, G. Salam (p. 11) 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
Towards Jetography, G. Salam (p. 11) 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
Towards Jetography, G. Salam (p. 11) 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
Towards Jetography, G. Salam (p. 11) 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
Towards Jetography, G. Salam (p. 11) 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
Towards Jetography, G. Salam (p. 11) 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 What seeds do you use? All particles above some threshold Done originally [JetClu, Atlas] Additionally from midpoints between stable cones Midpoint cone [Tevatron Run II]
Towards Jetography, G. Salam (p. 11) 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 What seeds do you use? All particles above some threshold Done originally [JetClu, Atlas] Additionally from midpoints between stable cones Midpoint cone [Tevatron Run II]
Towards Jetography, G. Salam (p. 12) Snowmass Readying jet technology for the LHC era [a.k.a. satisfying Snowmass]
Towards Jetography, G. Salam (p. 13) Snowmass Snowmass accords Snowmass Accord (1990):
Towards Jetography, G. Salam (p. 13) 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, not practical for O ( 10 9) events Can be reduced to N ln N (60s 20ms) Cacciari & GPS 05 + CGAL
Towards Jetography, G. Salam (p. 13) 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
Towards Jetography, G. Salam (p. 14) Snowmass Cone IR issues JetClu (& Atlas Cone) in Wjj @ 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
Towards Jetography, G. Salam (p. 14) Snowmass Cone IR issues JetClu (& Atlas Cone) in Wjj @ 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
Towards Jetography, G. Salam (p. 14) Snowmass Cone IR issues JetClu (& Atlas Cone) in Wjj @ 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
Towards Jetography, G. Salam (p. 14) Snowmass Cone IR issues JetClu (& Atlas Cone) in Wjj @ 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
Towards Jetography, G. Salam (p. 14) Snowmass Cone IR issues JetClu (& Atlas Cone) in Wjj @ 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
Towards Jetography, G. Salam (p. 15) 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 lnp 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 + 1 jet LO NLO NLO NLO 3 jets none LO LO NLO [nlojet++] W/Z + 2 jets none LO LO NLO [MCFM] m jet in 2j + X none none none LO NB: 50,000,000$/ /CHF/e investment in NLO Multi-jet contexts much more sensitive: ubiquitous at LHC And LHC will rely on QCD for background double-checks extraction of cross sections, extraction of parameters
Towards Jetography, G. Salam (p. 15) Snowmass Cone IR issues IRC safety & real-life Real life does not have infinities, but pert. infinity leaves a real-life trace α 2 s + α3 s + α4 s α2 s + α3 s + α4 s lnp t/λ α 2 s + α3 s + α3 s }{{} BOTH WASTED Among consequences of IR unsafety: Last meaningful order JetClu, ATLAS MidPoint CMS it. cone Known at cone [IC-SM] [IC mp-sm] [IC-PR] Inclusive jets LO NLO NLO NLO ( NNLO) W/Z + 1 jet LO NLO NLO NLO 3 jets none LO LO NLO [nlojet++] W/Z + 2 jets none LO LO NLO [MCFM] m jet in 2j + X none none none LO NB: 50,000,000$/ /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
Towards Jetography, G. Salam (p. 15) 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 lnp 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 + 1 jet LO NLO NLO NLO 3 jets none LO LO NLO [nlojet++] W/Z + 2 jets 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
Towards Jetography, G. Salam (p. 16) Snowmass Cone IR issues Two directions Fix stable-cone finding SISCone How do we solve cone IR safety problems? GPS & Soyez 07 Same family as Tev. Run II alg Invent "cone-like" alg. anti-kt Cacciari, GPS & Soyez 08
Towards Jetography, G. Salam (p. 17) Snowmass Cone IR issues Essential characteristic of cones? Cone (ICPR)
Towards Jetography, G. Salam (p. 17) 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
Towards Jetography, G. Salam (p. 17) 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.
Towards Jetography, G. Salam (p. 17) 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 ) R2 ij Regularly held against k t
Towards Jetography, G. Salam (p. 17) 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 jets are irregular cone-based way of getting k t alg. Because soft junk clusters together first: d ij = min(k 2 ti,k2 tj ) R2 ij Regularly held against k t Is there some other, non circular jets?
Towards Jetography, G. Salam (p. 18) 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 ) R2 ij anti-k t: d ij = R 2 ij max(k 2 ti, k2 tj ) Hard stuff clusters with nearest neighbour Privilege collinear divergence over soft divergence Cacciari, GPS & Soyez 08
Towards Jetography, G. Salam (p. 18) 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 ) R2 ij anti-k t: d ij = R 2 ij max(k 2 ti, k2 tj ) Hard stuff clusters with nearest neighbour Privilege collinear divergence over soft divergence Cacciari, GPS & Soyez 08
Towards Jetography, G. Salam (p. 18) 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 ) R2 ij anti-k t: d ij = R 2 ij max(k 2 ti, k2 tj ) Hard stuff clusters with nearest neighbour Privilege collinear divergence over soft divergence Cacciari, GPS & Soyez 08
Towards Jetography, G. Salam (p. 18) 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 ) R2 ij anti-k t: d ij = R 2 ij max(k 2 ti, k2 tj ) Hard stuff clusters with nearest neighbour Privilege collinear divergence over soft divergence Cacciari, GPS & Soyez 08
Towards Jetography, G. Salam (p. 18) 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 ) R2 ij anti-k t: d ij = R 2 ij max(k 2 ti, k2 tj ) Hard stuff clusters with nearest neighbour Privilege collinear divergence over soft divergence Cacciari, GPS & Soyez 08
Towards Jetography, G. Salam (p. 18) 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 ) R2 ij anti-k t: d ij = R 2 ij max(k 2 ti, k2 tj ) Hard stuff clusters with nearest neighbour Privilege collinear divergence over soft divergence Cacciari, GPS & Soyez 08
Towards Jetography, G. Salam (p. 18) 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 ) R2 ij anti-k t: d ij = R 2 ij max(k 2 ti, k2 tj ) Hard stuff clusters with nearest neighbour Privilege collinear divergence over soft divergence Cacciari, GPS & Soyez 08
Towards Jetography, G. Salam (p. 18) 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 ) R2 ij anti-k t: d ij = R 2 ij max(k 2 ti, k2 tj ) Hard stuff clusters with nearest neighbour Privilege collinear divergence over soft divergence Cacciari, GPS & Soyez 08
Towards Jetography, G. Salam (p. 18) 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 ) R2 ij anti-k t: d ij = R 2 ij max(k 2 ti, k2 tj ) Hard stuff clusters with nearest neighbour Privilege collinear divergence over soft divergence Cacciari, GPS & Soyez 08
Towards Jetography, G. Salam (p. 18) 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 ) R2 ij anti-k t: d ij = R 2 ij max(k 2 ti, k2 tj ) Hard stuff clusters with nearest neighbour Privilege collinear divergence over soft divergence Cacciari, GPS & Soyez 08
Towards Jetography, G. Salam (p. 18) 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 ) R2 ij anti-k t: d ij = R 2 ij max(k 2 ti, k2 tj ) Hard stuff clusters with nearest neighbour Privilege collinear divergence over soft divergence Cacciari, GPS & Soyez 08
Towards Jetography, G. Salam (p. 18) 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 ) R2 ij anti-k t: d ij = R 2 ij max(k 2 ti, k2 tj ) Hard stuff clusters with nearest neighbour Privilege collinear divergence over soft divergence Cacciari, GPS & Soyez 08
Towards Jetography, G. Salam (p. 18) 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 ) R2 ij anti-k t: d ij = R 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
Towards Jetography, G. Salam (p. 19) 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 ) R 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 http://fastjet.fr/ (Cacciari, GPS & Soyez 05-09)
Towards Jetography, G. Salam (p. 20) Snowmass A collection of algs ATLAS: first dijet event, with anti-k t
Towards Jetography, G. Salam (p. 21) Snowmass A collection of algs CMS: first dijet event, with anti-k t
Towards Jetography, G. Salam (p. 22) 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?
Towards Jetography, G. Salam (p. 23) 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
Towards Jetography, G. Salam (p. 23) 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
Towards Jetography, G. Salam (p. 24) 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]
Towards Jetography, G. Salam (p. 24) 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]
Towards Jetography, G. Salam (p. 24) 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]
Towards Jetography, G. Salam (p. 25) Physics of jets Perturbative p 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 lnr 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
Towards Jetography, G. Salam (p. 26) Physics of jets Non-perturbative p t Jet p t v. parton p t : hadronisation? Hadronisation: the parton-shower hadrons transition Method: infrared finite α s à la Dokshitzer & Webber 95 prediction based on e + e event shape data could have been deduced from old work Korchemsky & Sterman 95 Seymour 97 Main result 0.4 GeV p t,jet p t,parton shower 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.
Towards Jetography, G. Salam (p. 27) Physics of jets Non-perturbative p t Underlying Event (UE) Naive prediction (UE colour dipole between pp): { p t 0.4 GeV R2 2 CF q q dipole gluon dipole C A DWT Pythia tune or ATLAS Jimmy tune tell you: p t 10 15 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
Towards Jetography, G. Salam (p. 27) Physics of jets Non-perturbative p t Underlying Event (UE) Naive prediction (UE colour dipole between pp): { p t 0.4 GeV R2 2 CF q q dipole gluon dipole C A DWT Pythia tune or ATLAS Jimmy tune tell you: p t 10 15 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
Towards Jetography, G. Salam (p. 28) 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 p t,pt αsc i π lnr lnr lnr ln1.35r p t,hadr 0.4 GeVC i R 0.7? 1? area = πr 2 0.81 ± 0.28 0.81 ± 0.26 1 0.25 +πr 2 C i πb 0 ln αs(q 0) α s(rp t) 0.52 ± 0.41 0.08 ± 0.19 0 0.12 ± 0.07 In words: 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)
Towards Jetography, G. Salam (p. 29) Physics with jets Can we benefit from this understanding in our use of jets?
Towards Jetography, G. Salam (p. 30) 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
Towards Jetography, G. Salam (p. 31) Physics with jets Dijet resonances PT radiation: q : p t α sc F π p t lnr What R is best for an isolated jet? E.g. to reconstruct m X (p tq + p t q ) q Hadronisation: q : p t C F R 0.4 GeV p q X q p Underlying event: q,g : p t R2 2.5 15 GeV 2 q Minimise fluctuations in p t Use crude approximation: p 2 t p t 2 in small-r limit (?!) cf. Dasgupta, Magnea & GPS 07
Towards Jetography, G. Salam (p. 31) Physics with jets Dijet resonances PT radiation: q : p t α sc F π p t lnr Hadronisation: q : p t C F R Underlying event: 0.4 GeV q,g : p t R2 2.5 15 GeV 2 Minimise fluctuations in p t Use crude approximation: p 2 t p t 2 What R is best for an isolated jet? δp t 2 pert + δp t 2 h + δp t 2 UE [GeV2 ] 30 25 20 15 10 5 50 GeV quark jet LHC quark jets p t = 50 GeV δp t 2 h δp t 2 UE δp t 2 pert 0 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1 R in small-r limit (?!) cf. Dasgupta, Magnea & GPS 07
Towards Jetography, G. Salam (p. 31) Physics with jets Dijet resonances PT radiation: q : p t α sc F π p t lnr Hadronisation: q : p t C F R Underlying event: 0.4 GeV q,g : p t R2 2.5 15 GeV 2 Minimise fluctuations in p t Use crude approximation: p 2 t p t 2 What R is best for an isolated jet? δp t 2 pert + δp t 2 h + δp t 2 UE [GeV2 ] 50 40 30 20 10 LHC quark jets p t = 1 TeV 1 TeV quark jet δp t 2 pert δp t 2 UE 0 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1 R in small-r limit (?!) cf. Dasgupta, Magnea & GPS 07
Towards Jetography, G. Salam (p. 31) Physics with jets Dijet resonances PT radiation: What R is best for an isolated jet? q : p t α sc F π p t lnr 40 At low p t, small R limits relative impact of UE Hadronisation: q : p t C 30 At high Fp t, perturbative effects dominate over 0.4 GeV R non-perturbative R best 1. Underlying event: q,g : p t R2 2.5 15 GeV 2 Minimise fluctuations in p t Use crude approximation: p 2 t p t 2 δp t 2 pert + δp t 2 h + δp t 2 UE [GeV2 ] 50 20 10 LHC quark jets p t = 1 TeV 1 TeV quark jet δp t 2 pert δp t 2 UE 0 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1 R in small-r limit (?!) cf. Dasgupta, Magnea & GPS 07
Towards Jetography, G. Salam (p. 32) Physics with jets Dijet resonances 1/N dn/dbin / 2 0.08 0.06 0.04 0.02 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:0810.1304 p Resonance X dijets q q X q p 0 60 80 100 120 140 dijet mass [GeV] q
Towards Jetography, G. Salam (p. 32) Physics with jets Dijet resonances 1/N dn/dbin / 2 0.08 0.06 0.04 0.02 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:0810.1304 p Resonance X dijets jet q q X q p 0 60 80 100 120 140 dijet mass [GeV] jet q
Towards Jetography, G. Salam (p. 32) Physics with jets Dijet resonances 1/N dn/dbin / 2 0.08 0.06 0.04 0.02 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:0810.1304 p Resonance X dijets jet q q X q p 0 60 80 100 120 140 dijet mass [GeV] jet q
Towards Jetography, G. Salam (p. 32) Physics with jets Dijet resonances 1/N dn/dbin / 2 0.08 0.06 0.04 0.02 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:0810.1304 p Resonance X dijets jet q q X q p 0 60 80 100 120 140 dijet mass [GeV] jet q
Towards Jetography, G. Salam (p. 32) Physics with jets Dijet resonances 1/N dn/dbin / 2 0.08 0.06 0.04 0.02 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:0810.1304 p Resonance X dijets jet q q X q p 0 60 80 100 120 140 dijet mass [GeV] jet q
Towards Jetography, G. Salam (p. 32) Physics with jets Dijet resonances 1/N dn/dbin / 2 0.08 0.06 0.04 0.02 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:0810.1304 p Resonance X dijets jet q q X q p 0 60 80 100 120 140 dijet mass [GeV] jet q
Towards Jetography, G. Salam (p. 32) Physics with jets Dijet resonances 1/N dn/dbin / 2 0.08 0.06 0.04 0.02 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:0810.1304 p Resonance X dijets jet q q X q p 0 60 80 100 120 140 dijet mass [GeV] jet q
Towards Jetography, G. Salam (p. 32) Physics with jets Dijet resonances 1/N dn/dbin / 2 0.08 0.06 0.04 0.02 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:0810.1304 p Resonance X dijets jet q q X q p 0 60 80 100 120 140 dijet mass [GeV] jet q
Towards Jetography, G. Salam (p. 32) Physics with jets Dijet resonances 1/N dn/dbin / 2 0.08 0.06 0.04 0.02 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:0810.1304 p Resonance X dijets jet q q X q p 0 60 80 100 120 140 dijet mass [GeV] jet q
Towards Jetography, G. Salam (p. 32) Physics with jets Dijet resonances 1/N dn/dbin / 2 0.08 0.06 0.04 0.02 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:0810.1304 p Resonance X dijets jet q q X q p 0 60 80 100 120 140 dijet mass [GeV] jet q
Towards Jetography, G. Salam (p. 32) Physics with jets Dijet resonances 1/N dn/dbin / 2 0.08 0.06 0.04 0.02 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:0810.1304 p Resonance X dijets jet q q X q p 0 60 80 100 120 140 dijet mass [GeV] jet q
Towards Jetography, G. Salam (p. 32) Physics with jets Dijet resonances 1/N dn/dbin / 2 0.08 0.06 0.04 0.02 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:0810.1304 p Resonance X dijets jet q q X q p 0 60 80 100 120 140 dijet mass [GeV] jet q
Towards Jetography, G. Salam (p. 32) Physics with jets Dijet resonances 1/N dn/dbin / 2 0.08 0.06 0.04 0.02 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:0810.1304 ρ L from Q w f=0.24 3 2.5 2 1.5 qq, M = 100 GeV SISCone, f=0.75 arxiv:0810.1304 0 60 80 100 120 140 dijet mass [GeV] 1 0.5 1 1.5 R After scanning, summarise quality v. R. Minimum BEST picture not so different from crude analytical estimate
Towards Jetography, G. Salam (p. 33) Physics with jets Dijet resonances Scan through q q mass values ρ L from Q w f=0.24 3 2.5 2 1.5 1 m qq = 100 GeV qq, M = 100 GeV SISCone, f=0.75 arxiv:0810.1304 0.5 1 1.5 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:0807.4961 R NB: 100,000 plots for various jet algorithms, narrow qq and gg resonances from http://quality.fastjet.fr Cacciari, Rojo, GPS & Soyez 08
Towards Jetography, G. Salam (p. 33) Physics with jets Dijet resonances Scan through q q mass values ρ L from Q w f=0.24 3 2.5 2 1.5 1 m qq = 150 GeV qq, M = 150 GeV SISCone, f=0.75 arxiv:0810.1304 0.5 1 1.5 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:0807.4961 R NB: 100,000 plots for various jet algorithms, narrow qq and gg resonances from http://quality.fastjet.fr Cacciari, Rojo, GPS & Soyez 08
Towards Jetography, G. Salam (p. 33) Physics with jets Dijet resonances Scan through q q mass values ρ L from Q w f=0.24 3 2.5 2 1.5 1 m qq = 200 GeV qq, M = 200 GeV SISCone, f=0.75 arxiv:0810.1304 0.5 1 1.5 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:0807.4961 R NB: 100,000 plots for various jet algorithms, narrow qq and gg resonances from http://quality.fastjet.fr Cacciari, Rojo, GPS & Soyez 08
Towards Jetography, G. Salam (p. 33) Physics with jets Dijet resonances Scan through q q mass values ρ L from Q w f=0.24 3 2.5 2 1.5 1 m qq = 300 GeV qq, M = 300 GeV SISCone, f=0.75 arxiv:0810.1304 0.5 1 1.5 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:0807.4961 R NB: 100,000 plots for various jet algorithms, narrow qq and gg resonances from http://quality.fastjet.fr Cacciari, Rojo, GPS & Soyez 08
Towards Jetography, G. Salam (p. 33) Physics with jets Dijet resonances Scan through q q mass values ρ L from Q w f=0.24 3 2.5 2 1.5 1 m qq = 500 GeV qq, M = 500 GeV SISCone, f=0.75 arxiv:0810.1304 0.5 1 1.5 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:0807.4961 R NB: 100,000 plots for various jet algorithms, narrow qq and gg resonances from http://quality.fastjet.fr Cacciari, Rojo, GPS & Soyez 08
Towards Jetography, G. Salam (p. 33) Physics with jets Dijet resonances Scan through q q mass values ρ L from Q w f=0.24 3 2.5 2 1.5 1 m qq = 700 GeV qq, M = 700 GeV SISCone, f=0.75 arxiv:0810.1304 0.5 1 1.5 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:0807.4961 R NB: 100,000 plots for various jet algorithms, narrow qq and gg resonances from http://quality.fastjet.fr Cacciari, Rojo, GPS & Soyez 08
Towards Jetography, G. Salam (p. 33) Physics with jets Dijet resonances Scan through q q mass values ρ L from Q w f=0.24 3 2.5 2 1.5 1 m qq = 1000 GeV qq, M = 1000 GeV SISCone, f=0.75 arxiv:0810.1304 0.5 1 1.5 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:0807.4961 R NB: 100,000 plots for various jet algorithms, narrow qq and gg resonances from http://quality.fastjet.fr Cacciari, Rojo, GPS & Soyez 08
Towards Jetography, G. Salam (p. 33) Physics with jets Dijet resonances Scan through q q mass values ρ L from Q w f=0.24 3 2.5 2 1.5 1 m qq = 2000 GeV qq, M = 2000 GeV SISCone, f=0.75 arxiv:0810.1304 0.5 1 1.5 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:0807.4961 R NB: 100,000 plots for various jet algorithms, narrow qq and gg resonances from http://quality.fastjet.fr Cacciari, Rojo, GPS & Soyez 08
Towards Jetography, G. Salam (p. 33) Physics with jets Dijet resonances Scan through q q mass values ρ L from Q w f=0.24 3 2.5 2 1.5 1 m qq = 4000 GeV qq, M = 4000 GeV SISCone, f=0.75 arxiv:0810.1304 0.5 1 1.5 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:0807.4961 R NB: 100,000 plots for various jet algorithms, narrow qq and gg resonances from http://quality.fastjet.fr Cacciari, Rojo, GPS & Soyez 08
Towards Jetography, G. Salam (p. 33) Physics with jets Dijet resonances Scan through q q mass values ρ L from Q w f=0.24 3 2.5 2 1.5 1 m qq = 4000 GeV qq, M = 4000 GeV SISCone, f=0.75 arxiv:0810.1304 0.5 1 1.5 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:0807.4961 R NB: 100,000 plots for various jet algorithms, narrow qq and gg resonances from http://quality.fastjet.fr Cacciari, Rojo, GPS & Soyez 08
Towards Jetography, G. Salam (p. 33) Physics with jets Dijet resonances Scan through q q mass values ρ L from Q w f=0.24 3 2.5 2 1.5 1 m qq = 4000 GeV qq, M = 4000 GeV SISCone, f=0.75 arxiv:0810.1304 0.5 1 1.5 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:0807.4961 R NB: 100,000 plots for various jet algorithms, narrow qq and gg resonances from http://quality.fastjet.fr Cacciari, Rojo, GPS & Soyez 08
Towards Jetography, G. Salam (p. 34) Physics with jets Dijet resonances http://quality.fastjet.fr/
Towards Jetography, G. Salam (p. 35) Physics with jets Boosted heavy particles The dijet mass is a classic jets analysis. But LHC also opens up characteristically new kinematic regions, because s m EW. We can and should make use of this Illustrated in next slides, for Higgs search with m H = 115 GeV, H b b
Towards Jetography, G. Salam (p. 36) Physics with jets Boosted heavy particles E.g.: WH/ZH search channel @ 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 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 & gain clarity? W e,µ H b b ν
Towards Jetography, G. Salam (p. 36) Physics with jets Boosted heavy particles E.g.: WH/ZH search channel @ 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 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 & gain clarity? W e,µ b ν
Towards Jetography, G. Salam (p. 36) Physics with jets Boosted heavy particles E.g.: WH/ZH search channel @ 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 & gain clarity? e,µ W ν
Towards Jetography, G. Salam (p. 36) Physics with jets Boosted heavy particles E.g.: WH/ZH search channel @ LHC Signal is W lν, H b b. Backgrounds include Wb b, t t lνb bjj,... Studied e.g. in ATLAS TDR gg t t has lνb b with same intrinsic Question: mass scale, but much higher partonic luminosity What s the best strategy to identify the Need exquisite control of bkgd shape two-pronged structure of the boosted Higgs decay? b b ATLAS TDR pp WH lνb b + bkgds Difficulties, e.g. 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 & gain clarity? e,µ W ν
Towards Jetography, G. Salam (p. 37) 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 Y-splitter d kt ij = min(p 2 ti,p2 tj ) R2 ij only partially correlated with mass
Towards Jetography, G. Salam (p. 37) 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 Y-splitter d kt ij = min(p 2 ti,p2 tj ) R2 ij only partially correlated with mass
Towards Jetography, G. Salam (p. 38) Physics with jets Boosted heavy particles Our tool The Cambridge/Aachen jet alg. Dokshitzer et al 97 Wengler & Wobisch 98 Work out Rij 2 = y2 ij + φ2 ij between all pairs of objects i,j; Recombine the closest pair; Repeat until all objects separated by R ij > R. [in FastJet] Gives hierarchical view of the event; work through it backwards to analyse jet
Towards Jetography, G. Salam (p. 38) Physics with jets Boosted heavy particles Our tool The Cambridge/Aachen jet alg. Dokshitzer et al 97 Wengler & Wobisch 98 Work out Rij 2 = y2 ij + φ2 ij between all pairs of objects i,j; Recombine the closest pair; Repeat until all objects separated by R 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
Towards Jetography, G. Salam (p. 39) Physics with jets Boosted heavy particles pp ZH ν νb b, @14TeV, m H =115GeV Herwig 6.510 + Jimmy 4.31 + FastJet 2.3 SIGNAL Zbb BACKGROUND Cluster event, C/A, R=1.2 Butterworth, Davison, Rubin & GPS 08 arbitrary norm.
Towards Jetography, G. Salam (p. 39) Physics with jets Boosted heavy particles pp ZH ν νb b, @14TeV, m H =115GeV Herwig 6.510 + Jimmy 4.31 + FastJet 2.3 SIGNAL Zbb BACKGROUND Fill it in, show jets more clearly Butterworth, Davison, Rubin & GPS 08 arbitrary norm.
Towards Jetography, G. Salam (p. 39) Physics with jets Boosted heavy particles pp ZH ν νb b, @14TeV, m H =115GeV Herwig 6.510 + Jimmy 4.31 + FastJet 2.3 0.15 SIGNAL 200 < p tz < 250 GeV 0.1 0.05 0 80 100 120 140 160 m H [GeV] Zbb BACKGROUND 0.008 200 < p tz < 250 GeV 0.006 0.004 Consider hardest jet, m = 150 GeV Butterworth, Davison, Rubin & GPS 08 0.002 0 80 100 120 140 160 m H [GeV] arbitrary norm.
Towards Jetography, G. Salam (p. 39) Physics with jets Boosted heavy particles pp ZH ν νb b, @14TeV, m H =115GeV Herwig 6.510 + Jimmy 4.31 + FastJet 2.3 0.15 SIGNAL 200 < p tz < 250 GeV 0.1 0.05 0 80 100 120 140 160 m H [GeV] Zbb BACKGROUND 0.008 200 < p tz < 250 GeV 0.006 0.004 split: m = 150 GeV, max(m 1,m2) m = 0.92 repeat Butterworth, Davison, Rubin & GPS 08 0.002 0 80 100 120 140 160 m H [GeV] arbitrary norm.
Towards Jetography, G. Salam (p. 39) Physics with jets Boosted heavy particles pp ZH ν νb b, @14TeV, m H =115GeV Herwig 6.510 + Jimmy 4.31 + FastJet 2.3 0.15 SIGNAL 200 < p tz < 250 GeV 0.1 0.05 0 80 100 120 140 160 m H [GeV] Zbb BACKGROUND 0.008 200 < p tz < 250 GeV 0.006 0.004 split: m = 139 GeV, max(m 1,m2) m = 0.37 mass drop Butterworth, Davison, Rubin & GPS 08 0.002 0 80 100 120 140 160 m H [GeV] arbitrary norm.
Towards Jetography, G. Salam (p. 39) Physics with jets Boosted heavy particles pp ZH ν νb b, @14TeV, m H =115GeV Herwig 6.510 + Jimmy 4.31 + FastJet 2.3 0.15 SIGNAL 200 < p tz < 250 GeV 0.1 0.05 0 80 100 120 140 160 m H [GeV] Zbb BACKGROUND 0.008 200 < p tz < 250 GeV 0.006 0.004 check: y 12 p t2 p t1 0.7 OK + 2 b-tags (anti-qcd) Butterworth, Davison, Rubin & GPS 08 0.002 0 80 100 120 140 160 m H [GeV] arbitrary norm.
Towards Jetography, G. Salam (p. 39) Physics with jets Boosted heavy particles pp ZH ν νb b, @14TeV, m H =115GeV Herwig 6.510 + Jimmy 4.31 + FastJet 2.3 0.15 SIGNAL 200 < p tz < 250 GeV 0.1 0.05 0 80 100 120 140 160 m H [GeV] Zbb BACKGROUND 0.008 200 < p tz < 250 GeV 0.006 0.004 R filt = 0.3 Butterworth, Davison, Rubin & GPS 08 0.002 0 80 100 120 140 160 m H [GeV] arbitrary norm.
Towards Jetography, G. Salam (p. 39) Physics with jets Boosted heavy particles pp ZH ν νb b, @14TeV, m H =115GeV Herwig 6.510 + Jimmy 4.31 + FastJet 2.3 0.15 SIGNAL 200 < p tz < 250 GeV 0.1 0.05 0 80 100 120 140 160 m H [GeV] Zbb BACKGROUND 0.008 200 < p tz < 250 GeV 0.006 0.004 R filt = 0.3: take 3 hardest, m = 117 GeV Butterworth, Davison, Rubin & GPS 08 0.002 0 80 100 120 140 160 m H [GeV] arbitrary norm.
Towards Jetography, G. Salam (p. 40) 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
Towards Jetography, G. Salam (p. 41) Physics with jets Boosted heavy particles ATLAS results Leptonic channel What changes compared to particle-level analysis? 1.5σ as compared to 2.1σ Expected given larger mass window
Towards Jetography, G. Salam (p. 41) Physics with jets Boosted heavy particles ATLAS results Missing E T channel What changes compared to particle-level analysis? 1.5σ as compared to 3σ Suffers: some events redistributed to semi-leptonic channel
Towards Jetography, G. Salam (p. 41) Physics with jets Boosted heavy particles ATLAS results Semi-leptonic channel What changes compared to particle-level analysis? 3σ as compared to 3σ Benefits: some events redistributed from missing E T channel
Towards Jetography, G. Salam (p. 42) Physics with jets Boosted heavy particles ATLAS combined results Likelihood-based analysis of all three channels together gives signal significance of 3.7σ for 30 fb 1 To be compared with 4.2σ in hadron-level analysis for m H = 120 GeV K-factors not included: don t affect significance ( 1.5 for VH, 2 2.5 for Vbb) With 5% (20%) background uncertainty, ATLAS result becomes 3.5σ (2.8σ) Comparison to other channels at ATLAS (m H = 120, 30 fb 1 ): gg H γγ WW H ττ gg H ZZ 4.2σ 4.9σ 2.6σ Extracted from 0901.0512
Towards Jetography, G. Salam (p. 43) Physics with jets Boosted heavy particles The potential of better jet finding X gg VH,H b b tth,h b b RPV χ 0 1 qqq 1/N dn/dbin / 2 0.08 0.06 0.04 0.02 gg, M = 2000 GeV k t, R=0.4 Q w f=0.13 = 190 GeV arxiv:0810.1304 m 1 /(100GeV) dn/dbin per fb -1 70000 60000 50000 40000 30000 20000 10000 signal + background background (just dijets) signal Cam/Aachen R=0.7 p t1 > 500 GeV 0 1900 2000 2100 dijet mass [GeV] Herwig 6.5 + Jimmy 4.3 0 0 50 100 150 200 m 1 [GeV] 1/N dn/dbin / 2 0.08 0.06 0.04 0.02 gg, M = 2000 GeV C/A-filt, R=1.3 Q w f=0.13 = 53 GeV (subtr.) arxiv:0810.1304 m 1 /(100GeV) dn/dbin per fb -1 800 600 400 200 signal + background background (just dijets) signal Cam/Aachen + filt, R=0.7 0 1900 2000 2100 dijet mass [GeV] p t1 > 500 GeV, z split > 0.15, m bc > 0.25 m abc p t3, p t4 > {100,80} GeV, y 13, y 14 < 1.5 0 0 50 100 150 200 m 1 [GeV] 1) Cacciari, Rojo, GPS & Soyez 08; 2) Butterworth, Davison, Rubin & GPS 08; 3) Plehn, GPS & Spannowsky 09; 4) Butterworth, Ellis, Raklev & GPS 09.
Towards Jetography, G. Salam (p. 44) Conclusions Conclusions
Towards Jetography, G. Salam (p. 45) Conclusions Conclusions There are no longer any valid reasons for using jet algorithms that are incompatible with the Snowmass criteria. LHC experiments are adopting the new tools Individual analyses need to follow suit It s time to move forwards with the question of how best to use jets in searches Examples here show two things: Good jet-finding brings significant gains There s room for serious QCD theory input into optimising jet use Not the only way of doing things But brings more insight than trial & error MC This opens the road towards Jetography, QCD-based autofocus for jets
Towards Jetography, G. Salam (p. 46) Extras EXTRAS
Towards Jetography, G. Salam (p. 47) Extras speed k t and geometry There are N(N 1)/2 distances d ij surely we have to calculate them all in order to find smallest? k t distance measure is partly geometrical: min i,j d ij min(min{kti,k 2 tj} R 2 ij) 2 i,j = min(kti R 2 ij) 2 i,j = min(kti 2 min i j ւ 2D dist. on rap., φ cylinder ) R 2 ij In words: for each i look only at the k t distance to its 2D geometrical nearest neighbour (GNN). k t distance need only be calculated between GNNs Each point has 1 GNN need only calculate N d ij s Cacciari & GPS, 05
Towards Jetography, G. Salam (p. 47) Extras speed k t and geometry There are N(N 1)/2 distances d ij surely we have to calculate them all in order to find smallest? k t distance measure is partly geometrical: min i,j d ij min(min{kti,k 2 tj} R 2 ij) 2 i,j = min(kti R 2 ij) 2 i,j = min(kti 2 min i j ւ 2D dist. on rap., φ cylinder ) R 2 ij In words: for each i look only at the k t distance to its 2D geometrical nearest neighbour (GNN). k t distance need only be calculated between GNNs Each point has 1 GNN need only calculate N d ij s Cacciari & GPS, 05
Towards Jetography, G. Salam (p. 47) Extras speed k t and geometry There are N(N 1)/2 distances d ij surely we have to calculate them all in order to find smallest? k t distance measure is partly geometrical: min i,j d ij min(min{kti,k 2 tj} R 2 ij) 2 i,j = min(kti R 2 ij) 2 i,j = min(kti 2 min i j ւ 2D dist. on rap., φ cylinder ) R 2 ij In words: for each i look only at the k t distance to its 2D geometrical nearest neighbour (GNN). k t distance need only be calculated between GNNs Each point has 1 GNN need only calculate N d ij s Cacciari & GPS, 05
Towards Jetography, G. Salam (p. 47) Extras speed k t and geometry There are N(N 1)/2 distances d ij surely we have to calculate them all in order to find smallest? k t distance measure is partly geometrical: min i,j d ij min(min{kti,k 2 tj} R 2 ij) 2 i,j = min(kti R 2 ij) 2 i,j = min(kti 2 min i j ւ 2D dist. on rap., φ cylinder ) R 2 ij In words: for each i look only at the k t distance to its 2D geometrical nearest neighbour (GNN). k t distance need only be calculated between GNNs Each point has 1 GNN need only calculate N d ij s Cacciari & GPS, 05
Towards Jetography, G. Salam (p. 47) Extras speed k t and geometry There are N(N 1)/2 distances d ij surely we have to calculate them all in order to find smallest? k t distance measure is partly geometrical: min i,j d ij min(min{kti,k 2 tj} R 2 ij) 2 i,j = min(kti R 2 ij) 2 i,j = min(kti 2 min i j ւ 2D dist. on rap., φ cylinder ) R 2 ij In words: for each i look only at the k t distance to its 2D geometrical nearest neighbour (GNN). k t distance need only be calculated between GNNs Each point has 1 GNN need only calculate N d ij s Cacciari & GPS, 05
Towards Jetography, G. Salam (p. 48) Extras speed 2d nearest-neighbours 4 2 10 Given a set of vertices on plane (1...10) a Voronoi diagram partitions plane into cells containing all points closest to each vertex Dirichlet 1850, Voronoi 1908 A vertex s nearest other vertex is al- 1 7 8 5 3 How 6 does use of GNN help? Aren t 9 there still N2 N 2 2 R2 ways ij 2 to check...? in an adjacent cell. Geometrical nearest neighbour finding is a classic problem in the field of Computational Geometry E.g. GNN of point 7 must be among 1,4,2,8,3 (it is 3) Construction of Voronoi diagram for N points: N ln N time Fortune 88 Update of 1 point in Voronoi diagram: expected lnn time Devillers 99 [+ related work by other authors] Convenient C++ package available: CGAL, http://www.cgal.org with help of CGAL, k t clustering can be done in N ln N time Coded in the FastJet package (v1), Cacciari & GPS 06
Towards Jetography, G. Salam (p. 48) Extras speed 2d nearest-neighbours 4 2 1 7 8 3 10 5 6 9 Given a set of vertices on plane (1...10) a Voronoi diagram partitions plane into cells containing all points closest to each vertex Dirichlet 1850, Voronoi 1908 A vertex s nearest other vertex is always in an adjacent cell. E.g. GNN of point 7 must be among 1,4,2,8,3 (it is 3) Construction of Voronoi diagram for N points: N ln N time Fortune 88 Update of 1 point in Voronoi diagram: expected lnn time Devillers 99 [+ related work by other authors] Convenient C++ package available: CGAL, http://www.cgal.org with help of CGAL, k t clustering can be done in N ln N time Coded in the FastJet package (v1), Cacciari & GPS 06
Towards Jetography, G. Salam (p. 48) Extras speed 2d nearest-neighbours 4 2 1 7 8 3 10 5 6 9 Given a set of vertices on plane (1...10) a Voronoi diagram partitions plane into cells containing all points closest to each vertex Dirichlet 1850, Voronoi 1908 A vertex s nearest other vertex is always in an adjacent cell. E.g. GNN of point 7 must be among 1,4,2,8,3 (it is 3) Construction of Voronoi diagram for N points: N ln N time Fortune 88 Update of 1 point in Voronoi diagram: expected lnn time Devillers 99 [+ related work by other authors] Convenient C++ package available: CGAL, http://www.cgal.org with help of CGAL, k t clustering can be done in N ln N time Coded in the FastJet package (v1), Cacciari & GPS 06
Towards Jetography, G. Salam (p. 48) Extras speed 2d nearest-neighbours 4 2 1 7 8 3 10 5 6 9 Given a set of vertices on plane (1...10) a Voronoi diagram partitions plane into cells containing all points closest to each vertex Dirichlet 1850, Voronoi 1908 A vertex s nearest other vertex is always in an adjacent cell. E.g. GNN of point 7 must be among 1,4,2,8,3 (it is 3) Construction of Voronoi diagram for N points: N ln N time Fortune 88 Update of 1 point in Voronoi diagram: expected lnn time Devillers 99 [+ related work by other authors] Convenient C++ package available: CGAL, http://www.cgal.org with help of CGAL, k t clustering can be done in N ln N time Coded in the FastJet package (v1), Cacciari & GPS 06
Towards Jetography, G. Salam (p. 49) Extras speed Speeds in 2005 t / s 10 2 10 1 1 10-1 10-2 10-3 R=0.7 KtJet k t CDF MidPoint (seeds > 0 GeV) CDF MidPoint (seeds > 1 GeV) CDF JetClu (very unsafe) 3.4 GHz P4, 2 GB 10-4 Tevatron LHC lo-lumi LHC hi-lumi LHC Pb-Pb 100 1000 10000 100000 N FastJet (v2.x), codes all developments, natively (k t, Cam/Aachen, anti-k t ) or as plugins (SISCone): Cacciari, GPS & Soyez 05 09 http://fastjet.fr/
Towards Jetography, G. Salam (p. 49) Extras speed Speeds in 2009 t / s 10 2 10 1 1 10-1 10-2 10-3 R=0.7 KtJet k t CDF MidPoint (seeds > 0 GeV) CDF MidPoint (seeds > 1 GeV) CDF JetClu (very unsafe) Seedless IR Safe Cone (SISCone) FastJet anti-k t Cam/Aachen k t 10-4 LHC lo-lumi LHC hi-lumi LHC Pb-Pb 100 1000 10000 100000 N FastJet (v2.x), codes all developments, natively (k t, Cam/Aachen, anti-k t ) or as plugins (SISCone): Cacciari, GPS & Soyez 05 09 http://fastjet.fr/
Towards Jetography, G. Salam (p. 50) Extras ICPR unsafety Iterative Cone [with progressive removal] Procedure: Find one stable cone By iterating from hardest seed particle Call it a jet; remove its particles from the event; repeat
Towards Jetography, G. Salam (p. 50) Extras ICPR unsafety Iterative Cone [with progressive removal] Procedure: Find one stable cone By iterating from hardest seed particle Call it a jet; remove its particles from the event; repeat
Towards Jetography, G. Salam (p. 50) Extras ICPR unsafety Iterative Cone [with progressive removal] Procedure: Find one stable cone By iterating from hardest seed particle Call it a jet; remove its particles from the event; repeat
Towards Jetography, G. Salam (p. 50) Extras ICPR unsafety Iterative Cone [with progressive removal] Procedure: Find one stable cone By iterating from hardest seed particle Call it a jet; remove its particles from the event; repeat
Towards Jetography, G. Salam (p. 50) Extras ICPR unsafety Iterative Cone [with progressive removal] Procedure: Find one stable cone By iterating from hardest seed particle Call it a jet; remove its particles from the event; repeat
Towards Jetography, G. Salam (p. 50) Extras ICPR unsafety Iterative Cone [with progressive removal] Procedure: Find one stable cone By iterating from hardest seed particle Call it a jet; remove its particles from the event; repeat
Towards Jetography, G. Salam (p. 50) Extras ICPR unsafety Iterative Cone [with progressive removal] Procedure: Find one stable cone By iterating from hardest seed particle Call it a jet; remove its particles from the event; repeat
Towards Jetography, G. Salam (p. 50) Extras ICPR unsafety Iterative Cone [with progressive removal] Procedure: Find one stable cone By iterating from hardest seed particle Call it a jet; remove its particles from the event; repeat
Towards Jetography, G. Salam (p. 50) Extras ICPR unsafety Iterative Cone [with progressive removal] Procedure: Find one stable cone By iterating from hardest seed particle Call it a jet; remove its particles from the event; repeat
Towards Jetography, G. Salam (p. 50) Extras ICPR unsafety Iterative Cone [with progressive removal] Procedure: Find one stable cone By iterating from hardest seed particle Call it a jet; remove its particles from the event; repeat
Towards Jetography, G. Salam (p. 50) Extras ICPR unsafety Iterative Cone [with progressive removal] Procedure: Find one stable cone By iterating from hardest seed particle Call it a jet; remove its particles from the event; repeat
Towards Jetography, G. Salam (p. 50) Extras ICPR unsafety Iterative Cone [with progressive removal] Procedure: Find one stable cone By iterating from hardest seed particle Call it a jet; remove its particles from the event; repeat
Towards Jetography, G. Salam (p. 50) Extras ICPR unsafety Iterative Cone [with progressive removal] Procedure: Find one stable cone By iterating from hardest seed particle 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
Towards Jetography, G. Salam (p. 51) Extras ICPR unsafety ICPR iteration issue 500 cone iteration cone axis cone p T (GeV/c) 400 300 200 100 0 1 0 1 rapidity Collinear splitting can modify the hard jets: ICPR algorithms are collinear unsafe = perturbative calculations give
Towards Jetography, G. Salam (p. 51) Extras ICPR unsafety ICPR iteration issue 500 cone iteration cone axis cone p T (GeV/c) 400 300 200 100 0 1 0 1 rapidity Collinear splitting can modify the hard jets: ICPR algorithms are collinear unsafe = perturbative calculations give
Towards Jetography, G. Salam (p. 51) Extras ICPR unsafety ICPR iteration issue 500 cone iteration cone axis cone p T (GeV/c) 400 300 200 100 0 1 0 1 rapidity Collinear splitting can modify the hard jets: ICPR algorithms are collinear unsafe = perturbative calculations give
Towards Jetography, G. Salam (p. 51) Extras ICPR unsafety ICPR iteration issue 500 cone iteration cone axis cone p T (GeV/c) 400 300 200 100 0 1 0 1 rapidity Collinear splitting can modify the hard jets: ICPR algorithms are collinear unsafe = perturbative calculations give
Towards Jetography, G. Salam (p. 51) Extras ICPR unsafety ICPR iteration issue 500 cone iteration cone axis cone p T (GeV/c) 400 300 200 100 0 1 0 1 rapidity Collinear splitting can modify the hard jets: ICPR algorithms are collinear unsafe = perturbative calculations give
Towards Jetography, G. Salam (p. 51) Extras ICPR unsafety ICPR iteration issue 500 cone iteration cone axis cone p T (GeV/c) 400 300 200 100 0 1 0 1 jet 1 rapidity Collinear splitting can modify the hard jets: ICPR algorithms are collinear unsafe = perturbative calculations give
Towards Jetography, G. Salam (p. 51) Extras ICPR unsafety ICPR iteration issue 500 cone iteration cone axis cone p T (GeV/c) 400 300 200 100 0 1 0 1 jet 1 rapidity Collinear splitting can modify the hard jets: ICPR algorithms are collinear unsafe = perturbative calculations give
Towards Jetography, G. Salam (p. 51) Extras ICPR unsafety ICPR iteration issue 500 cone iteration cone axis cone p T (GeV/c) 400 300 200 100 0 1 0 1 rapidity Collinear splitting can modify the hard jets: ICPR algorithms are collinear unsafe = perturbative calculations give
Towards Jetography, G. Salam (p. 51) Extras ICPR unsafety ICPR iteration issue 500 cone iteration cone axis cone p T (GeV/c) 400 300 200 100 0 1 0 1 rapidity Collinear splitting can modify the hard jets: ICPR algorithms are collinear unsafe = perturbative calculations give
Towards Jetography, G. Salam (p. 51) Extras ICPR unsafety ICPR iteration issue 500 cone iteration cone axis cone p T (GeV/c) 400 300 200 100 0 1 0 1 rapidity Collinear splitting can modify the hard jets: ICPR algorithms are collinear unsafe = perturbative calculations give
Towards Jetography, G. Salam (p. 51) Extras ICPR unsafety ICPR iteration issue 500 cone iteration cone axis cone p T (GeV/c) 400 300 200 100 0 1 0 1 rapidity Collinear splitting can modify the hard jets: ICPR algorithms are collinear unsafe = perturbative calculations give
Towards Jetography, G. Salam (p. 51) Extras ICPR unsafety ICPR iteration issue 500 cone iteration cone axis cone p T (GeV/c) 400 300 200 100 0 1 0 1 rapidity Collinear splitting can modify the hard jets: ICPR algorithms are collinear unsafe = perturbative calculations give
Towards Jetography, G. Salam (p. 51) Extras ICPR unsafety ICPR iteration issue 500 cone iteration cone axis cone p T (GeV/c) 400 300 200 100 0 1 0 1 jet 1 rapidity Collinear splitting can modify the hard jets: ICPR algorithms are collinear unsafe = perturbative calculations give
Towards Jetography, G. Salam (p. 51) Extras ICPR unsafety ICPR iteration issue 500 cone iteration cone axis cone p T (GeV/c) 400 300 200 100 0 1 0 1 jet 1 rapidity Collinear splitting can modify the hard jets: ICPR algorithms are collinear unsafe = perturbative calculations give
Towards Jetography, G. Salam (p. 51) Extras ICPR unsafety ICPR iteration issue 500 cone iteration cone axis cone p T (GeV/c) 400 300 200 100 0 1 0 1 jet 1 rapidity Collinear splitting can modify the hard jets: ICPR algorithms are collinear unsafe = perturbative calculations give
Towards Jetography, G. Salam (p. 51) Extras ICPR unsafety ICPR iteration issue 500 cone iteration cone axis cone p T (GeV/c) 400 300 200 100 0 1 0 1 jet 1 rapidity Collinear splitting can modify the hard jets: ICPR algorithms are collinear unsafe = perturbative calculations give
Towards Jetography, G. Salam (p. 51) Extras ICPR unsafety ICPR iteration issue 500 cone iteration cone axis cone p T (GeV/c) 400 300 200 100 0 1 0 1 jet 1 rapidity jet 2 Collinear splitting can modify the hard jets: ICPR algorithms are collinear unsafe = perturbative calculations give
Towards Jetography, G. Salam (p. 51) Extras ICPR unsafety ICPR iteration issue 500 cone iteration cone axis cone p T (GeV/c) 400 300 200 100 0 1 0 1 jet 1 rapidity jet 2 Collinear splitting can modify the hard jets: ICPR algorithms are collinear unsafe = perturbative calculations give
Towards Jetography, G. Salam (p. 51) Extras ICPR unsafety ICPR iteration issue 500 cone iteration cone axis cone p T (GeV/c) 400 300 200 100 0 1 0 1 jet 1 rapidity jet 2 Collinear splitting can modify the hard jets: ICPR algorithms are collinear unsafe = perturbative calculations give
Towards Jetography, G. Salam (p. 52) Extras ICPR unsafety Consequences of collinear unsafety Collinear Safe Collinear Unsafe jet 1 jet 1 jet 1 n α s x ( ) n α s x (+ ) Infinities cancel n α s x ( ) jet 1 n jet 2 α s x (+ ) Infinities do not cancel Invalidates perturbation theory
Towards Jetography, G. Salam (p. 52) Extras ICPR unsafety Consequences of collinear unsafety Collinear Safe Collinear Unsafe jet 1 jet 1 jet 1 n α s x ( ) n α s x (+ ) Infinities cancel n α s x ( ) jet 1 n jet 2 α s x (+ ) Infinities do not cancel Invalidates perturbation theory
Towards Jetography, G. Salam (p. 53) Extras IRC unsafety impact Impact of IRC issues in W+3j CDF have measured W+3jet X-section with JetClu (IR 2+1 unsafe). NLO calculation with JetClu would diverge [for zero seed threshold] Strategy for theory: use 2 algs for theory prediction, SISCone & anti-k t ; difference between them is IRC unsafety systematic. With CDF cuts and R choice, difference is O (20%) 10% @ NLO: Ellis, Melnikov & Zanderighi 09 20% exp. systematics
Towards Jetography, G. Salam (p. 53) Extras IRC unsafety impact Impact of IRC issues in W+3j CDF have measured W+3jet X-section with JetClu (IR 2+1 unsafe). NLO calculation with JetClu would diverge [for zero seed threshold] Strategy for theory: use 2 algs for theory prediction, SISCone & anti-k t ; difference between them is IRC unsafety systematic. With CDF cuts and R choice, difference is O (20%) 10% @ NLO: Ellis, Melnikov & Zanderighi 09 20% exp. systematics dσ anti-kt /dp t3 / dσ SISCone /dp t3 2 anti-k t / SISCone Tevatron W + 3jets @ LO (MCFM 5.2) 1.5 1 0.5 η jets < 2, R=0.4 0 20 25 30 35 40 45 50 p t3 [GeV]
Towards Jetography, G. Salam (p. 53) Extras IRC unsafety impact Impact of IRC issues in W+3j CDF have measured W+3jet X-section with JetClu (IR 2+1 unsafe). NLO calculation with JetClu would diverge [for zero seed threshold] Strategy for theory: use 2 algs for theory prediction, SISCone & anti-k t ; difference between them is IRC unsafety systematic. With CDF cuts and R choice, difference is O (20%) 10% @ NLO: Ellis, Melnikov & Zanderighi 09 20% exp. systematics With other cuts and R choice, IRC systematic can be up to 75% Future measurements deserve to be done with IRC safe algs... dσ anti-kt /dp t3 / dσ SISCone /dp t3 2 anti-k t / SISCone Tevatron W + 3jets @ LO (MCFM 5.2) 1.5 1 η jets < 1, R=0.7 0.5 η jets < 2, R=0.7 η jets < 1, R=0.4 η jets < 2, R=0.4 0 20 25 30 35 40 45 50 p t3 [GeV]
Towards Jetography, G. Salam (p. 54) Extras IRC unsafety impact 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
Towards Jetography, G. Salam (p. 54) Extras IRC unsafety impact 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
Towards Jetography, G. Salam (p. 54) Extras IRC unsafety impact 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.