Basics of QCD: jets & jet substructure
Gavin Salam (CERN) with extensive use of material by Matteo Cacciari and Gregory Soyez
ICTP–SAIFR school on QCD and LHC physics July 2015, São Paulo, Brazil 1 JETS Collimated, energetic bunches of particles
Gavin Salam (CERN) QCD basics 4 ICTP-SAIFR school, July 2015 2 Find all papers by ATLAS and CMS 850 records found
Gavin Salam (CERN) QCD basics 4 ICTP-SAIFR school, July 2015 3 Pull out those that refer to one widely used jet-alg 538 records found
> 60% of papers use jets!
Gavin Salam (CERN) QCD basics 4 ICTP-SAIFR school, July 2015 4 Why do we see jets?
[Introduction] [Background knowledge] Why do we see jets? Parton fragmentation
Gluon emission Gluon emission: π+ dE dθdE d✓ quark αs ↵s ≫ 1 1 K E θ E ✓ L ! Z 0 π At low scales:
perturbative + − K Non-perturbative − αs → 1 hadronisation π physics non ↵ 1 s ⇠ High-energy partons unavoidably lead to collimated bunches of hadrons
Gavin Salam (CERN) Jets and jet substructure (1) June 2013 3 / 35 Gavin Salam (CERN) QCD basics 4 ICTP-SAIFR school, July 2015 5 Jet finding as a form of projection [Introduction] [Background knowledge] Jets as projections
π π p 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
ProjectionProjection to tojets jets should should bebe resilient resilient to to QCD QCD effects effects
Gavin Salam (CERN) Jets and jet substructure (1) June 2013 8 / 35 Gavin Salam (CERN) QCD basics 4 ICTP-SAIFR school, July 2015 6 [Introduction]Reconstructing jets is an ambiguous task [Background knowledge] Seeing v. defining jets
Jets are what we see. How many jets do you see? Clearly(?) 2 jets here Do you really want to ask yourself this question for 109 events? Gavin Salam (CERN) QCD basics 4 ICTP-SAIFR school, July 2015 7 Gavin Salam (CERN) Jets and jet substructure (1) June 2013 6 / 35 [Introduction]Reconstructing jets is an ambiguous task [Background knowledge] Seeing v. defining jets
Jets are what we see. How many jets do you see? Clearly(?)2 2clear jets here jets Do you really want to ask yourself this question for 109 events? Gavin Salam (CERN) QCD basics 4 ICTP-SAIFR school, July 2015 7 Gavin Salam (CERN) Jets and jet substructure (1) June 2013 6 / 35 [Introduction]Reconstructing jets is an ambiguous task [Background knowledge] Seeing v. defining jets
Jets are what we see. How many jets do you see? Clearly(?)2 2clear jets here jets 3 jets? Do you really want to ask yourself this question for 109 events? Gavin Salam (CERN) QCD basics 4 ICTP-SAIFR school, July 2015 7 Gavin Salam (CERN) Jets and jet substructure (1) June 2013 6 / 35 [Introduction]Reconstructing jets is an ambiguous task [Background knowledge] Seeing v. defining jets
Jets are what we see. How many jets do you see? Clearly(?)2 2clear jets here jets 3 jets? Do you reallyor 4 want jets? to ask yourself this question for 109 events? Gavin Salam (CERN) QCD basics 4 ICTP-SAIFR school, July 2015 8 Gavin Salam (CERN) Jets and jet substructure (1) June 2013 6 / 35 Gavin Salam (CERN) QCD basics 4 ICTP-SAIFR school, July 2015 9 Make a choice: specify a jet definition
jet definition {pi} {jk} particles, jets 4-momenta, calorimeter towers, ....
• Which particles do you put together into a same jet? • How do you recombine their momenta (4-momentum sum is the obvious choice, right?)
“Jet [definitions] are legal contracts between theorists and experimentalists’’ -- MJ Tannenbaum They’re also a way of organising the information in an event 1000’s of particles per events, up to 20.000,000 events per second
Gavin Salam (CERN) QCD basics 4 ICTP-SAIFR school, July 2015 10 Jet definitions date back to the late 1970s
Sterman and Weinberg, Phys. Rev. Lett. 39, 1436 (1977):
Gavin Salam (CERN) QCD basics 4 ICTP-SAIFR school, July 2015 11 [Theory v. experiment] Key[Cone algorithms] requirement: infraredConsequences and ofcollinear collinear unsafety safety
Collinear Safe Collinear Unsafe
jet 1 jet 1 jet 1 jet 1 jet 2 n n n n αs x (−∞ ) αs x (+∞ ) αs x (−∞ ) αs x (+∞ ) Infinities cancel Infinities do not cancel
Invalidates perturbation theory
Jets lecture 2 (Gavin Salam) CERN Academic Training March/April 2011 9 / 28 Gavin Salam (CERN) QCD basics 4 ICTP-SAIFR school, July 2015 12 hadron-collider kt algorithm
Two parameters, R and pt,min (These are the two parameters in essentially every widely used hadron-collider jet algorithm) R2 d =min(p2 ,p2 ) ij , R2 =(y y )2 +( )2 ij ti tj R2 ij i j i j
Sequential recombination algorithm
1. Find smallest of dij, diB
2. If ij, recombine them 3. If iB, call i a jet and remove from list of particles 4. repeat from step 1 until no particles left Inclusive kt algorithm Only use jets with pt > pt,min S.D. Ellis & Soper, 1993 Catani, Dokshitzer, Seymour & Webber, 1993
Gavin Salam (CERN) QCD basics 4 ICTP-SAIFR school, July 2015 13 Sequential recombination variants
Cambridge/Aachen: the simplest of hadron-collider algorithms
• Recombine pair of objects closest in ΔRij • Repeat until all ΔRij > R — remaining objects are jets
Dokshitzer, Leder, Moretti, Webber ’97 (Cambridge): more involved e+e− form Wobisch & Wengler ’99 (Aachen): simple inclusive hadron-collider form One still applies a pt,min cut to the jets, as for inclusive kt
C/A privileges the collinear divergence of QCD; it ‘ignores’ the soft one
Gavin Salam (CERN) QCD basics 4 ICTP-SAIFR school, July 2015 14 anti-kt
Anti-kt: formulated similarly to inclusive kt, but with 2 1 Rij 1 dij = 2 2 2 ,diB = 2 max(pti,ptj) R pti
Cacciari, GPS & Soyez ’08 [+Delsart unpublished]
Anti-kt privileges the collinear divergence of QCD and disfavours clustering between pairs of soft particles
Most pairwise clusterings involve at least one hard particle
Gavin Salam (CERN) QCD basics 4 ICTP-SAIFR school, July 2015 15 Anti-kt in action
2 Clustering grows 1 Rij 1 dij = 2 2 2 ,diB = 2 around hard cores max(pti,ptj) R pti
Gavin Salam (CERN) QCD basics 4 ICTP-SAIFR school, July 2015 16 Anti-kt in action
2 Clustering grows 1 Rij 1 dij = 2 2 2 ,diB = 2 around hard cores max(pti,ptj) R pti
Gavin Salam (CERN) QCD basics 4 ICTP-SAIFR school, July 2015 16 Anti-kt in action
2 Clustering grows 1 Rij 1 dij = 2 2 2 ,diB = 2 around hard cores max(pti,ptj) R pti
Gavin Salam (CERN) QCD basics 4 ICTP-SAIFR school, July 2015 16 Anti-kt in action
2 Clustering grows 1 Rij 1 dij = 2 2 2 ,diB = 2 around hard cores max(pti,ptj) R pti
Gavin Salam (CERN) QCD basics 4 ICTP-SAIFR school, July 2015 16 Anti-kt in action
2 Clustering grows 1 Rij 1 dij = 2 2 2 ,diB = 2 around hard cores max(pti,ptj) R pti
Gavin Salam (CERN) QCD basics 4 ICTP-SAIFR school, July 2015 16 Anti-kt in action
2 Clustering grows 1 Rij 1 dij = 2 2 2 ,diB = 2 around hard cores max(pti,ptj) R pti
Gavin Salam (CERN) QCD basics 4 ICTP-SAIFR school, July 2015 16 Anti-kt in action
2 Clustering grows 1 Rij 1 dij = 2 2 2 ,diB = 2 around hard cores max(pti,ptj) R pti
Gavin Salam (CERN) QCD basics 4 ICTP-SAIFR school, July 2015 16 Anti-kt in action
2 Clustering grows 1 Rij 1 dij = 2 2 2 ,diB = 2 around hard cores max(pti,ptj) R pti
Gavin Salam (CERN) QCD basics 4 ICTP-SAIFR school, July 2015 16 Anti-kt in action
2 Clustering grows 1 Rij 1 dij = 2 2 2 ,diB = 2 around hard cores max(pti,ptj) R pti
Gavin Salam (CERN) QCD basics 4 ICTP-SAIFR school, July 2015 16 Anti-kt in action
2 Clustering grows 1 Rij 1 dij = 2 2 2 ,diB = 2 around hard cores max(pti,ptj) R pti
Gavin Salam (CERN) QCD basics 4 ICTP-SAIFR school, July 2015 16 Anti-kt in action
2 Clustering grows 1 Rij 1 dij = 2 2 2 ,diB = 2 around hard cores max(pti,ptj) R pti
Gavin Salam (CERN) QCD basics 4 ICTP-SAIFR school, July 2015 16 Anti-kt in action
2 Clustering grows 1 Rij 1 dij = 2 2 2 ,diB = 2 around hard cores max(pti,ptj) R pti
Gavin Salam (CERN) QCD basics 4 ICTP-SAIFR school, July 2015 16 Anti-kt in action
2 Clustering grows 1 Rij 1 dij = 2 2 2 ,diB = 2 around hard cores max(pti,ptj) R pti
Gavin Salam (CERN) QCD basics 4 ICTP-SAIFR school, July 2015 16 Anti-kt in action
2 Clustering grows 1 Rij 1 dij = 2 2 2 ,diB = 2 around hard cores max(pti,ptj) R pti
Gavin Salam (CERN) QCD basics 4 ICTP-SAIFR school, July 2015 16 Anti-kt in action
2 Clustering grows 1 Rij 1 dij = 2 2 2 ,diB = 2 around hard cores max(pti,ptj) R pti
Gavin Salam (CERN) QCD basics 4 ICTP-SAIFR school, July 2015 16 Anti-kt in action
2 Clustering grows 1 Rij 1 dij = 2 2 2 ,diB = 2 around hard cores max(pti,ptj) R pti
Anti-kt gives circular jets (“cone-like”) in a way that’s infrared safe
Gavin Salam (CERN) QCD basics 4 ICTP-SAIFR school, July 2015 16 Linearity: kt v. anti-kt
p /GeV t kt clustering, R=1 40
30 1
20
10 3 2 0 0123y
Gavin Salam (CERN) QCD basics 4 ICTP-SAIFR school, July 2015 17 Linearity: kt v. anti-kt
p /GeV t kt clustering, R=1 40
30 1
20
10 3 2 0 0123y
Gavin Salam (CERN) QCD basics 4 ICTP-SAIFR school, July 2015 18 Linearity: kt v. anti-kt
p /GeV t kt clustering, R=1 40
30 1
20
10 3 2 0 0123y
Gavin Salam (CERN) QCD basics 4 ICTP-SAIFR school, July 2015 19 Linearity: kt v. anti-kt
p /GeV t kt clustering, R=1 40
30 1
20
10 3 2 0 0123y
Gavin Salam (CERN) QCD basics 4 ICTP-SAIFR school, July 2015 20 Linearity: kt v. anti-kt
p /GeV t kt clustering, R=1 40
30 1
20
10 3 2 0 0123y
Gavin Salam (CERN) QCD basics 4 ICTP-SAIFR school, July 2015 21 Linearity: kt v. anti-kt
p /GeV t kt clustering, R=1 40
30 1
20
10 3 2 0 0123y
Gavin Salam (CERN) QCD basics 4 ICTP-SAIFR school, July 2015 22 Linearity: kt v. anti-kt
p /GeV t kt clustering, R=1 40
30 1
20
10 3 2 0 0123y
Gavin Salam (CERN) QCD basics 4 ICTP-SAIFR school, July 2015 23 Linearity: kt v. anti-kt
p /GeV t kt clustering, R=1 40
30 1
20
10 3 2 0 0123 y 50 jet pt v. pt,2 45 40 [GeV]
t,jet 35 p 30 kt alg. 0 5 10 15 p [GeV] Gavin Salam (CERN) t,2 QCD basics 4 ICTP-SAIFR school, July 2015 24 Linearity: kt v. anti-kt
p /GeV p /GeV t kt clustering, R=1 t anti−kt clustering, R=1 40 40
30 1 30 1
20 20
10 10 3 3 2 2 0 0 0123 0123 y y 50 jet pt v. pt,2 45 40 [GeV]
t,jet 35 p 30 kt alg. 0 5 10 15 p [GeV] Gavin Salam (CERN) t,2 QCD basics 4 ICTP-SAIFR school, July 2015 25 Linearity: kt v. anti-kt
p /GeV p /GeV t kt clustering, R=1 t anti−kt clustering, R=1 40 40
30 1 30 1
20 20
10 10 3 3 2 2 0 0 0123 0123 y y 50 jet pt v. pt,2 45 40 [GeV]
t,jet 35 p 30 kt alg. 0 5 10 15 p [GeV] Gavin Salam (CERN) t,2 QCD basics 4 ICTP-SAIFR school, July 2015 26 Linearity: kt v. anti-kt
p /GeV p /GeV t kt clustering, R=1 t anti−kt clustering, R=1 40 40
30 1 30 1
20 20
10 10 3 3 2 2 0 0 0123 0123 y y 50 jet pt v. pt,2 45 40 [GeV]
t,jet 35 p 30 kt alg. 0 5 10 15 p [GeV] Gavin Salam (CERN) t,2 QCD basics 4 ICTP-SAIFR school, July 2015 27 Linearity: kt v. anti-kt
p /GeV p /GeV t kt clustering, R=1 t anti−kt clustering, R=1 40 40
30 1 30 1
20 20
10 10 3 3 2 2 0 0 0123 0123 y y 50 jet pt v. pt,2 45 40 [GeV]
t,jet 35 p 30 kt alg. 0 5 10 15 p [GeV] Gavin Salam (CERN) t,2 QCD basics 4 ICTP-SAIFR school, July 2015 28 Linearity: kt v. anti-kt
p /GeV p /GeV t kt clustering, R=1 t anti−kt clustering, R=1 40 40
30 1 30 1
20 20
10 10 3 3 2 2 0 0 0123 0123 y y 50 jet pt v. pt,2 45 40 [GeV]
t,jet 35 p 30 kt alg. 0 5 10 15 p [GeV] Gavin Salam (CERN) t,2 QCD basics 4 ICTP-SAIFR school, July 2015 29 Linearity: kt v. anti-kt
p /GeV p /GeV t kt clustering, R=1 t anti−kt clustering, R=1 40 40
30 1 30 1
20 20
10 10 3 3 2 2 0 0 0123 0123 y y 50 jet pt v. pt,2 45 40 [GeV]
t,jet 35 p 30 kt alg. 0 5 10 15 p [GeV] Gavin Salam (CERN) t,2 QCD basics 4 ICTP-SAIFR school, July 2015 30 Linearity: kt v. anti-kt
p /GeV p /GeV t kt clustering, R=1 t anti−kt clustering, R=1 40 40
30 1 30 1
20 20
10 10 3 3 2 2 0 0 0123 0123 y y 50 jet pt v. pt,2 45 40 [GeV]
t,jet 35 p 30 kt alg. 0 5 10 15 p [GeV] Gavin Salam (CERN) t,2 QCD basics 4 ICTP-SAIFR school, July 2015 31 Linearity: kt v. anti-kt
p /GeV p /GeV t kt clustering, R=1 t anti−kt clustering, R=1 40 40
30 1 30 1
20 20
10 10 3 3 2 2 0 0 0123 0123 y y 50 50 jet pt v. pt,2 jet pt v. pt,2 45 45 40 40 [GeV] [GeV]
t,jet 35 t,jet 35 p p 30 30 kt alg. anti-kt alg. 0 5 10 15 0 5 10 15 p [GeV] p [GeV] Gavin Salam (CERN) t,2 QCD basics 4 ICTP-SAIFRt,2 school, July 2015 32 // specify a jet definition double R = 0.4 JetDefinition jet_def(antikt_algorithm, R); jet_algorithm can be any one of the four IRC safe algorithms, or also most of the old IRC-unsafe ones, for legacy purposes
// specify the input particles vector
More this afternoon in the tutorial
Gavin Salam (CERN) QCD basics 4 ICTP-SAIFR school, July 2015 33 // specify a jet definition double R = 0.4 JetDefinition jet_def(antikt_algorithm, R); jet_algorithm can be any one of the four IRC safe algorithms, or also most of the old IRC-unsafe ones, for legacy purposes
// specify the input particles vector
// extract the jets vector
// pt of hardest jet double pt_hardest = jets[0].pt();
// constituents of hardest jet vector
More this afternoon in the tutorial
Gavin Salam (CERN) QCD basics 4 ICTP-SAIFR school, July 2015 33 Small v. large jet radius (R)
Small jet radius Large jet radius
single parton @ LO: jet radius irrelevant
Gavin SalamGavin (CERN) Salam (CERN) JetsQCD and basics jet 4 substructure (2) ICTP-SAIFRCFHEP, school, April 2014 July 2015 3 / 1934 Small v. large jet radius (R)
Small jet radius Large jet radius
θ θ
perturbative fragmentation: large jet radius better (it captures more)
Gavin Salam (CERN) Jets and jet substructure (2) CFHEP, April 2014 3 / 19 Small v. large jet radius (R)
Small jet radius Large jet radius L L + + + + 0 0 − − π π K K π π K K π π
non−perturbative non−perturbative
hadronisation hadronisation
θ θ
non-perturbative fragmentation: large jet radius better (it captures more)
Gavin Salam (CERN) Jets and jet substructure (2) CFHEP, April 2014 3 / 19 Pileup
Jet non-perturbative phase (hadronisation)
perturbative radiation
background radiation (underlying event)
Gavin Salam (CERN) QCD basics 4 ICTP-SAIFR school, July 2015 37 Pileup
Jet non-perturbative phase (hadronisation)
perturbative radiation
background radiation (underlying event)
background radiation (pileup)
Gavin Salam (CERN) QCD basics 4 ICTP-SAIFR school, July 2015 37 Pileup
Jet non-perturbative phase (hadronisation)
perturbative radiation Out of time pileup (especially ATLAS) background radiation (underlying event)
background radiation (pileup)
Gavin Salam (CERN) QCD basics 4 ICTP-SAIFR school, July 2015 37 ATLAS
Pileup for real ~ 20 m
a few cm
Gavin Salam (CERN) QCD basics 4 ICTP-SAIFR school, July 2015 38 Small v. large jet radius (R)
Small jet radius Large jet radius L L + + + + 0 0 − − π π K K π π K K π π
non−perturbative non−perturbative
hadronisation hadronisation
θ θ
UE UE
underlying ev. & pileup “noise”: small jet radius better (it captures less)
Gavin Salam (CERN) Jets and jet substructure (2) CFHEP, April 2014 3 / 19 Small v. large jet radius (R)
Small jet radius Large jet radius
multi-hard-parton events: small jet radius better (it resolves partons more effectively)
Gavin Salam (CERN) Jets and jet substructure (2) CFHEP, April 2014 3 / 19 Can we capture all quarks and gluons?
Should we capture all quarks and gluons? pppp →→ tt¯¯tt simulated with Pythia, displayed with Delphes
Gavin Salam (CERN) Jets and jet substructure (2) CFHEP, April 2014 17 / 19 6 partons v. 6 jets?
Alpgen pp → t¯t → 6q fraction of pp→tt→6q events with all Rqq > R 1
no pt cut on quarks 0.8
0.6
0.4
0.2 pp, 7 TeV Alpgen partons 0 0 0.5 1 1.5 R
Gavin Salam (CERN) Jets and jet substructure (2) CFHEP, April 2014 18 / 19 6 partons v. 6 jets?
Alpgen pp → t¯t → 6q fraction of pp→tt→6q events with all Rqq > R 1
require all ptq > 10 GeV 0.8
0.6
0.4
0.2 pp, 7 TeV Alpgen partons 0 0 0.5 1 1.5 R
Gavin Salam (CERN) Jets and jet substructure (2) CFHEP, April 2014 18 / 19 6 partons v. 6 jets?
Alpgen pp → t¯t → 6q fraction of pp→tt→6q events with all Rqq > R 1
require all ptq > 20 GeV 0.8
0.6
0.4
0.2 pp, 7 TeV Alpgen partons 0 0 0.5 1 1.5 R
Gavin Salam (CERN) Jets and jet substructure (2) CFHEP, April 2014 18 / 19 6 partons v. 6 jets?
Alpgen pp → t¯t → 6q fraction of pp→tt→6q events with all Rqq > R 1
require all ptq > 30 GeV 0.8
0.6
0.4
0.2 pp, 7 TeV Alpgen partons 0 0 0.5 1 1.5 R
Gavin Salam (CERN) Jets and jet substructure (2) CFHEP, April 2014 18 / 19 6 partons v. 6 jets?
Alpgen pp → t¯t → 6q Herwig pp → t¯t → hadrons fraction of pp→tt→6q events with all Rqq > R Distribution of number of jets 1 4 Herwig 6.5 (no UE) require all p > 20 GeV tq pp, 7 TeV
0.8 anti-kt R=0.5 3 pt,jet > 20 GeV 0.6
[arb. units] 2
0.4 jets /d n σ d 1 0.2 pp, 7 TeV Alpgen partons 0 0 0 0.5 1 1.5 0 2 4 6 8 10 12 R number of jets
Gavin Salam (CERN) Jets and jet substructure (2) CFHEP, April 2014 18 / 19 Two things that make jets@LHC special
The large hierarchy of scales
√s ⨠ MEW
The huge pileup
npileup ~ 20 – 40
[These involve two opposite extremes: low pt and high pt, which nevertheless talk to each other]
Gavin Salam (CERN) QCD basics 4 ICTP-SAIFR school, July 2015 48 [1 jet ! 2partons] E.g. X t¯t resonancese.g. ttbar of varying resonances difficulty →
RS KK resonances t¯t, from Frederix & Maltoni, 0712.2355 → NB: QCD dijet spectrum is 103 times t¯t ∼ Jets lecture 3 (Gavin Salam) CERN Academic Training March/April 2011 10 / 29 Gavin Salam (CERN) QCD basics 4 ICTP-SAIFR school, July 2015 49 Boosted massive particles fat jets BoostedBoosted EWhadronic scale→ decaysobjects
Normal analyses: two quarks from High-pt regime: EW object X X qq¯ reconstructed as two jets is boosted, decay is collimated, → qq¯ both in same jet
X at rest z single X jet 1 boosted X fat jet (1 −z) jet 2 Happens for pt ! 2m/R pt ! 320 GeV for m = mW , R =0.5
Gavin Salam (CERN/LPTHE/Princeton) Jets in Higgs Searches HC2012 2012-11-18 19 / 29
Gavin Salam (CERN) QCD basics 4 ICTP-SAIFR school, July 2015 50 Papers on jet substructure
Number of papers containing the words ‘jet substructure’ 7
Papers containing "jet substructure" + pioneering papers by Mike Seymour in 1991 and 1994 More than 150 papers 6 (Source: INSPIRE) since 2008 5 (+ some background noise)
4 Pioneered by M. Seymour in the early ‘90s 3 papers / month 2 Exploded around 2008
Butterworth, Cox, Forshaw 1 Mike Seymour
0 1990 1995 2000 2005 2010 2015 year
Gavin Salam (CERN) QCD basics 4 ICTP-SAIFR school, July 2015 51 Tagging & Grooming Two widely used terms though there’s not a consensus about what they mean Tagging • reduces the background, leaves much of signal
Grooming • improves signal mass resolution (removing pileup, etc.), without significantly changing background & signal event numbers
Gavin Salam (CERN) QCD basics 4 ICTP-SAIFR school, July 2015 52 One core idea for tagging
Gavin Salam (CERN) QCD basics 4 ICTP-SAIFR school, July 2015 53 Inside the jet mass [1 jet ! 2partons] Inside the jet mass 0.9 0.9 QCD Jet Mass distribution QCD jet mass distribution has the 0.8 QCD Jet Mass distribution 0.8 Pythia 6.4, qq→qq, no UE Pythia 6.4, qq qq, no UE approximate 0.7 anti-k→t, R=0.7
) 0.7 LHC,anti-k 7 tTeV, R=0.7 ) jet 0.6 LHC, 7 TeV jet 0.6 dN ptR 0.5 αs ln Sudakov 0.5 pt,jets > 700 GeV 0.4 pt,jets > 700 GeV d ln m ∼ m × 0.4 0.3
1/N dN/dlog(m 0.3 Work from ’80s and ’90s 1/N dN/dlog(m 0.2 0.2 + Almeida et al ’08 0.1 0.1 The logarithm comes from integral 0 0 10 10 100 100 m [GeV] over soft divergence of QCD: jetmjet [GeV] 1 2 dz ! m2 z 2 2 pt R
A hard cut on z reduces QCD back- ground & simplifies its shape
Gavin Salam (CERN) QCD basics 4 ICTP-SAIFR school, July 2015 54 Jets lecture 3 (Gavin Salam) CERN Academic Training March/April 2011 15 / 29 [1 jet ! 2partons] Inside the jet mass [1 jet ! 2partons] Inside the jet mass 0.9 Inside the jet mass 0.9 0.8 QCD Jet Mass distribution QCD jet mass distribution has the 0.9 QCD Jet Mass distribution 0.8 Pythia 6.4, qq→qq, no UE QCD jet mass distribution has the QCD JetPythia Mass 6.4, distributionqq→qq, no UE approximate 0.7 0.8 anti-kt, R=0.7
) 0.7 anti-k , R=0.7 Pythia LHC,6.4, qq 7→ tTeVqq, no UE approximate ) jet 0.6 LHC, 7 TeV 0.7 anti-kt, R=0.7 jet 0.6 ) LHC, 7 TeV dN ptR 0.5 jet 0.6 dN αs ln ptR Sudakov 0.5 pt,jets > 700 GeV 0.4 0.5 pt,jets > 700 GeV d ln m ∼ αs ln m × Sudakov 0.4 pt,jets > 700 GeV d ln m ∼ m × 0.3 0.4
1/N dN/dlog(m 0.3 Work from ’80s and ’90s 1/N dN/dlog(m 0.3 Work from ’80s and ’90s
0.2 1/N dN/dlog(m 0.2 + Almeida et al ’08 0.1 0.2 + Almeida et al ’08 0.1 0.1 The logarithm comes from integral 0 0 The logarithm comes from integral 10 10 0 100 100 10 100 over soft divergence of QCD: mjetm [GeV] [GeV] over soft divergence of QCD: jetm [GeV] 1 jet 1 1 1 22 dzdz 2 ! mm2 z ! 2 2 z
z 0.1 p 2R 2 z 0.1 ptt R
2-body2-body phasespacephasespace AA hard hard cut cut on on zz reducesreduces QCD back- back- 0.01 0.01 10 10 100 100 groundground & & simplifies simplifies its its shape shape mjet [GeV] mjet [GeV] Gavin Salam (CERN) QCD basics 4 ICTP-SAIFR school, July 2015 54 JetsJets lecture lecture 3 (Gavin 3 (Gavin Salam) Salam) CERNCERN Academic Academic Training Training March/AprilMarch/April 2011 15 15 / / 29 29 Inside the jet mass [1 jet[1! jet2partons]! 2partons] [1 jet ! 2partons] InsideInside the the jet jet mass mass 0.9 Inside the jet mass 0.9 0.9 0.8 QCD Jet Mass distribution QCDQCD jet jet mass mass distribution distribution has has the the 0.9 QCD Jet Mass distribution 0.8 0.8 QCD PythiaJet Mass 6.4, qq→ distributionqq, no UE QCD jet mass distribution has the QCD JetPythia Mass 6.4, distributionqq→qq, no UE approximate 0.7 0.8 Pythiaanti-k 6.4,t, qqR=0.7→qq, no UE approximate
) 0.7 anti-k , R=0.7 0.7 Pythia LHC,6.4, qq 7anti-k→ tTeVqq, t,no R=0.7 UE approximate ) jet
0.6 ) LHC, 7 TeV 0.7 anti-kLHC,t, R=0.7 7 TeV jet 0.6jet 0.6 ) LHC, 7 TeV dNdN pptRtR 0.5 jet 0.6 dN ααs slnlnptR SudakovSudakov 0.5 0.5 pt,jets > 700 GeV 0.4 0.5 pt,jetspt,jets > 700 > 700 GeV GeV ddlnlnmm∼∼αs ln mm ××Sudakov 0.4 0.4 pt,jets > 700 GeV d ln m ∼ m × 0.3 0.4
1/N dN/dlog(m 0.3 0.3 WorkWork from from ’80s ’80s and and ’90s ’90s 1/N dN/dlog(m 1/N dN/dlog(m 0.3 Work from ’80s and ’90s
0.2 1/N dN/dlog(m 0.2 0.2 ++ Almeida Almeida et et al al ’08 ’08 after cut on z > 0.25 0.1 0.2 after cut on z > 0.25 + Almeida et al ’08 0.1 0.1 0.1 (a la BERS)(a la BERS) TheThe logarithm logarithm comes comes from from integral integral 0 0 0 The logarithm comes from integral 10 10 0 10 100 100 100 10 100 overover soft soft divergence divergence of of QCD: QCD: mjetm [GeV]m [GeV] [GeV] over soft divergence of QCD: jetm jet [GeV] 1 1 jet 1 1 1 12 keep keep 22 dzdzdz 2 cut oncut z on z !mm22 z reject !! m2 2 zz z reject 0.1 2p 2R z 0.1 p 2Rt 2 z 0.1 ptt R
2-body 2-body2-body phasespace 0.01 phasespacephasespace AAA hard hard hard cut cut cut on on onzzzreducesreducesreduces QCD QCD back- back- back- 0.01 0.01 10 100 ground & simplifies its shape 10 10 100 100 groundground & & simplifies simplifies its its shape shape m [GeV] mjetjet [GeV] mjet [GeV] Gavin Salam (CERN)Jets lecture 3 (Gavin Salam) QCD basicsCERN 4 Academic Training ICTP-SAIFRMarch/April school, 2011 July 2015 15 / 2955 JetsJets lecture lecture 3 (Gavin 3 (Gavin Salam) Salam) CERNCERN Academic Academic Training Training March/AprilMarch/April 2011 15 15 / / 29 29 Inside the jet mass
0.9 3.5 0.8 QCD Jet Mass distribution W+jet Jet Mass distribution 3 Pythia 6.4, qq→qq, no UE Pythia 6.4, pp→Wj, no UE
0.7 anti-kt, R=0.7 anti-kt, R=0.7
) LHC, 7 TeV ) 2.5 LHC, 7 TeV
jet 0.6 jet 0.5 2 pt,jets > 700 GeV pt,jets > 700 GeV 0.4 1.5 0.3 1/N dN/dlog(m 1/N dN/dlog(m 1 0.2 after cut on z > 0.25 0.5 0.1 (a la BERS) 0 0 10 100 10 100
mjet [GeV] mjet [GeV] 1 1
keep cut on z reject z 0.1 z 0.1
2-body 2-body phasespace phasespace 0.01 0.01 10 100 10 100
mjet [GeV] mjet [GeV] Gavin Salam (CERN) QCD basics 4 ICTP-SAIFR school, July 2015 56 Inside the jet mass
0.9 3.5 0.8 QCD Jet Mass distribution W+jet Jet Mass distribution 3 Pythia 6.4, qq→qq, no UE Pythia 6.4, pp→Wj, no UE
0.7 anti-kt, R=0.7 anti-kt, R=0.7
) LHC, 7 TeV ) 2.5 LHC, 7 TeV
jet 0.6 jet 0.5 2 pt,jets > 700 GeV pt,jets > 700 GeV 0.4 1.5 0.3 1/N dN/dlog(m 1/N dN/dlog(m 1 0.2 after cut on z > 0.25 after cut on z > 0.25 0.5 0.1 (a la BERS) 0 0 10 100 10 100
mjet [GeV] mjet [GeV] 1 1
keep keep cut on z cut on z reject reject
z 0.1 z 0.1
2-body 2-body phasespace phasespace 0.01 0.01 10 100 10 100
mjet [GeV] mjet [GeV] Gavin Salam (CERN) QCD basics 4 ICTP-SAIFR school, July 2015 57 0.25 qq → qq + Wj mixture 0.2
jet 0.15 pt,jets > 700 GeV anti-kt, R = 0.7 Signal + bkgd 0.1 after cut on z m/N dN/dm
0.05 + CA subjet (z > 0.25)
0 0 50 100 150 200
mjet [GeV]
Gavin Salam (CERN) QCD basics 4 ICTP-SAIFR school, July 2015 58 One core idea for grooming
[see blackboard]
Gavin Salam (CERN) QCD basics 4 ICTP-SAIFR school, July 2015 59 [1 jet ! 2partons] [Some of the other ideas] Noise removal from jets “Grooming”
Groomed jet masses
[Boost 2010 writeup] Plain jet mass (anti-kt)
! Filtering Gavin Salam (CERN) ButterworthQCD basics 4 et al ’08 ICTP-SAIFR school, July 2015 60 ! Pruning Ellis, Vermillion and Walsh ’09 ! Trimming Krohn, Thaler & Wang ’09 [With earlier methods by Seymour ’93 and Kodolova et al ’07; Rubin ’10 for filtering optimisation; also Soper & Spannowsky ’10, ’11]
Jets 2 (M. Cacciari and G. Salam) GGI September 2011 24 / 29 How do the tools work in practice?
Gavin Salam (CERN) QCD basics 4 ICTP-SAIFR school, July 2015 61 Identifying jet substructure: try out anti-kt
How well can an algorithm identify anti-kt algorithm the “blobs” of energy inside a jet that pt/GeV come from different partons?
50 This is crucial for identifying the kinematic variables of the partons in the jet (e.g. z). 40
30
20
10
0 01234y Identifying jet substructure: try out anti-kt
How well can an algorithm identify anti-kt algorithm the “blobs” of energy inside a jet that dmin is dij = 3.57137e−05 pt/GeV come from different partons?
50 This is crucial for identifying the kinematic variables of the partons in the jet (e.g. z). 40
30
20
10
0 01234y Identifying jet substructure: try out anti-kt
How well can an algorithm identify anti-kt algorithm the “blobs” of energy inside a jet that pt/GeV come from different partons?
50 This is crucial for identifying the kinematic variables of the partons in the jet (e.g. z). 40
30
20
10
0 01234y Identifying jet substructure: try out anti-kt
How well can an algorithm identify anti-kt algorithm the “blobs” of energy inside a jet that dmin is dij = 0.000496598 pt/GeV come from different partons?
50 This is crucial for identifying the kinematic variables of the partons in the jet (e.g. z). 40
30
20
10
0 01234y Identifying jet substructure: try out anti-kt
How well can an algorithm identify anti-kt algorithm the “blobs” of energy inside a jet that pt/GeV come from different partons?
50 This is crucial for identifying the kinematic variables of the partons in the jet (e.g. z). 40
30
20
10
0 01234y Identifying jet substructure: try out anti-kt
How well can an algorithm identify anti-kt algorithm the “blobs” of energy inside a jet that dmin is dij = 0.000688842 pt/GeV come from different partons?
50 This is crucial for identifying the kinematic variables of the partons in the jet (e.g. z). 40
30
20
10
0 01234y Identifying jet substructure: try out anti-kt
How well can an algorithm identify anti-kt algorithm the “blobs” of energy inside a jet that pt/GeV come from different partons?
50 This is crucial for identifying the kinematic variables of the partons in the jet (e.g. z). 40
30
20
10
0 01234y Identifying jet substructure: try out anti-kt
How well can an algorithm identify anti-kt algorithm the “blobs” of energy inside a jet that dmin is dij = 0.000805103 pt/GeV come from different partons?
50 This is crucial for identifying the kinematic variables of the partons in the jet (e.g. z). 40
Anti-kt gradually makes its way 30 through the secondary blob → no clear identification of substructure 20 associated with 2nd parton.
10
0 01234y Identifying jet substructure: try out anti-kt
How well can an algorithm identify anti-kt algorithm the “blobs” of energy inside a jet that pt/GeV come from different partons?
50 This is crucial for identifying the kinematic variables of the partons in the jet (e.g. z). 40
Anti-kt gradually makes its way 30 through the secondary blob → no clear identification of substructure 20 associated with 2nd parton.
10
0 01234y Identifying jet substructure: try out anti-kt
How well can an algorithm identify anti-kt algorithm the “blobs” of energy inside a jet that dmin is dij = 0.000773759 pt/GeV come from different partons?
50 This is crucial for identifying the kinematic variables of the partons in the jet (e.g. z). 40
Anti-kt gradually makes its way 30 through the secondary blob → no clear identification of substructure 20 associated with 2nd parton.
10
0 01234y Identifying jet substructure: try out anti-kt
How well can an algorithm identify anti-kt algorithm the “blobs” of energy inside a jet that pt/GeV come from different partons?
50 This is crucial for identifying the kinematic variables of the partons in the jet (e.g. z). 40
Anti-kt gradually makes its way 30 through the secondary blob → no clear identification of substructure 20 associated with 2nd parton.
10
0 01234y Identifying jet substructure: try out anti-kt
How well can an algorithm identify anti-kt algorithm the “blobs” of energy inside a jet that dmin is dij = 0.0014577 pt/GeV come from different partons?
50 This is crucial for identifying the kinematic variables of the partons in the jet (e.g. z). 40
Anti-kt gradually makes its way 30 through the secondary blob → no clear identification of substructure 20 associated with 2nd parton.
10
0 01234y Identifying jet substructure: try out anti-kt
How well can an algorithm identify anti-kt algorithm the “blobs” of energy inside a jet that pt/GeV come from different partons?
50 This is crucial for identifying the kinematic variables of the partons in the jet (e.g. z). 40
Anti-kt gradually makes its way 30 through the secondary blob → no clear identification of substructure 20 associated with 2nd parton.
10
0 01234y Identifying jet substructure: try out anti-kt
How well can an algorithm identify anti-kt algorithm the “blobs” of energy inside a jet that dmin is diB = 0.00147749 pt/GeV come from different partons?
50 This is crucial for identifying the kinematic variables of the partons in the jet (e.g. z). 40
Anti-kt gradually makes its way 30 through the secondary blob → no clear identification of substructure 20 associated with 2nd parton.
10
0 01234y Identifying jet substructure: try out anti-kt
How well can an algorithm identify anti-kt algorithm the “blobs” of energy inside a jet that pt/GeV come from different partons?
50 This is crucial for identifying the kinematic variables of the partons in the jet (e.g. z). 40
Anti-kt gradually makes its way 30 through the secondary blob → no clear identification of substructure 20 associated with 2nd parton.
10
0 01234y Identifying jet substructure: try out anti-kt
How well can an algorithm identify anti-kt algorithm the “blobs” of energy inside a jet that dmin is diB = 1.96 pt/GeV come from different partons?
50 This is crucial for identifying the kinematic variables of the partons in the jet (e.g. z). 40
Anti-kt gradually makes its way 30 through the secondary blob → no clear identification of substructure 20 associated with 2nd parton.
10
0 01234y Identifying jet substructure: try out anti-kt
How well can an algorithm identify anti-kt algorithm the “blobs” of energy inside a jet that pt/GeV come from different partons?
50 This is crucial for identifying the kinematic variables of the partons in the jet (e.g. z). 40
Anti-kt gradually makes its way 30 through the secondary blob → no clear identification of substructure 20 associated with 2nd parton.
10
0 01234y Identifying jet substructure: try out kt
How well can an algorithm identify kt algorithm the “blobs” of energy inside a jet that pt/GeV come from different partons?
50 This is crucial for identifying the kinematic variables of the partons in the jet (e.g. z). 40
30
20
10
0 01234y Identifying jet substructure: try out kt
How well can an algorithm identify kt algorithm the “blobs” of energy inside a jet that dmin is dij = 0.318802 pt/GeV come from different partons?
50 This is crucial for identifying the kinematic variables of the partons in the jet (e.g. z). 40
30
20
10
0 01234y Identifying jet substructure: try out kt
How well can an algorithm identify kt algorithm the “blobs” of energy inside a jet that pt/GeV come from different partons?
50 This is crucial for identifying the kinematic variables of the partons in the jet (e.g. z). 40
30
20
10
0 01234y Identifying jet substructure: try out kt
How well can an algorithm identify kt algorithm the “blobs” of energy inside a jet that dmin is dij = 0.977453 pt/GeV come from different partons?
50 This is crucial for identifying the kinematic variables of the partons in the jet (e.g. z). 40
30
20
10
0 01234y Identifying jet substructure: try out kt
How well can an algorithm identify kt algorithm the “blobs” of energy inside a jet that pt/GeV come from different partons?
50 This is crucial for identifying the kinematic variables of the partons in the jet (e.g. z). 40
30
20
10
0 01234y Identifying jet substructure: try out kt
How well can an algorithm identify kt algorithm the “blobs” of energy inside a jet that dmin is dij = 1.48276 pt/GeV come from different partons?
50 This is crucial for identifying the kinematic variables of the partons in the jet (e.g. z). 40
kt clusters soft “junk” early on in the 30 clustering
20
10
0 01234y Identifying jet substructure: try out kt
How well can an algorithm identify kt algorithm the “blobs” of energy inside a jet that pt/GeV come from different partons?
50 This is crucial for identifying the kinematic variables of the partons in the jet (e.g. z). 40
kt clusters soft “junk” early on in the 30 clustering
20
10
0 01234y Identifying jet substructure: try out kt
How well can an algorithm identify kt algorithm the “blobs” of energy inside a jet that dmin is dij = 2.34277 pt/GeV come from different partons?
50 This is crucial for identifying the kinematic variables of the partons in the jet (e.g. z). 40
kt clusters soft “junk” early on in the 30 clustering
20
10
0 01234y Identifying jet substructure: try out kt
How well can an algorithm identify kt algorithm the “blobs” of energy inside a jet that pt/GeV come from different partons?
50 This is crucial for identifying the kinematic variables of the partons in the jet (e.g. z). 40
kt clusters soft “junk” early on in the 30 clustering
20
10
0 01234y Identifying jet substructure: try out kt
How well can an algorithm identify kt algorithm the “blobs” of energy inside a jet that dmin is dij = 13.5981 pt/GeV come from different partons?
50 This is crucial for identifying the kinematic variables of the partons in the jet (e.g. z). 40
kt clusters soft “junk” early on in the 30 clustering
20
10
0 01234y Identifying jet substructure: try out kt
How well can an algorithm identify kt algorithm the “blobs” of energy inside a jet that pt/GeV come from different partons?
50 This is crucial for identifying the kinematic variables of the partons in the jet (e.g. z). 40
kt clusters soft “junk” early on in the 30 clustering
20
10
0 01234y Identifying jet substructure: try out kt
How well can an algorithm identify kt algorithm the “blobs” of energy inside a jet that dmin is dij = 30.8068 pt/GeV come from different partons?
50 This is crucial for identifying the kinematic variables of the partons in the jet (e.g. z). 40
kt clusters soft “junk” early on in the 30 clustering
20
10
0 01234y Identifying jet substructure: try out kt
How well can an algorithm identify kt algorithm the “blobs” of energy inside a jet that pt/GeV come from different partons?
50 This is crucial for identifying the kinematic variables of the partons in the jet (e.g. z). 40
kt clusters soft “junk” early on in the 30 clustering
20
10
0 01234y Identifying jet substructure: try out kt
How well can an algorithm identify kt algorithm the “blobs” of energy inside a jet that dmin is dij = 717.825 pt/GeV come from different partons?
50 This is crucial for identifying the kinematic variables of the partons in the jet (e.g. z). 40
kt clusters soft “junk” early on in the 30 clustering Its last step is to merge two hard 20 pieces. Easily undone to identify un- derlying kinematics 10
0 01234y Identifying jet substructure: try out kt
How well can an algorithm identify kt algorithm the “blobs” of energy inside a jet that pt/GeV come from different partons?
50 This is crucial for identifying the kinematic variables of the partons in the jet (e.g. z). 40
kt clusters soft “junk” early on in the 30 clustering Its last step is to merge two hard 20 pieces. Easily undone to identify un- derlying kinematics 10
0 01234y Identifying jet substructure: try out kt
How well can an algorithm identify kt algorithm the “blobs” of energy inside a jet that dmin is diB = 11432 pt/GeV come from different partons?
50 This is crucial for identifying the kinematic variables of the partons in the jet (e.g. z). 40
kt clusters soft “junk” early on in the 30 clustering Its last step is to merge two hard 20 pieces. Easily undone to identify un- derlying kinematics 10
0 01234y Identifying jet substructure: try out kt
How well can an algorithm identify kt algorithm the “blobs” of energy inside a jet that pt/GeV come from different partons?
50 This is crucial for identifying the kinematic variables of the partons in the jet (e.g. z). 40
kt clusters soft “junk” early on in the 30 clustering Its last step is to merge two hard 20 pieces. Easily undone to identify un- derlying kinematics 10 This meant it was the first algorithm to be used for jet substructure. 0 01234y Seymour ’93 Butterworth, Cox & Forshaw ’02 Identifying jet substructure: Cam/Aachen
Cambridge/Aachen algorithm How well can an algorithm identify the “blobs” of energy inside a jet that pt/GeV come from different partons?
50
40
30
20
10
0 01234y
G. Salam (CERN/Princeton/Paris) test-bed., G. Salam June 13, 2013 1 / 1 Identifying jet substructure: Cam/Aachen
Cambridge/Aachen algorithm How well can an algorithm identify the “blobs” of energy inside a jet that DeltaR_{ij} = 0.142857 pt/GeV come from different partons?
50
40
30
20
10
0 01234y
G. Salam (CERN/Princeton/Paris) test-bed., G. Salam June 13, 2013 1 / 1 Identifying jet substructure: Cam/Aachen
Cambridge/Aachen algorithm How well can an algorithm identify the “blobs” of energy inside a jet that pt/GeV come from different partons?
50
40
30
20
10
0 01234y
G. Salam (CERN/Princeton/Paris) test-bed., G. Salam June 13, 2013 1 / 1 Identifying jet substructure: Cam/Aachen
Cambridge/Aachen algorithm How well can an algorithm identify the “blobs” of energy inside a jet that DeltaR_{ij} = 0.214286 pt/GeV come from different partons?
50
40
30
20
10
0 01234y
G. Salam (CERN/Princeton/Paris) test-bed., G. Salam June 13, 2013 1 / 1 Identifying jet substructure: Cam/Aachen
Cambridge/Aachen algorithm How well can an algorithm identify the “blobs” of energy inside a jet that pt/GeV come from different partons?
50
40
30
20
10
0 01234y
G. Salam (CERN/Princeton/Paris) test-bed., G. Salam June 13, 2013 1 / 1 Identifying jet substructure: Cam/Aachen
Cambridge/Aachen algorithm How well can an algorithm identify the “blobs” of energy inside a jet that DeltaR_{ij} = 0.415037 pt/GeV come from different partons?
50
40
30
20
10
0 01234y
G. Salam (CERN/Princeton/Paris) test-bed., G. Salam June 13, 2013 1 / 1 Identifying jet substructure: Cam/Aachen
Cambridge/Aachen algorithm How well can an algorithm identify the “blobs” of energy inside a jet that pt/GeV come from different partons?
50
40
30
20
10
0 01234y
G. Salam (CERN/Princeton/Paris) test-bed., G. Salam June 13, 2013 1 / 1 Identifying jet substructure: Cam/Aachen
Cambridge/Aachen algorithm How well can an algorithm identify the “blobs” of energy inside a jet that DeltaR_{ij} = 0.686928 pt/GeV come from different partons?
50
40
30
20
10
0 01234y
G. Salam (CERN/Princeton/Paris) test-bed., G. Salam June 13, 2013 1 / 1 Identifying jet substructure: Cam/Aachen
Cambridge/Aachen algorithm How well can an algorithm identify the “blobs” of energy inside a jet that pt/GeV come from different partons?
50 C/A identifies two hard blobs with limited soft contamination
40
30
20
10
0 01234y
G. Salam (CERN/Princeton/Paris) test-bed., G. Salam June 13, 2013 1 / 1 Identifying jet substructure: Cam/Aachen
Cambridge/Aachen algorithm How well can an algorithm identify the “blobs” of energy inside a jet that DeltaR_{ij} = 1.20645 pt/GeV come from different partons?
50 C/A identifies two hard blobs with limited soft contamination, joins them 40
30
20
10
0 01234y
G. Salam (CERN/Princeton/Paris) test-bed., G. Salam June 13, 2013 1 / 1 Identifying jet substructure: Cam/Aachen
Cambridge/Aachen algorithm How well can an algorithm identify the “blobs” of energy inside a jet that pt/GeV come from different partons?
50 C/A identifies two hard blobs with limited soft contamination, joins them 40
30
20
10
0 01234y
G. Salam (CERN/Princeton/Paris) test-bed., G. Salam June 13, 2013 1 / 1 Identifying jet substructure: Cam/Aachen
Cambridge/Aachen algorithm How well can an algorithm identify the “blobs” of energy inside a jet that DeltaR_{ij} = 1.93202 pt/GeV come from different partons?
50 C/A identifies two hard blobs with limited soft contamination, joins them, and then adds in remaining 40 soft junk
30
20
10
0 01234y
G. Salam (CERN/Princeton/Paris) test-bed., G. Salam June 13, 2013 1 / 1 Identifying jet substructure: Cam/Aachen
Cambridge/Aachen algorithm How well can an algorithm identify the “blobs” of energy inside a jet that pt/GeV come from different partons?
50 C/A identifies two hard blobs with limited soft contamination, joins them, and then adds in remaining 40 soft junk
30
20
10
0 01234y
G. Salam (CERN/Princeton/Paris) test-bed., G. Salam June 13, 2013 1 / 1 Identifying jet substructure: Cam/Aachen
Cambridge/Aachen algorithm How well can an algorithm identify the “blobs” of energy inside a jet that DeltaR_{ij} > 2 pt/GeV come from different partons?
50 C/A identifies two hard blobs with limited soft contamination, joins them, and then adds in remaining 40 soft junk
30
20
10
0 01234y
G. Salam (CERN/Princeton/Paris) test-bed., G. Salam June 13, 2013 1 / 1 Identifying jet substructure: Cam/Aachen
Cambridge/Aachen algorithm How well can an algorithm identify the “blobs” of energy inside a jet that pt/GeV come from different partons?
50 C/A identifies two hard blobs with limited soft contamination, joins them, and then adds in remaining 40 soft junk
30
20
10
0 01234y
G. Salam (CERN/Princeton/Paris) test-bed., G. Salam June 13, 2013 1 / 1 Identifying jet substructure: Cam/Aachen
Cambridge/Aachen algorithm How well can an algorithm identify the “blobs” of energy inside a jet that DeltaR_{ij} > 2 pt/GeV come from different partons?
50 C/A identifies two hard blobs with limited soft contamination, joins them, and then adds in remaining 40 soft junk
30
20
10
0 01234y
G. Salam (CERN/Princeton/Paris) test-bed., G. Salam June 13, 2013 1 / 1 Identifying jet substructure: Cam/Aachen
Cambridge/Aachen algorithm How well can an algorithm identify the “blobs” of energy inside a jet that pt/GeV come from different partons?
50 C/A identifies two hard blobs with limited soft contamination, joins them, and then adds in remaining 40 soft junk
30 The interesting substructure is buried inside the clustering sequence — it’s 20 less contamined by soft junk, but needs to be pulled out with special 10 techniques Butterworth, Davison, Rubin & GPS ’08 Kaplan, Schwartz, Reherman & Tweedie ’08 0 Butterworth, Ellis, Rubin & GPS ’09 01234y Ellis, Vermilion & Walsh ’09
G. Salam (CERN/Princeton/Paris) test-bed., G. Salam June 13, 2013 1 / 1
anti-kt algorithm kt algorithm Cambridge/Aachen
pt/GeV pt/GeV pt/GeV
50 50 50
40 40 40
30 30 30
20 20 20
10 10 10
0 0 0 01234y 01234y 01234y
Gavin Salam (CERN) QCD basics 4 ICTP-SAIFR school, July 2015 113 Jets lecture 3 (Gavin Salam) CERN Academic Training March/April 2011 19 / 29 ¯ pp → ZH → νν¯bb, @14TeV, mH =115GeV SIGNAL Herwig 6.510 + Jimmy 4.31 + FastJet 2.3
Zbb BACKGROUND
Cluster event, C/A, R=1.2
Butterworth, Davison, Rubin & GPS ’08 arbitrary norm. ¯ pp → ZH → νν¯bb, @14TeV, mH =115GeV SIGNAL Herwig 6.510 + Jimmy 4.31 + FastJet 2.3
Zbb BACKGROUND
Fill it in, → show jets more clearly
Butterworth, Davison, Rubin & GPS ’08 arbitrary norm. ¯ pp → ZH → νν¯bb, @14TeV, mH =115GeV SIGNAL Herwig 6.510 + Jimmy 4.31 + FastJet 2.3 200 < ptZ < 250 GeV 0.15
0.1
0.05
0 80 100 120 140 160
mH [GeV] Zbb BACKGROUND 200 < ptZ < 250 GeV 0.008
0.006
0.004
0.002 Consider hardest jet, m =150GeV 0 80 100 120 140 160
mH [GeV] Butterworth, Davison, Rubin & GPS ’08 arbitrary norm. ¯ pp → ZH → νν¯bb, @14TeV, mH =115GeV SIGNAL Herwig 6.510 + Jimmy 4.31 + FastJet 2.3 200 < ptZ < 250 GeV 0.15
0.1
0.05
0 80 100 120 140 160
mH [GeV] Zbb BACKGROUND 200 < ptZ < 250 GeV 0.008
0.006
0.004
0.002 max(m1,m2) . split: m =150GeV, =092 → repeat 0 m 80 100 120 140 160
mH [GeV] Butterworth, Davison, Rubin & GPS ’08 arbitrary norm. ¯ pp → ZH → νν¯bb, @14TeV, mH =115GeV SIGNAL Herwig 6.510 + Jimmy 4.31 + FastJet 2.3 200 < ptZ < 250 GeV 0.15
0.1
0.05
0 80 100 120 140 160
mH [GeV] Zbb BACKGROUND 200 < ptZ < 250 GeV 0.008
0.006
0.004
0.002 max(m1,m2) . split: m =139GeV, =037 → mass drop 0 m 80 100 120 140 160
mH [GeV] Butterworth, Davison, Rubin & GPS ’08 arbitrary norm. ¯ pp → ZH → νν¯bb, @14TeV, mH =115GeV SIGNAL Herwig 6.510 + Jimmy 4.31 + FastJet 2.3 200 < ptZ < 250 GeV 0.15
0.1
0.05
0 80 100 120 140 160
mH [GeV] Zbb BACKGROUND 200 < ptZ < 250 GeV 0.008
0.006
0.004
0.002 check: y ≃ pt2 ≃ 0.7 → OK + 2 b-tags (anti-QCD) 12 pt1 0 80 100 120 140 160
mH [GeV] Butterworth, Davison, Rubin & GPS ’08 arbitrary norm. ¯ pp → ZH → νν¯bb, @14TeV, mH =115GeV SIGNAL Herwig 6.510 + Jimmy 4.31 + FastJet 2.3 200 < ptZ < 250 GeV 0.15
0.1
0.05
0 80 100 120 140 160
mH [GeV] Zbb BACKGROUND 200 < ptZ < 250 GeV 0.008
0.006
0.004
0.002
Rfilt =0.3 0 80 100 120 140 160
mH [GeV] Butterworth, Davison, Rubin & GPS ’08 arbitrary norm. ¯ pp → ZH → νν¯bb, @14TeV, mH =115GeV SIGNAL Herwig 6.510 + Jimmy 4.31 + FastJet 2.3 200 < ptZ < 250 GeV 0.15
0.1
0.05
0 80 100 120 140 160
mH [GeV] Zbb BACKGROUND 200 < ptZ < 250 GeV 0.008
0.006
0.004
0.002
Rfilt =0.3: take 3 hardest, m = 117 GeV 0 80 100 120 140 160
mH [GeV] Butterworth, Davison, Rubin & GPS ’08 arbitrary norm. Boosted Higgs analysis -- pp →ZH → ννbb
Undo the clustering into subjets, Re-cluster with smaller Cluster with a large R until a large mass drop R, and keep only 3 is observed hardest jets
Gavin Salam (CERN) QCD basics 4 ICTP-SAIFR school, July 2015 122 different (2-body) substructure tools
Detailed relative positions depend on physics context (and are possibly contentious!)
trimming filtering pruning Grooming MDT
jet N- mass subjettiness, CN, DN
Tagging
Gavin Salam (CERN) QCD basics 4 ICTP-SAIFR school, July 2015 123 different (2-body) substructure tools
Detailed relative positions depend on physics context (and are possibly contentious!)
combined trimming filtering pruning Grooming MDT
jet N- mass subjettiness, CN, DN
Tagging
Gavin Salam (CERN) QCD basics 4 ICTP-SAIFR school, July 2015 123 different (2-body) substructure tools
Detailed relative positions depend on physics context (and are possibly contentious!)
multivariate taggers combined trimming filtering pruning Grooming MDT
jet N- mass subjettiness, CN, DN
Tagging
Gavin Salam (CERN) QCD basics 4 ICTP-SAIFR school, July 2015 123 different (2-body) substructure tools
Detailed relative positions depend on physics context (and are possibly contentious!)
multivariate taggers combined trimming jet filtering deconstruction pruning template Q-jets
Grooming tagger MDT
jet N- mass subjettiness, CN, DN
Tagging
Gavin Salam (CERN) QCD basics 4 ICTP-SAIFR school, July 2015 123 SeeingBoosted W’s Ws and and tops tops in single in a jets:single data! jet HEPTopTagger W’s in a single jet tops in a single jet ATLAS-CONF-2013-084
C/A R=1.5 jets with pT > 200 GeV after W→µν preselection and m23/m123 default HEPTopTagger criteria mW/mt
CMS single-jet W mass peak in events with a lepton and separate b-tagged jet. 98% purity Uses pruning (+ mass-drop arctan(m /m ) 13 12condition on split jet) ~4000 tops!
with Pruning + Mass Drop requirement BOOST 2013 Flagstaff, AZ Chris Pollard Duke University 24 NB: combined in IR unsafe way... Gavin Salam (CERN) QCD basics 4 withICTP-SAIFR HEPTopTagger school, July 2015 124 Gavin Salam (CERN) Perturbative QCD in hadron collisions SILAFAE 2012-12-10 32 / 35
Gavin Salam (CERN/Princeton/CNRS) Theory of Fat Jets Higgs Hunting 2012-07-19 19 / 28 ATLAS di-boson excess
About 30 interpretations on arXiv so far!
Gavin Salam (CERN) QCD basics 4 ICTP-SAIFR school, July 2015 125 Points to remember from these lectures
• Major difference relative to QED: quarks and gluons both emit gluons • Non-perturbative physics lurks in many places; limiting its impact (jets), factorising unavoidable non- perturbative parts (PDFs), are both key to our successful use of perturbative QCD • Tightly connected with infrared and collinear divergences, which are ubiquitous in QCD
Gavin Salam (CERN) QCD basics 4 ICTP-SAIFR school, July 2015 126 EXTRAS
127 Time to cluster N particles in FastJet
102 Intel X3360, gcc4.4(-O2) 10 FastJet 3.1, R=0.4 1
0.1 10-2 Time to cluster N -3 time (s) 10 particles -4 10 kt anti-k 10-5 t anti-kt (FJ3.0) 10-6 Cambridge/Aachen SISCone 10-7 10 102 103 104 105 106 1.0 Improvement 0.5 0.3 0.2 wrt FJ 3.0.x, antikt, R=0.4 0.1 factor of 2 for 10k ratio(FJ3.1/FJ3.0) 10 102 103 104 105 106 N
Gavin Salam (CERN) QCD basics 4 ICTP-SAIFR school, July 2015 128 Gavin Salam (CERN) QCD basics 4 ICTP-SAIFR school, July 2015 129