Jets. Gavin Salam. LPTHE, CNRS and UPMC (Univ. Paris 6)

Transcription

1 Jets Gavin Salam LPTHE, CNRS and UPMC (Univ. Paris 6) Spring Course of the International Graduate School Bielefeld Paris Helsinki, GRK 881 & PACO Quantum Fields and Strongly Interacting Matter Orsay, France, March 21

2 Jets (p. 2) Comparing algorithms Lecture 3 What jet definition should I use? [jet def. jet alg., R, (f )]

3 Jets (p. 3) Comparing algorithms Jet Folklore Jet discussions: often polarised, driven by unuantified statements Rigorous approach is to uantify similarities & differences Bottom line: grains of truth in the ualitative statements So want good cone algorithms too [NB: recall, two variants xc-sm & xc-pr]

4 Jets (p. 4) Comparing algorithms the reach of jet algorithms p t1 R 1 jet? p t2 2 jets? SISCone (xc-sm) reaches further for hard radiation than other algs

5 Jets (p. 4) Comparing algorithms the reach of jet algorithms p t1 R 1 jet? p t2 2 jets? 1 Prob. 2 k t subjets 1 Cam/Aachen jet 1. z = p t,2 /p t,1.5 Cam/ Aachen parton level R / R cone.5. SISCone (xc-sm) reaches further for hard radiation than other algs

6 Jets (p. 4) Comparing algorithms the reach of jet algorithms 1 p t1 R p t2 Prob. 2 k t subjets 1 Cam/Aachen jet 1 jet? 2 jets? 1. z = p t,2 /p t,1 1.5 Prob. 2 k t subjets 1 anti-k t jet anti-k t xcpr parton level R / R cone z = p t,2 /p t,1.5 Cam/ Aachen parton level R / R cone.5. SISCone (xc-sm) reaches further for hard radiation than other algs

7 Jets (p. 4) Comparing algorithms the reach of jet algorithms z = p t,2 /p t,1 1.5 p t1 R p t2 Prob. 2 k t subjets 1 Cam/Aachen jet Cam/ Aachen parton level 1 jet? 2 jets? R / R cone z = p t,2 /p t,1 z = p t,2 /p t, Prob. 2 k t subjets 1 anti-k t jet anti-k t xcpr parton level R / R cone Prob. 2 k t subjets 1 SISCone jet R kt = 1.; R cone =.4 SISCone SISCone (f=.75) parton level R / R cone SISCone (xc-sm) reaches further for hard radiation than other algs

8 Jets (p. 4) Comparing algorithms the reach of jet algorithms z = p t,2 /p t,1 1.5 p t1 R p t2 Prob. 2 k t subjets 1 Cam/Aachen jet Cam/ Aachen parton level 1 jet? 2 jets? R / R cone z = p t,2 /p t,1 z = p t,2 /p t, Prob. 2 k t subjets 1 anti-k t jet anti-k t xcpr parton level R / R cone Prob. 2 k t subjets 1 SISCone jet R kt = 1.; R cone =.4 SISCone SISCone (f=.75) parton level R / R cone SISCone (xc-sm) reaches further for hard radiation than other algs

9 Jets (p. 5) Comparing algorithms Jet contours visualised

10 Jets (p. 6) Comparing algorithms To first approx: various algs. moderately different; but R can matter a lot more. Next slides: jet energy v. parton energy

11 Jets (p. 7) Comparing algorithms Perturbative p t Jet p t v. parton p t : perturbatively? The uestion s dangerous: a parton is an ambiguous concept Three limits can help you: Threshold limit e.g. de Florian & Vogelsang 7 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 1.1CF uarks.94c A +.7n f gluons + O (α s ) only O (α s ) depends on algorithm & process cf. Dasgupta, Magnea & GPS 7

12 Jets (p. 8) Comparing algorithms 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.4 GeV p t,jet p t,parton shower R { CF uarks gluons C A cf. Dasgupta, Magnea & GPS 7 coefficient holds for anti-k t ; see Dasgupta & Delenda 9 for k t alg.

13 Jets (p. 9) Comparing algorithms Non-perturbative p t Underlying Event (UE) Naive prediction (UE colour dipole between pp): { p t.4 GeV R2 2 CF dipole gluon dipole C A DWT Pythia tune or ATLAS Jimmy tune tell you: p t 1 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 8

14 Jets (p. 9) Comparing algorithms Non-perturbative p t Underlying Event (UE) Naive prediction (UE colour dipole between pp): { p t.4 GeV R2 2 CF dipole gluon dipole C A DWT Pythia tune or ATLAS Jimmy tune tell you: p t 1 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 8

15 Jets (p. 1) Comparing algorithms 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.4 GeVC i R.7? 1? area = πr 2.81 ± ± πr 2 C i πb ln αs(q ) α s(rp t).52 ±.41.8 ± ±.7 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)

16 Jets (p. 11) Physics with jets Can we benefit from this understanding in our use of jets?

17 Jets (p. 12) 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

18 Jets (p. 13) Physics with jets Dijet resonances PT radiation: : p t α sc F π p t lnr What R is best for an isolated jet? E.g. to reconstruct m X (p t + p t ) Hadronisation: : p t C F R.4 GeV p X p Underlying event:,g : p t R GeV 2 Minimise fluctuations in p t Use crude approximation: p 2 t p t 2 in small-r limit (?!) cf. Dasgupta, Magnea & GPS 7

19 Jets (p. 13) Physics with jets Dijet resonances PT radiation: : p t α sc F π p t lnr Hadronisation: : p t C F R Underlying event:.4 GeV,g : p t R 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 ] GeV uark jet LHC uark jets p t = 5 GeV δp t 2 h δp t 2 UE δp t 2 pert R in small-r limit (?!) cf. Dasgupta, Magnea & GPS 7

20 Jets (p. 13) Physics with jets Dijet resonances PT radiation: : p t α sc F π p t lnr Hadronisation: : p t C F R Underlying event:.4 GeV,g : p t R 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 ] LHC uark jets p t = 1 TeV 1 TeV uark jet δp t 2 pert δp t 2 UE R in small-r limit (?!) cf. Dasgupta, Magnea & GPS 7

21 Jets (p. 13) Physics with jets Dijet resonances PT radiation: What R is best for an isolated jet? : p t α sc F π p t lnr 4 At low p t, small R limits relative impact of UE Hadronisation: : p t C 3 At high Fp t, perturbative effects dominate over.4 GeV R non-perturbative R best 1. Underlying event:,g : p t R 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 ] LHC uark jets p t = 1 TeV 1 TeV uark jet δp t 2 pert δp t 2 UE R in small-r limit (?!) cf. Dasgupta, Magnea & GPS 7

22 Jets (p. 14) Physics with jets Dijet resonances 1/N dn/dbin / R =.3, M = 1 GeV SISCone, R=.3, f=.75 Q w f=.24 = 24. GeV Dijet mass: scan over R [Pythia 6.4] arxiv: p Resonance X dijets X p dijet mass [GeV]

23 Jets (p. 14) Physics with jets Dijet resonances 1/N dn/dbin / R =.3, M = 1 GeV SISCone, R=.3, f=.75 Q w f=.24 = 24. GeV Dijet mass: scan over R [Pythia 6.4] arxiv: p Resonance X dijets jet X p dijet mass [GeV] jet

24 Jets (p. 14) Physics with jets Dijet resonances 1/N dn/dbin / R =.4, M = 1 GeV SISCone, R=.4, f=.75 Q w f=.24 = 22.5 GeV Dijet mass: scan over R [Pythia 6.4] arxiv: p Resonance X dijets jet X p dijet mass [GeV] jet

25 Jets (p. 14) Physics with jets Dijet resonances 1/N dn/dbin / R =.5, M = 1 GeV SISCone, R=.5, f=.75 Q w f=.24 = 22.6 GeV Dijet mass: scan over R [Pythia 6.4] arxiv: p Resonance X dijets jet X p dijet mass [GeV] jet

26 Jets (p. 14) Physics with jets Dijet resonances 1/N dn/dbin / R =.6, M = 1 GeV SISCone, R=.6, f=.75 Q w f=.24 = 23.8 GeV Dijet mass: scan over R [Pythia 6.4] arxiv: p Resonance X dijets jet X p dijet mass [GeV] jet

27 Jets (p. 14) Physics with jets Dijet resonances 1/N dn/dbin / R =.7, M = 1 GeV SISCone, R=.7, f=.75 Q w f=.24 = 25.1 GeV Dijet mass: scan over R [Pythia 6.4] arxiv: p Resonance X dijets jet X p dijet mass [GeV] jet

28 Jets (p. 14) Physics with jets Dijet resonances 1/N dn/dbin / R =.8, M = 1 GeV SISCone, R=.8, f=.75 Q w f=.24 = 26.8 GeV Dijet mass: scan over R [Pythia 6.4] arxiv: p Resonance X dijets jet X p dijet mass [GeV] jet

29 Jets (p. 14) Physics with jets Dijet resonances 1/N dn/dbin / R =.9, M = 1 GeV SISCone, R=.9, f=.75 Q w f=.24 = 28.8 GeV Dijet mass: scan over R [Pythia 6.4] arxiv: p Resonance X dijets jet X p dijet mass [GeV] jet

30 Jets (p. 14) Physics with jets Dijet resonances 1/N dn/dbin / R = 1., M = 1 GeV SISCone, R=1., f=.75 Q w f=.24 = 31.9 GeV Dijet mass: scan over R [Pythia 6.4] arxiv: p Resonance X dijets jet X p dijet mass [GeV] jet

31 Jets (p. 14) Physics with jets Dijet resonances 1/N dn/dbin / R = 1.1, M = 1 GeV SISCone, R=1.1, f=.75 Q w f=.24 = 34.7 GeV Dijet mass: scan over R [Pythia 6.4] arxiv: p Resonance X dijets jet X p dijet mass [GeV] jet

32 Jets (p. 14) Physics with jets Dijet resonances 1/N dn/dbin / R = 1.2, M = 1 GeV SISCone, R=1.2, f=.75 Q w f=.24 = 37.9 GeV Dijet mass: scan over R [Pythia 6.4] arxiv: p Resonance X dijets jet X p dijet mass [GeV] jet

33 Jets (p. 14) Physics with jets Dijet resonances 1/N dn/dbin / R = 1.3, M = 1 GeV SISCone, R=1.3, f=.75 Q w f=.24 = 42.3 GeV Dijet mass: scan over R [Pythia 6.4] arxiv: p Resonance X dijets jet X p dijet mass [GeV] jet

34 Jets (p. 14) Physics with jets Dijet resonances 1/N dn/dbin / R = 1.3, M = 1 GeV SISCone, R=1.3, f=.75 Q w f=.24 = 42.3 GeV Dijet mass: scan over R [Pythia 6.4] arxiv: ρ L from Q w f= , M = 1 GeV SISCone, f=.75 arxiv: dijet mass [GeV] R After scanning, summarise uality v. R. Minimum BEST picture not so different from crude analytical estimate

35 Jets (p. 15) Physics with jets Dijet resonances Scan through mass values ρ L from Q w f= m = 1 GeV, M = 1 GeV SISCone, f=.75 arxiv: 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: R NB: 1, plots for various jet algorithms, narrow and gg resonances from Cacciari, Rojo, GPS & Soyez 8

36 Jets (p. 15) Physics with jets Dijet resonances Scan through mass values ρ L from Q w f= m = 15 GeV, M = 15 GeV SISCone, f=.75 arxiv: 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: R NB: 1, plots for various jet algorithms, narrow and gg resonances from Cacciari, Rojo, GPS & Soyez 8

37 Jets (p. 15) Physics with jets Dijet resonances Scan through mass values ρ L from Q w f= m = 2 GeV, M = 2 GeV SISCone, f=.75 arxiv: 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: R NB: 1, plots for various jet algorithms, narrow and gg resonances from Cacciari, Rojo, GPS & Soyez 8

38 Jets (p. 15) Physics with jets Dijet resonances Scan through mass values ρ L from Q w f= m = 3 GeV, M = 3 GeV SISCone, f=.75 arxiv: 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: R NB: 1, plots for various jet algorithms, narrow and gg resonances from Cacciari, Rojo, GPS & Soyez 8

39 Jets (p. 15) Physics with jets Dijet resonances Scan through mass values ρ L from Q w f= m = 5 GeV, M = 5 GeV SISCone, f=.75 arxiv: 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: R NB: 1, plots for various jet algorithms, narrow and gg resonances from Cacciari, Rojo, GPS & Soyez 8

40 Jets (p. 15) Physics with jets Dijet resonances Scan through mass values ρ L from Q w f= m = 7 GeV, M = 7 GeV SISCone, f=.75 arxiv: 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: R NB: 1, plots for various jet algorithms, narrow and gg resonances from Cacciari, Rojo, GPS & Soyez 8

41 Jets (p. 15) Physics with jets Dijet resonances Scan through mass values ρ L from Q w f= m = 1 GeV, M = 1 GeV SISCone, f=.75 arxiv: 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: R NB: 1, plots for various jet algorithms, narrow and gg resonances from Cacciari, Rojo, GPS & Soyez 8

42 Jets (p. 15) Physics with jets Dijet resonances Scan through mass values ρ L from Q w f= m = 2 GeV, M = 2 GeV SISCone, f=.75 arxiv: 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: R NB: 1, plots for various jet algorithms, narrow and gg resonances from Cacciari, Rojo, GPS & Soyez 8

43 Jets (p. 15) Physics with jets Dijet resonances Scan through mass values ρ L from Q w f= m = 4 GeV, M = 4 GeV SISCone, f=.75 arxiv: 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: R NB: 1, plots for various jet algorithms, narrow and gg resonances from Cacciari, Rojo, GPS & Soyez 8

44 Jets (p. 15) Physics with jets Dijet resonances Scan through mass values ρ L from Q w f= m = 4 GeV, M = 4 GeV SISCone, f=.75 arxiv: 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: R NB: 1, plots for various jet algorithms, narrow and gg resonances from Cacciari, Rojo, GPS & Soyez 8

45 Jets (p. 15) Physics with jets Dijet resonances Scan through mass values ρ L from Q w f= m = 4 GeV, M = 4 GeV SISCone, f=.75 arxiv: 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: R NB: 1, plots for various jet algorithms, narrow and gg resonances from Cacciari, Rojo, GPS & Soyez 8

46 Jets (p. 16) Physics with jets Dijet resonances

47 Jets (p. 17) Physics with jets Dijet resonances Robustness: M top varies with R? 1/n dn/dm k t R=.4 tt -> b+bµν µ M top no UE with UE Pythia 6.325, m t = 175 GeV/c reconstructed m t [GeV/c 2 ] Game: measure top mass to 1 GeV example for Tevatron m t = 175 GeV Small R: lose 6 GeV to PT radiation and hadronisation, UE and pileup irrelevant Large R: hadronisation and PT radiation leave mass at 175 GeV, UE adds 2 4 GeV. Is the final top mass (after W jet-energy-scale and Monte Carlo unfolding) independent of R used to measure jets? Powerful cross-check of systematic effects cf. Seymour & Tevlin 6

48 Jets (p. 17) Physics with jets Dijet resonances Robustness: M top varies with R? 1/n dn/dm k t R=.5 tt -> b+bµν µ M top no UE with UE Pythia 6.325, m t = 175 GeV/c reconstructed m t [GeV/c 2 ] Game: measure top mass to 1 GeV example for Tevatron m t = 175 GeV Small R: lose 6 GeV to PT radiation and hadronisation, UE and pileup irrelevant Large R: hadronisation and PT radiation leave mass at 175 GeV, UE adds 2 4 GeV. Is the final top mass (after W jet-energy-scale and Monte Carlo unfolding) independent of R used to measure jets? Powerful cross-check of systematic effects cf. Seymour & Tevlin 6

49 Jets (p. 17) Physics with jets Dijet resonances Robustness: M top varies with R? 1/n dn/dm k t R=.6 tt -> b+bµν µ no UE with UE.5 Pythia 6.325, m t = 175 GeV/c reconstructed m t [GeV/c 2 ] Game: measure top mass to 1 GeV example for Tevatron m t = 175 GeV Small R: lose 6 GeV to PT radiation and hadronisation, UE and pileup irrelevant Large R: hadronisation and PT radiation leave mass at 175 GeV, UE adds 2 4 GeV. Is the final top mass (after W jet-energy-scale and Monte Carlo unfolding) independent of R used to measure jets? Powerful cross-check of systematic effects cf. Seymour & Tevlin 6

50 Jets (p. 17) Physics with jets Dijet resonances Robustness: M top varies with R? 1/n dn/dm k t R=.7 tt -> b+bµν µ no UE with UE.5 Pythia 6.325, m t = 175 GeV/c reconstructed m t [GeV/c 2 ] Game: measure top mass to 1 GeV example for Tevatron m t = 175 GeV Small R: lose 6 GeV to PT radiation and hadronisation, UE and pileup irrelevant Large R: hadronisation and PT radiation leave mass at 175 GeV, UE adds 2 4 GeV. Is the final top mass (after W jet-energy-scale and Monte Carlo unfolding) independent of R used to measure jets? Powerful cross-check of systematic effects cf. Seymour & Tevlin 6

51 Jets (p. 17) Physics with jets Dijet resonances Robustness: M top varies with R? 1/n dn/dm k t R=.8 tt -> b+bµν µ no UE with UE.5 Pythia 6.325, m t = 175 GeV/c reconstructed m t [GeV/c 2 ] Game: measure top mass to 1 GeV example for Tevatron m t = 175 GeV Small R: lose 6 GeV to PT radiation and hadronisation, UE and pileup irrelevant Large R: hadronisation and PT radiation leave mass at 175 GeV, UE adds 2 4 GeV. Is the final top mass (after W jet-energy-scale and Monte Carlo unfolding) independent of R used to measure jets? Powerful cross-check of systematic effects cf. Seymour & Tevlin 6

52 Jets (p. 17) Physics with jets Dijet resonances Robustness: M top varies with R? 1/n dn/dm k t R=1. tt -> b+bµν µ no UE with UE.5 Pythia 6.325, m t = 175 GeV/c reconstructed m t [GeV/c 2 ] Game: measure top mass to 1 GeV example for Tevatron m t = 175 GeV Small R: lose 6 GeV to PT radiation and hadronisation, UE and pileup irrelevant Large R: hadronisation and PT radiation leave mass at 175 GeV, UE adds 2 4 GeV. Is the final top mass (after W jet-energy-scale and Monte Carlo unfolding) independent of R used to measure jets? Powerful cross-check of systematic effects cf. Seymour & Tevlin 6

53

54 Jets (p. 19) jet a parton 1 jet partons Jets without hard partons: Most jet algorithms give you 5 1 jets, mostly not hard. provide window on UE and min-bias

55 Jets (p. 2) jet a parton 1 jet partons Making use of all jets

56 Jets (p. 21) jet a parton 1 jet partons Estimating ρ background noise level 8 median (pt/area) Most jets in event are background 6 Their p t is correlated with their area. P t,jet 4 2 dijet event + 1 minbias (Kt-alg, R=1) Estimate ρ: ρ median {jets} [ pt,jet A jet ] jet area Median limits bias from hard jets Cacciari & GPS 7

57 Jets (p. 22) jet a parton 1 jet partons Common challenge: large contamination A pp event (LHC 5.5 TeV, Pythia)

58 Jets (p. 22) jet a parton 1 jet partons Common challenge: large contamination Contamination in jet RHIC AuAu: O (4 GeV) LHC PbPb: O (1 GeV) LHC pp (hi-lumi) O (5 4 GeV) A pp event (LHC 5.5 TeV, Pythia), embedded in a HI collision background (Hydjet 1.5)

59 Jets (p. 22) jet a parton 1 jet partons Common challenge: large contamination Contamination in jet RHIC AuAu: O (4 GeV) LHC PbPb: O (1 GeV) LHC pp (hi-lumi) O (5 4 GeV) A pp event (LHC 5.5 TeV, Pythia), embedded in a HI collision background (Hydjet 1.5) and an actual STAR event

60 Jets (p. 23) jet a parton 1 jet partons Subtracting noise from jets p subtracted t,jet = p t,jet ρ A jet A jet = jet area ρ = p t per unit area from underlying event (or background ) This procedure is intended to be common to pp, pp with pileup (multiple simultaneous minbias) and HIC NB in AuAu at RHIC: p subtracted t,jet = 2 5 GeV, ρ 8 GeV and A jet.5

61 Jets (p. 24) jet a parton 1 jet partons This method is basis of STAR jet results Method designed to minimise biases, but some still persist. STAR corrects remaining biases based (partly) on Monte Carlo modelling. Question: can we calculate size of biases? Can we further reduce them? Identify complementary methods?

62 Jets (p. 24) jet a parton 1 jet partons This method is basis of STAR jet results Method designed to minimise biases, but some still persist. STAR corrects remaining biases based (partly) on Monte Carlo modelling. Question: can we calculate size of biases? Can we further reduce them? Identify complementary methods?

63 Jets (p. 25) jet a parton 1 jet 2 partons Pushing jets to their limit: when a W,Z, H or a top a single jet Not unusual at LHC: m W, m t 14 TeV

64 Jets (p. 26) jet a parton 1 jet 2 partons E.g.: WH/ZH search LHC Signal is W lν, H b b. Backgrounds include Wb b, t t lνb bjj,... Studied e.g. in ATLAS TDR Difficulties, e.g. gg t t has lνb b with same intrinsic mass scale, but much higher partonic luminosity Need exuisite control of bkgd shape Try a long shot? Go to high p t (p th,p tv > 2 GeV) Lose 95% of signal, but more efficient? Maybe kill t t & gain clarity? W e,µ H b b ν

65 Jets (p. 26) jet a parton 1 jet 2 partons E.g.: WH/ZH search LHC Signal is W lν, H b b. Backgrounds include Wb b, t t lνb bjj,... Studied e.g. in ATLAS TDR pp WH lνb b + bkgds ATLAS TDR Difficulties, e.g. gg t t has lνb b with same intrinsic mass scale, but much higher partonic luminosity Need exuisite control of bkgd shape H b Try a long shot? Go to high p t (p th,p tv > 2 GeV) Lose 95% of signal, but more efficient? Maybe kill t t & gain clarity? W e,µ b ν

66 Jets (p. 26) jet a parton 1 jet 2 partons E.g.: WH/ZH search LHC Signal is W lν, H b b. Backgrounds include Wb b, t t lνb bjj,... Studied e.g. in ATLAS TDR Difficulties, e.g. gg t t has lνb b with same intrinsic mass scale, but much higher partonic luminosity Need exuisite 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 > 2 GeV) Lose 95% of signal, but more efficient? Maybe kill t t & gain clarity? e,µ W ν

67 Jets (p. 26) jet a parton 1 jet 2 partons E.g.: WH/ZH search LHC Signal is W lν, H b b. Backgrounds include Wb b, t t lνb bjj,... Studied e.g. in ATLAS TDR 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 exuisite 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 > 2 GeV) Lose 95% of signal, but more efficient? Maybe kill t t & gain clarity? e,µ W ν

68 Jets (p. 27) jet a parton 1 jet 2 partons EW bosons high p t Illustrate LHC challenges with a recently widely discussed class of problems: Can you identify hadronically decaying EW bosons when they re produced at high p t? boosted X z single jet R m p t 1 z(1 z) (1 z) Significant discussion over years: heavy new things decay to EW states Seymour 94 [Higgs WW νljets] Butterworth, Cox & Forshaw 2 [WW WW νljets ] Agashe et al. 6 [KK excitation of gluon t t] Butterworth, Ellis & Raklev 7 [SUSY decay chains W, H] Skiba & Tucker-Smith 7 [vector uarks] ETC. Lillie, Randall & Wang 7 [KK excitation of gluon t t]

69 Jets (p. 27) jet a parton 1 jet 2 partons EW bosons high p t Illustrate LHC challenges with a recently widely discussed class of problems: Can you identify hadronically decaying EW bosons when they re produced at high p t? boosted X z single jet R m p t 1 z(1 z) (1 z) Significant discussion over years: heavy new things decay to EW states Seymour 94 [Higgs WW νljets] Butterworth, Cox & Forshaw 2 [WW WW νljets ] Agashe et al. 6 [KK excitation of gluon t t] Butterworth, Ellis & Raklev 7 [SUSY decay chains W, H] Skiba & Tucker-Smith 7 [vector uarks] ETC. Lillie, Randall & Wang 7 [KK excitation of gluon t t]

70 Jets (p. 27) jet a parton 1 jet 2 partons EW bosons high p t Illustrate LHC challenges with a recently widely discussed class of problems: Can you identify hadronically decaying EW bosons when they re produced at high p t? boosted X z single jet R m p t 1 z(1 z) (1 z) Significant discussion over years: heavy new things decay to EW states Seymour 94 [Higgs WW νljets] Butterworth, Cox & Forshaw 2 [WW WW νljets ] Agashe et al. 6 [KK excitation of gluon t t] Butterworth, Ellis & Raklev 7 [SUSY decay chains W, H] Skiba & Tucker-Smith 7 [vector uarks] ETC. Lillie, Randall & Wang 7 [KK excitation of gluon t t]

71 Jets (p. 28) jet a parton 1 jet 2 partons pp ZH ν νb m H =115GeV Herwig Jimmy FastJet 2.3 SIGNAL Zbb BACKGROUND Cluster event, C/A, R=1.2 Butterworth, Davison, Rubin & GPS 8 arbitrary norm.

72 Jets (p. 28) jet a parton 1 jet 2 partons pp ZH ν νb m H =115GeV Herwig Jimmy FastJet 2.3 SIGNAL Zbb BACKGROUND Fill it in, show jets more clearly Butterworth, Davison, Rubin & GPS 8 arbitrary norm.

73 Jets (p. 28) jet a parton 1 jet 2 partons pp ZH ν νb m H =115GeV Herwig Jimmy FastJet SIGNAL 2 < p tz < 25 GeV m H [GeV] Zbb BACKGROUND.8 2 < p tz < 25 GeV.6.4 Consider hardest jet, m = 15 GeV Butterworth, Davison, Rubin & GPS m H [GeV] arbitrary norm.

74 Jets (p. 28) jet a parton 1 jet 2 partons pp ZH ν νb m H =115GeV Herwig Jimmy FastJet SIGNAL 2 < p tz < 25 GeV m H [GeV] Zbb BACKGROUND.8 2 < p tz < 25 GeV split: m = 15 GeV, max(m 1,m2) m =.92 repeat Butterworth, Davison, Rubin & GPS m H [GeV] arbitrary norm.

75 Jets (p. 28) jet a parton 1 jet 2 partons pp ZH ν νb m H =115GeV Herwig Jimmy FastJet SIGNAL 2 < p tz < 25 GeV m H [GeV] Zbb BACKGROUND.8 2 < p tz < 25 GeV split: m = 139 GeV, max(m 1,m2) m =.37 mass drop Butterworth, Davison, Rubin & GPS m H [GeV] arbitrary norm.

76 Jets (p. 28) jet a parton 1 jet 2 partons pp ZH ν νb m H =115GeV Herwig Jimmy FastJet SIGNAL 2 < p tz < 25 GeV m H [GeV] Zbb BACKGROUND.8 2 < p tz < 25 GeV.6.4 check: y 12 p t2 p t1.7 OK + 2 b-tags (anti-qcd) Butterworth, Davison, Rubin & GPS m H [GeV] arbitrary norm.

77 Jets (p. 28) jet a parton 1 jet 2 partons pp ZH ν νb m H =115GeV Herwig Jimmy FastJet SIGNAL 2 < p tz < 25 GeV m H [GeV] Zbb BACKGROUND.8 2 < p tz < 25 GeV.6.4 R filt =.3 Butterworth, Davison, Rubin & GPS m H [GeV] arbitrary norm.

78 Jets (p. 28) jet a parton 1 jet 2 partons pp ZH ν νb m H =115GeV Herwig Jimmy FastJet SIGNAL 2 < p tz < 25 GeV m H [GeV] Zbb BACKGROUND.8 2 < p tz < 25 GeV.6.4 R filt =.3: take 3 hardest, m = 117 GeV Butterworth, Davison, Rubin & GPS m H [GeV] arbitrary norm.

79 Jets (p. 29) jet a parton 1 jet 2 partons combine HZ and HW, p t > 2 GeV Take Z l + l, Z ν ν, W lν l = e, µ p tv,p th > 2 GeV η V, η H < 2.5 Assume real/fake b-tag rates of.6/.2. Some extra cuts in HW channels to reject t t. Assume m H = 115 GeV. At 5σ for 3 fb 1 this looks like a competitive channel for light Higgs discovery. A powerful method! Currently under study in the LHC experiments

80 Jets (p. 3) jet a parton Boosted top Tagging boosted top-uarks High-p t top production often envisaged in New Physics processes. high-p t EW boson, but: top has 3-body decay and is coloured. 7 papers on top tagging in 8-9 (at least): jet mass + something extra. Questions What efficiency for tagging top? What rate of fake tags for normal jets? Rough results for top uark with p t 1 TeV Extra eff. fake [from T&W] just jet mass 5% 1% Brooijmans 8 3,4 k t subjets, d cut 45% 5% Thaler & Wang 8 2,3 k t subjets, z cut + various 4% 5% Kaplan et al. 8 3,4 C/A subjets, z cut + θ h 4% 1% Almeida et al. 8 predict mass dist n, use jet-shape Ellis et al. 9 C/A pruning 1%.5% ATLAS 9 3,4 k t subjets, d cut MC likelihood 9% 15% Plehn et al. 9 C/A mass drops, θ h [busy evs, p t 25] 4% 2.5%

81 Jets (p. 31) jet a parton Boosted top Efficiency v. p t

82 Jets (p. 31) jet a parton Boosted top Efficiency v. p t without detector segmentation

83 Jets (p. 32) Conclusions Conclusions

84 Jets (p. 33) Conclusions The potential of better jet finding X gg VH,H b b tth,h b b RPV χ 1 1/N dn/dbin / gg, M = 2 GeV k t, R=.4 Q w f=.13 = 19 GeV arxiv: m 1 /(1GeV) dn/dbin per fb signal + background background (just dijets) signal Cam/Aachen R=.7 p t1 > 5 GeV dijet mass [GeV] Herwig Jimmy m 1 [GeV] 1/N dn/dbin / gg, M = 2 GeV C/A-filt, R=1.3 Q w f=.13 = 53 GeV (subtr.) arxiv: m 1 /(1GeV) dn/dbin per fb signal + background background (just dijets) signal Cam/Aachen + filt, R= dijet mass [GeV] p t1 > 5 GeV, z split >.15, m bc >.25 m abc p t3, p t4 > {1,8} GeV, y 13, y 14 < m 1 [GeV] 1) Cacciari, Rojo, GPS & Soyez 8; 2) Butterworth, Davison, Rubin & GPS 8; 3) Plehn, GPS & Spannowsky 9; 4) Butterworth, Ellis, Raklev & GPS 9.

85 Jets (p. 34) Extras Extras

86 Jets (p. 35) Extras ATLAS results Leptonic channel What changes compared to particle-level analysis? 1.5σ as compared to 2.1σ Expected given larger mass window

87 Jets (p. 35) Extras 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

88 Jets (p. 35) Extras 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

89 Jets (p. 36) Extras ATLAS combined results Likelihood-based analysis of all three channels together gives signal significance of 3.7σ for 3 fb 1 To be compared with 4.2σ in hadron-level analysis for m H = 12 GeV K-factors not included: don t affect significance ( 1.5 for VH, for Vbb) With 5% (2%) background uncertainty, ATLAS result becomes 3.5σ (2.8σ) Comparison to other channels at ATLAS (m H = 12, 3 fb 1 ): gg H γγ WW H ττ gg H ZZ 4.2σ 4.9σ 2.6σ Extracted from