Lecture IV: Jet finding techniques and results

1. Lecture IV: Jet finding techniques and results Marco van Leeuwen Utrecht University Jyväskylä Summer School 2008

2. Parton energy from g-jet and jet reconstruction  Second-generation measurements at RHIC – first generation at LHC? Qualitatively: known pQCDxPDF extract `known’ from e+e- Full deconvolution large uncertainties (+ not transparent) Fix/measure Ejet to take one factor out • Two approaches: • g-jet • Jet reconstruction

3. Perturbative QCD processes • Hadron production • Heavy flavours • Jet production • e+e-→ jets • p(bar)+p → jets • Direct photon production Theory difficulty Measurement difficulty

4. Fixing the parton energy with g-jet events  Input energy loss distribution T. Renk, PRC74, 034906 Away-side spectra in g-jet Eg = 15 GeV Nuclear modification factor Away-side spectra for g-jet are sensitive to P(DE) g-jet: know jet energy  sensitive to P(DE) RAA insensitive to P(DE)

5. g-jet in Au+Au Use shower shape in EMCal to form p0 sample and g-rich sample Combinatorial subtraction to obtain direct-g sample

6. Direct-g recoil suppression  8 < ET,g < 16 GeV 2 < pTassoc < 10 GeV J. Frantz, Hard Probes 2008 A. Hamed, Hard Probes 2008 STAR Preliminary ET,g DAA(zT) IAA(zT) = Dpp(zT) Large suppression for away-side: factor 3-5 Results agree with model predictions Uncertainties still sizable Some improvements expected for final resultsFuture improvements with increased RHIC luminosity

7. Jet reconstruction algorithms Two categories of jet algorithms: • Sequential recombination kT, anti-kT, Durham • Define distance measure, e.g. dij = min(pTi,pTj)*Rij • Cluster closest • Cone • Draw Cone radius R around starting point • Iterate until stable h,jjet = <h,j>particles Sum particles inside jet Different prescriptions exist, most natural: E-scheme, sum 4-vectors Jet is an object defined by jet algorithm If parameters are right, may approximate parton For a complete discussion, see: http://www.lpthe.jussieu.fr/~salam/teaching/PhD-courses.html

8. Collinear and infrared safety Illustration by G. Salam • Jets should not be sensitive to soft effects (hadronisation and E-loss) • Collinear safe • Infrared safe

9. Collinear safety Illustration by G. Salam Note also: detector effects, such as splitting clusters in calorimeter (p0 decay)

10. Infrared safety Illustration by G. Salam Infrared safety also implies robustness against soft background in heavy ion collisions

11. Clustering algorithms – kT algorithm

12. kT algorithm Various distance measures have been used, e.g. Jade, Durham, Cambridge/Aachen • Calculate • For every particle i: distance to beam • For every pair i,j : distance • Find minimal d • If diB, i is a jet • If dij, combine i and j • Repeat until only jets Current standard choice:

13. kT algorithm demo

14. kT algorithm properties • Everything ends up in jets • kT-jets irregular shape • Measure area with ‘ghost particles’ • kT-algo starts with soft stuff • ‘background’ clusters first, affects jet • Infrared and collinear safe • Naïve implementation slow (N3). Not necessary  Fastjet Alternative: anti-kT

15. Cone algorithm • Jets defined as cone • Iterate until stable:(h,j)Cone = <h,j>particles in cone • Starting points for cones, seeds, e.g. highest pT particles • Split-merge prescription for overlapping cones

16. Cone algorithm demo

17. IR safety is subtle, but important G. Salam, arXiv:0906.1833

18. Seedless cone 1D: slide cone over particles and search for stable cone Key observation: content of cone only changes when the cone boundary touches a particle Extension to 2D (h,j) Limiting cases occur when two particles are on the edge of the cone

19. Split-merge procedure • Overlapping cones unavoidable • Solution: split-merge procedureEvaluate Pt1, Pt,shared • If Pt,shared/Pt1> fmerge jets • Else split jets (e.g. assign Pt,shared to closest jet or split Pt,shared according to Pt1/Pt2) f = 0.5 … 0.75 Jet1 Jet1 Jet2 Jet2 Merge: Ptshared large fraction of Pt1 Split: Ptshared small fraction of Pt1

20. Note on recombination schemes Simple Not boost-invariant for massive particles ET-weighted averaging: Most unambiguous scheme: E-scheme, add 4-vectors Boost-invariant Needs particle masses (e.g. assign pion mass) Generates massive jets

21. Current best jet algorithms • Only three good choices: • kT algorithm (sequential recombination, non-circular jets) • Anti-kT algoritm (sequential recombination, circular jets) • SISCone algorithm (Infrared Safe Cone) + some minor variations: Durham algo, differentcombination schemes These are all available in the FastJet package: http://www.lpthe.jussieu.fr/~salam/fastjet/ Really no excuse to use anything else(and potentially run into trouble)

22. Speed matters G. Salam, arXiv:0906.1833 At LHC, multiplicities are largeA lot has been gained from improving implementations

23. Relating jets and single hadrons High-pT hadrons from jet fragmentation Qualitatively: • Single hadrons are suppressed: • Suppression of jet yield (out-of-cone radiation) RAAjets < 1 • Modification of fragment distribution (in-cone radiation) softening of fragmentation function and/or broadening of jet structure

24. Jet finding in heavy ion events STAR preliminary pt per grid cell [GeV] η j ~ 21 GeV Jets clearly visible in heavy ion events at RHIC Combinatorial background Needs to be subtracted • Use different algorithms to estimate systematic uncertainties: • Cone-type algorithms simple cone, iterative cone, infrared safe SISCone • Sequential recombination algorithmskT, Cambridge, inverse kT http://rhig.physics.yale.edu/~putschke/Ahijf/A_Heavy_Ion_Jet-Finder.html FastJet:Cacciari, Salam and Soyez; arXiv: 0802.1188

25. Jet spectra p+p Au+Au central STAR Preliminary STAR Preliminary Note kinematic reach out to 50 GeV • Jet energy depends on R, affects spectra • kT, anti-kT give similar results Take ratios to compare p+p, Au+Au

26. Jet RAA at RHIC M. Ploskon, STAR, QM09 Jet RAA >> 0.2 (hadron RAA) Jet finding recovers most of the energy loss  measure of initial parton energy Some dependence on jet-algorithm? Under study…

27. Radius dependence M. Ploskon, STAR, QM09 RAA depends on jet radius: Small R jet is single hadron Jet broadening due to E-loss?

28. Fragmentation functions Use recoil jet to avoid biases 20<pt,rec(AuAu)<25 GeV E. Bruna, STAR, QM09 pt,rec(AuAu)>25 GeV STAR Preliminary Suppression of fragmentation also small (>> 0.2)

29. Di-jet spectra Jet IAA Away-side jet yield suppressed  partons absorbed E. Bruna, STAR, QM09 STAR Preliminary ... due to large path lentgh (trigger bias) Elena Bruna for the STAR Collaboration - QM09 STAR Preliminary 29

30. Emerging picture from jet results • Jet RAA ~ 1 for sufficiently large R – unbiased parton selection • Away side jet fragmentation ummodified – away-side jet emerges without E-loss • Jet IAA ~ 0.2 – Many jets are absorded (large E-loss) Study vs R, E to quantify P(DE) and broadening

31. Modeling in-medium fragmentation From C. Salgado

32. Sudakov prescription From C. Salgado

33. Sudakov prescription From C. Salgado

34. Sudakov MC implementation Used by most MC event generators (PYTHIA, HERWIG) From C. Salgado

35. Full MC event From C. Salgado

36. MC results Q-PYTHIA N. Armesto et al, arXiv:0907.1014 Softening of fragmentation(pT-spectra) Broadening Caveat: all plot are parton-level. Effect of hadronisation may be large

37. Summary • Jet-finding, g-jet to fix parton energy Sensitivity to P(DE), jet broadening • g-hadron results agree with predictionNeed more statistics for P(DE) • Jet-algorithm requirements: Infrared and Collinear safe • Jet results from RHIC: • Can recover full parton energy (R=0.4) • Indicate large broadening • Away-side jet IAA ~ 0.2, jet absorption? • Full event MC genartors are being developed important reference/benchmark for jet-analyses

38. Extra slides

39. Direct-g recoil yields  Run 4 p+p/Au+Au @ 200 GeV M. Nguyen, Quark Matter 2006 A. Hamed, Hard Probes 2008 Direct-g–jet measurements being pursued by STAR and PHENIX Requires large data samples Suppression of away-side yield visible Similar to di-hadrons, but now with selected parton energy