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Jet Grooming

Jet Grooming. ?. Brock Tweedie Johns Hopkins University 23 June 2010. Outline. Growing jets Grooming jets. Growing Jets. Sequential Algorithms. Cambridge/Aachen kT Anti-kT. Cambridge/Aachen.

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Jet Grooming

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  1. Jet Grooming ? Brock Tweedie Johns Hopkins University 23 June 2010

  2. Outline • Growing jets • Grooming jets

  3. Growing Jets

  4. Sequential Algorithms • Cambridge/Aachen • kT • Anti-kT

  5. Cambridge/Aachen • Sequentially sum up nearest-neighbor 4-vectors in the h-f plane until all 4-vectors are distanced by more than a prespecified R

  6. Cambridge/Aachen

  7. Cambridge/Aachen

  8. Cambridge/Aachen

  9. Cambridge/Aachen R

  10. Cambridge/Aachen JET #1 JET #2

  11. Cambridge/Aachen JET #1 B C A C A B JET #2 D E D E

  12. kT • C/A-like, with R-parameter • Nontrivial distance measure between 4-vectors…sensitive to energy • “Beam distance” criterion for jet formation

  13. kT • Add D-closest pairs of 4-vectors unless a DiB is smallest • If DiB is smallest, promote i to a jet, pluck it from the list, and continue clustering what remains Defined for pairs of 4-vectors Dij = min(pTi,pTj) * DRij DiB = pTi * R Defined for individual 4-vectors

  14. kT

  15. kT

  16. Anti-kT • Same as kT, but measure now prioritizes clustering with hard 4-vectors Dij = min(1/pTi,1/pTj) * DRij DiB = (1/pTi) * R

  17. Anti-kT

  18. Anti-kT

  19. Anti-kT

  20. Anti-kT

  21. Anti-kT

  22. Anti-kT

  23. Anti-kT

  24. Catchment Area Comparison C/A kT Anti-kT Cacciari & Salam

  25. Grooming Jets

  26. WW in Idealization W fat-jet l n

  27. WW in Idealization W subjet #1 W subjet #2 l n Discriminators against QCD: 1. Jet mass ~ mW 2. kT / z / cosq* / DR / …

  28. WW in Reality W fat-jet l Underlying event, ISR, FSR, pileup n

  29. WW in Reality Subjet #1 Subjet #2

  30. W-Jets with YSplitter pTW = 300 ~ 500 GeV W fat-jet mass W fat-jet kT scale Butterworth, Cox, Forshaw

  31. Simple Fix #1: Shrinking Fat-Jets Rfat ~ # / pTW

  32. Seems to Work Okay… R = 0.4 R = 0.6 R = 0.8 pTW = [400,500] C/A jets * PYTHIA 6.4 default UE tune

  33. Why Don’t We Just Do This? • Introduction of user-defined mass scale…have to know what you’re looking for! • Not obvious that this gets always gets rid of all of the junk • Intermediate-boost regime (Higgsstrahlung) • 3+ body decays spread out more irregularly • Fails to constrain substructure beyond 2-body, but we could continue investigating the distribution of jet constituents afterwards

  34. Simple Fix #2: Shrinking Thin-Jets Rthin ~ # / pTW

  35. Degraded Signal Peak vs Bump-on-a-Bump Intermediately-boosted Higgs from Higgsstrahlung R = 0.2 C/A R = 0.5 C/A

  36. More Refined Strategies • Filtering • Pruning • Trimming

  37. Filtering Advertisement BDRS filtering R = 0.5 C/A Butterworth, Davison, Rubin, Salam

  38. Filtering: A Top-Down View Fat-jet clustered with C/A JET “hard” split “soft” split cell cell cell cell cell cell SUBJET #4 SUBJET #1 cell cell Reclustered into “thin-jets” using DR-scale from the hard split SUBJET #3 SUBJET #2 Higgs’s neighborhood

  39. Filtering: A Top-Down View Fat-jet clustered with C/A JET “hard” split “soft” split cell cell cell cell cell cell cell cell * Benefits of reclustering depend on process and pT range of interest top neighborhood

  40. Hard Measures • Original BDRS: fractional drop in mass, and energy asymmetry between split clusters • Scale-invariant -> no mass features sculpted into backgrounds • JHU Top-tag: energy relative to original fat-jet, and DR between clusters

  41. Filtering Advantages • Ditches large-angle soft junk automatically • Adaptively determines appropriate DR scales for clustering substructures • Easy to define scale-invariant hardness measures • Can be easily extended to flexible searches for arbitrary multiplicities of substructures • Top-jets • Neutralino-jets • Boosted Higgs in busy environment (SUSY, tth)

  42. Pruning: A Bottom-Up View JET cell cell cell cell cell cell cell cell Ellis, Vermillion, Walsh

  43. Pruning: A Bottom-Up View “hard” merge “soft” merge cell cell cell cell cell cell cell cell

  44. Pruning: A Bottom-Up View “hard” merge “soft” merge cell cell cell cell cell cell cell cell

  45. Nominal Pruning Parameters • Merging 4-vectors cannot simultaneously be too asymmetric and too far apart…”hard” merging means: • Min(pT1,pT2)/(pT1+pT2) > zcut • DR12 < Dcut ~ mfat/pTfat OR

  46. Case Study: Boosted Top Mass

  47. Detailed Substructure is “Trivial” JET cell cell cell cell cell cell * However, tied to this specific (local) definition of “hard”

  48. Trimming 1. Recluster fat-jet constituents into very thin jets Krohn, Thaler, Wang

  49. Trimming 1. Recluster fat-jet constituents into very thin jets 2. Throw away thin-jets that are too soft Krohn, Thaler, Wang

  50. Case Study: Dijet Resonance R = 1.5 anti-kT, reclustered with R = 0.2 Throw away if pT ~< (1%)*pTfat

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