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Advanced Jet Grooming Techniques and Algorithms for Particle Physics Analysis

This document outlines the latest advancements in jet grooming techniques and algorithms for enhancing particle physics analyses. Presented by Brock Tweedie at Johns Hopkins University, it covers a range of methods including growing and grooming jets, the application of sequential algorithms, and refined clustering techniques such as kT and anti-kT. The emphasis is on improving jet formation, filtering, and pruning methods to enhance signal analysis in complex environments. Practical examples and case studies demonstrate the efficacy of these techniques in identifying substructures in high-energy particle collisions.

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Advanced Jet Grooming Techniques and Algorithms for Particle Physics Analysis

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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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