Jet Grooming
description
Transcript of Jet Grooming
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Jet Grooming
Brock Tweedie
Johns Hopkins University
23 June 2010
?
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Outline
• Growing jets
• Grooming jets
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Growing Jets
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Sequential Algorithms
• Cambridge/Aachen
• kT
• Anti-kT
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Cambridge/Aachen
• Sequentially sum up nearest-neighbor 4-vectors in the plane until all 4-vectors are distanced by more than a prespecified R
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Cambridge/Aachen
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Cambridge/Aachen
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Cambridge/Aachen
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Cambridge/Aachen
R
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Cambridge/Aachen
JET #1
JET #2
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Cambridge/Aachen
A
B
C
D
E
A B C
D E
JET #1
JET #2
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kT
• C/A-like, with R-parameter
• Nontrivial distance measure between 4-vectors…sensitive to energy
• “Beam distance” criterion for jet formation
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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
Dij = min(pTi,pTj) * Rij
DiB = pTi * R
Defined for pairs of 4-vectors
Defined for individual 4-vectors
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kT
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kT
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Anti-kT
• Same as kT, but measure now prioritizes clustering with hard 4-vectors
Dij = min(1/pTi,1/pTj) * Rij
DiB = (1/pTi) * R
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Anti-kT
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Anti-kT
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Anti-kT
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Anti-kT
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Anti-kT
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Anti-kT
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Anti-kT
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Catchment Area Comparison
C/A kT
Anti-kT
Cacciari & Salam
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Grooming Jets
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WW in Idealization
l
W fat-jet
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WW in Idealization
l
W subjet #1
W subjet #2
Discriminators against QCD:
1. Jet mass ~ mW
2. kT / z / cos* / R / …
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WW in Reality
l
W fat-jet
Underlying event,
ISR, FSR, pileup
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WW in Reality
Subjet #1
Subjet #2
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W-Jets with YSplitter
W fat-jet mass W fat-jet kT scale
pTW = 300 ~ 500 GeV
Butterworth, Cox, Forshaw
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Simple Fix #1: Shrinking Fat-Jets
Rfat ~ # / pTW
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Seems to Work Okay…
pTW = [400,500]
R = 0.6R = 0.8
R = 0.4
* PYTHIA 6.4 default UE tune
C/A jets
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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
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Simple Fix #2: Shrinking Thin-Jets
Rthin ~ # / pTW
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Degraded Signal Peak vs Bump-on-a-Bump
R = 0.5 C/AR = 0.2 C/A
Intermediately-boosted Higgs from Higgsstrahlung
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More Refined Strategies
• Filtering
• Pruning
• Trimming
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Filtering Advertisement
R = 0.5 C/A BDRS filtering
Butterworth, Davison, Rubin, Salam
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Filtering: A Top-Down View
cell
cell
cell
cellcell
cellcell
cell
JET“hard” split
“soft” split
SUBJET #1
SUBJET #2Higgs’s neighborhood
SUBJET #3Reclustered into “thin-
jets” using R-scale from the hard split
SUBJET #4
Fat-jet clustered with C/A
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Filtering: A Top-Down View
cell
cell
cell
cellcell
cellcell
cell
JET“hard” split
“soft” split
top neighborhood
* Benefits of reclustering depend on process and pT range of interest
Fat-jet clustered with C/A
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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 R between clusters
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Filtering Advantages
• Ditches large-angle soft junk automatically• Adaptively determines appropriate R 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)
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Pruning: A Bottom-Up View
cell
cell
cell
cellcell
cellcell
cell
JET
Ellis, Vermillion, Walsh
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Pruning: A Bottom-Up View
cell
cell
cell
cellcell
cellcell
cell
“hard” merge
“soft” merge
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Pruning: A Bottom-Up View
cell
cell
cell
cellcell
cellcell
cell
“hard” merge
“soft” merge
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Nominal Pruning Parameters
• Merging 4-vectors cannot simultaneously be too asymmetric and too far apart…”hard” merging means:– Min(pT1,pT2)/(pT1+pT2) > zcut
– R12 < Dcut ~ mfat/pTfat
OR
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Case Study: Boosted Top Mass
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Detailed Substructure is “Trivial”
cell
cell
cellcell
cellcell
JET
* However, tied to this specific (local) definition of “hard”
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Trimming
Krohn, Thaler, Wang
1. Recluster fat-jet constituents into very thin jets
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Trimming
Krohn, Thaler, Wang
1. Recluster fat-jet constituents into very thin jets
2. Throw away thin-jets that are too soft
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Case Study: Dijet Resonance
R = 1.5 anti-kT, reclustered with R = 0.2
Throw away if pT ~< (1%)*pTfat
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Boosting Discovery from Combining Algorithms?
Soper & Spannowsky
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Summary: Growing
• We know how to make jets in ways that either organize substructure or form nice circles in a trustable way
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Summary: Grooming
• Interesting jets are full of junk as well as substructure– Mass resolution degrades
• Simple-minded workarounds tend to get us into trouble or are non-optimal
• Variety of more sophisticated procedures are now on the market…all with similar names!
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Summary: Grooming• Filtering
– Trace back through a fat-jet’s clustering history, find “hard” splits (discarding “soft” splits), maybe recluster with refined R using this info
• Pruning– Redo clustering of a fat-jet’s constituents,
vetoing mergings that are too far AND too asymmetric
• Trimming– Recluster fat-jet with tiny R and throw away
thin-jets that are too soft
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Summary: Grooming
• Not much systematic comparison (still)– But see Soper & Spannowsky