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Tagging Hadronic Tops at High Pt
Brock Tweedie
Johns Hopkins University
16 Sept 08
D.E. Kaplan, K. Rehermann, M. Schwartz, B.T. arXiv:0806.0848 (to appear in PRL)
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Why Tops?
t
t
t
t
~
~
Z’ / g’
t
t
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Tagging Tops
t W bl b
q q’ b_
_
• Find 3 hard objects• ID b-jet using
displaced vertices• Reconstruct top mass
and W mass
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…At High Pt
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Boosted Top Kinematics at 1 TeV
R = sqrt(2 + 2)
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Standard Resonance Search
• l+jets• Cone jets of fixed size
– Capture variable # of top decay products per jet– Lose multiplicity and kinematic info– Leptons become non-isolated– Subject to backgrounds you maybe shouldn’t be worrying about
• Degraded b-tagging– At high Pt, tracks are crowded– Fake displaced vertices are a big issue, still under investigation– 1 TeV top: 20% b-tag / ~1% udsg mistag
• Progressively worse at higher Pt
• Total signal efficiency ~ 1%
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Mission Statement
• We would like some way to look inside these jets and use as much info as possible
• We would also like to free ourselves of reliance on b-tagging
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1 TeV Top-Jet Gallery
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1 TeV Light-Jet Gallery
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Cambridge/Aachen Algorithm
0. Calorimeter cells = massless 4-vectors
1. Calculate distance Rij between all pairs of 4-vectors
2. Stop if all Rij > R, otherwise add together the closest pair and go back to Step 1
(Same as kt, different measure)
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Cambridge/Aachen Algorithm
1
2
3
1 2 3
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Our Inspiration
• Jon Butterworth, et al boosted Higgs ID
• Cluster/decluster strategy– First cluster event with a large R using C/A– Identify a jet of interest, then reverse the
clustering steps in search of substructure
• Extract “subjets”– Hard clusters of energy inside a jet– Size/distance is not fixed
J. Butterworth, A. Davison, M. Rubin, G. Salam, PRL 100:242001,2008 _ [arXiv:0802.2470 [hep-ph]]
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fastjet
• Authors: M. Cacciari, G. Salam, G. Soyez• Standalone C++ code• 3 sequential recombination jet algorithms,
various options– kt, C/A, anti-kt
• Stores the entire clustering tree• Very user-friendly
http://www.lpthe.jussieu.fr/~salam/fastjet/
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Our Algorithm, Part I
0. Cluster event with C/A and look at individual jets
1. Decluster jet one step. Throw away softer object if its Pt < p and continue declustering.
2. Stop declustering if:• Both objects Pt > p. These are subjets.
• Both objects Pt < p
• Objects are “too close”: |N| + |N| < r
• Only one object is leftdeclustering fails, rebuild original jet
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Our Algorithm, Part II
3. If the jet breaks into two subjets, repeat declustering on those subjets
4. Keep cases with 3 or 4 final subjets– 4th is rare, and tends to be very soft
5. Apply kinematic cuts
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Details
• C/A R parameter, p, and r picked according to event HT > {1, 1.6, 2.6} TeV– R = {0.8, 0.6, 0.4}– p = Pt * {0.10, 0.05, 0.05}
• Same p for both declustering stages
– r = {1.9, 1.9, 1.9}
• Also jet reconstruction criteria– Pt > max(500 GeV, 0.7*HT/2)– || < 2.5
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1 TeV Top-Jet Gallery
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1 TeV Light-Jet Gallery
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Some 2 TeV Top-Jets
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Subjet Rates
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Kinematic Cuts
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Final Efficiencies
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Dijet Mass Spectrum
• All-hadronic tops• PYTHIA 6.4 continuum QCD and top pair• Pt > max(500 GeV, mjj/4)
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Dijet Mass Spectrum
• All-hadronic tops• PYTHIA 6.4 continuum QCD and top pair• Pt > max(500 GeV, mjj/4)
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Loose Ends
• Physics– PYTHIA = QCD?
• Technology– Ideal calorimeter = real calorimeter?
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MadGraph “Dijet”
“pp > jjjj”
• Ptj > 50 GeV• Rjj > 0.13• lead jet Pt = [1, 1.2] TeV• no parton shower,
no hadronization• s rescaled at “soft” vertices
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Physics Summary
• PYTHIA vs MG vs HERWIG– Rates match at ~50% level
• Factor of ~2 uncertainty in dijet BG estimate
– Kinematic distributions extremely similar
• In any event, can probably measure QCD distributions in-situ with sidebands of jet mass
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CMS Collaboration
• Sal Rappoccio, Morris Swartz, Petar Maksimovic
• Working on implementation of algorithm in CMS framework
• Proof of concept: 2 TeV Z’, full detector simulation– PYTHIA-based physics– Decays to light quarks and tops– Pt = 0.5~1 TeV
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Us vs Sal
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Us vs Sal
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Technology Summary
• Bottomline: it still works
• Final efficiencies for t / q (2 TeV Z’)– Us: 36% / 0.7%– Sal: 32% / 1.0%
• Most S/B degradation attributable to energy resolution
• Higher stats / masses in the pipeline– How fast do efficiencies fall off?
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Future Directions
• ECAL– Captures ~10% of jet energy– 5x better spatial resolution
• Tracker– Sees all charged particles– Even better resolution– Crowded for individual track ID, but maybe not
for tracing Et flow
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Summary• Top-jets can be treated much like b-jets
– Tag 10’s of % of top jets– Mistag ~1% of background jets
• Method works in full detector sim
• Should significantly help searches for top resonances and heavy top partners at the TeV scale
• Stay tuned for experimental results in the near future!!