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Steve Ellis (See Also Jesse Thaler last week). New (BSM) Physics Searches with Single Jets: Jet Substructure and Jet Grooming. LPC @ Fermilab 11.18.10. Background/Assumptions:. LHC is intended to find new “stuff” (BSM physics) Hadronic “stuff” will be organized into jets

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new bsm physics searches with single jets jet substructure and jet grooming

Steve Ellis

(See Also Jesse Thaler last week)

New (BSM) Physics Searches with Single Jets: Jet Substructure and Jet Grooming

LPC @ Fermilab 11.18.10

background assumptions
Background/Assumptions:
  • LHC is intended to find new “stuff” (BSM physics)
  • Hadronic “stuff” will be organized into jets
  • At 14 (7) TeV many interesting particles (t, W, Z, Susy, ..) will be boosted enough to be in a single jet

 Want to use single jets in search for BSM physics

  • Want to distinguish jets with decays from QCD jets
  • Want to use jet substructure for this purpose

LPC @ Fermilab S.D. Ellis 11/18/10

outline issues
Outline & Issues
  • Brief review of (QCD) jets - defined by algorithms (no intrinsic definition) - jets have substructure, including masses (not just 1 parton, 1 jet)
  • Typically use Recombination (kT) jets

 natural substructure, but also algorithm systematics (shaping of distributions) contributions from (uncorrelated) ISR, FSR, UE and Pile-up

  • But can use Cone or Anti-kT as jet finder

Warning!

LPC @ Fermilab S.D. Ellis 11/18/10

outline issues cont d
Outline & Issues (cont’d)
  • Search for BSM physics in SINGLE jets at the LHC by separating from QCD; want generic techniques, which are robust under systematic effects– Consider a variety of jet substructure measures: 1) masses (simply look for bumps)

2) angularities (single jet version of thrust-like) distribution 3) subjets found with jet algorithm with smaller size parameter, e.g., “taggers” top q, Higgs, or “groomers” that delete all but the leading subjets

LPC @ Fermilab S.D. Ellis 11/18/10

outline issues cont d1
Outline & Issues (cont’d)
  • QCD yields a large but Smooth (limited structure) QCD background
  • But substructure (mass bumps) in signal can degraded by
      • algorithm systematics
      • uncorrelated UE and Pile-Up contributions
  • Need to “clean-up” or “Groom” the jets, e.g., PRUNING

- remove large angle, soft branchings

  • Validate with studies of boosted top q’s, W’s, Z’s at Tevatron and LHC

LPC @ Fermilab S.D. Ellis 11/18/10

jet substructure a new tool for the lhc
Jet Substructure – a new tool for the LHC!

Two general approaches –

1) Taggers that use specific decay properties, e.g., top quarks, W/Zs, Higgs, … to predict jet substructure (subjets)

2) More generic jet grooming to let the underlying structure, e.g., mass, be manifest (even when don’t know what it is, i.e., searches) and reduce impact of UE, PU and algorithm details

See the overview in the (immediately) forthcoming Proceedings of Boost 2010!

LPC @ Fermilab S.D. Ellis 11/18/10

defining jets
Defining Jets
  • Map the observed (hadronic) final states onto the (short-distance) partons by summing up all the approximately collinear stuff, ideally on an event-by-event basis.
  • Need rules for summing  jet algorithm Start with list of particles/towers End with list of jets (and stuff not in jets)
  • E.g.,
  • Cone Algorithms, based on fixed geometry – focus on core of jetSimple, “well” suited to hadron colliders with Underlying Events (UE),but found jets can/do overlap
  • Recombination (or kT) Algorithm, based on pairwisemerging to undo shower Tends to “vacuum up” soft particles, “well” suited to e+e- colliders

LPC @ Fermilab S.D. Ellis 11/18/10

recombination algorithm focus on undoing the shower pairwise natural definition of substructure
Recombination Algorithm – focus on undoing the shower pairwise,  Natural definition of substructure

Merge partons, particles or towers pairwise based on “closeness” defined by minimum value ofkT, i.e. make list of metric values(rapidity y and azimuth , pTtransverse to beam)

If kT,(ij)is the minimum, merge pair (add 4-vectors), replace pair with sum in list and redo list;

IfkT,iis the minimum →i is a jet! (no more merging for i, it is isolated by D),

1 angular size parameterD (NLO, equals Cone for D = R, Rsep = 1), plus = 1, ordinary kT, recombinesoft stuff first = 0, Cambridge/Aachen (CA),controlled by angles only  = -1, Anti-kT, justrecombinestuff around hard guys – cone-like (with seeds)

LPC @ Fermilab S.D. Ellis 11/18/10

recombination algorithm in action here ca algorithm on qcd jet
Recombination Algorithm– in action, here CA algorithm on QCD jet

Think of starting with calorimeter cells, recombine “closest” pair at each step leading to larger pT

For CA close in quantity

(0.05 x 0.05) Cells with E > 1 GeV

low pTtohigh pT

LPC @ Fermilab S.D. Ellis 11/18/10

note the details of the substructure at each step depend on the algorithm
Note: the details of the substructure (at each step) depend on the algorithm

kT(pT and angle)

CA (just angle)

LPC @ Fermilab S.D. Ellis 11/18/10

jet masses in qcd a brief review
Jet Masses in QCD: A Brief Review
  • In NLO PertThy

Phase space from pdfs, f ~ 1 & const

Dimensions

Jet Size, D = R ~ , determined by jet algorithm

Peaked at low mass(log(m)/m behavior),cuts off for (M/P)2 > 0.25 ~ D2/4 (M/P > 0.5) large mass can’t fit in fixed size jet, QCD suppressed for M/P > 0.3 (~  < 3) Want heavy particle boosted enough to be in a jet (use large-ish D ~1), but not so much to be QCD like (~ 2 <  < 5)

Soft – Collinear pole version

Useful QCD “Rule-of-Thumb”

LPC @ Fermilab S.D. Ellis 11/18/10

jet mass in pythia matched set d 1 500 gev c pt 700 gev c
Jet Mass in PYTHIA (matched set)D = 1, 500 GeV/c < pT < 700 GeV/c

Algorithm matters

Tails depends (somewhat) on being matched set

Turns over

LPC @ Fermilab S.D. Ellis 11/18/10

jet mass cdf data cdf pub jet public 10199 7 19 10
Jet Mass – CDF Data (CDF/PUB/JET/PUBLIC/10199 7/19/10)

At least qualitatively the expected shape – masses slightly larger than MC – need the true hard emissions (as in matched sets)

Large mass tail grows, as expected, with jet size parameter in the algorithm -You find what you look for!

LPC @ Fermilab S.D. Ellis 11/18/10

angularity a jet shape measure introduced by g sterman and collaborators
Angularity – a jet shape measure(introduced by G. Sterman and collaborators)
  • (Full) Event shapes in e+e- - (from early days) First define Thrust direction and value viawrt the Thrust direction define angularity with parameter aNote for a = 0 just (1-T). For massless particles

LPC @ Fermilab S.D. Ellis 11/18/10

slide15
Angularity – Apply to single jetFind jet components with algorithm, jet direction replaces Thrust direction
  • Single Jet Angularity – some choice involved, e.g., in CDF note Public/10199 (following Sterman, et al.)

where this is applied to jets binned in both pT,J (EJ) and mJ

This results in a limited range for general a

LPC @ Fermilab S.D. Ellis 11/18/10

cdf data
CDF data

Cone, R = 0.4

Cone, R = 0.7

Theory = minimal double pole expression

LPC @ Fermilab S.D. Ellis 11/18/10

slide17
Angularity – Apply to single jet - IIFind jet components with algorithm, jet direction replaces Thrust direction
  • Single Jet Angularity – a different choice, Q 2E(SDE, Hornig, Lee, Vermilion & Walsh, 1001.0014, applied with SCET in e+e-)

now applied to jets binned only in pT,J (EJ) and we have ie., factor of m/2E compared to earlier. Also

A narrow jet

Recall earlier

LPC @ Fermilab S.D. Ellis 11/18/10

scet results nlo nll in global logs no hadronization
SCET Results – NLO + NLL (in global logs) no hadronization

General features:

Damped at small  (Sudakov) Peak and then falls off

Shoulder at large 

Distributions:

Broader as R (~D) increases Broader as a increases Broader for gluons

LPC @ Fermilab S.D. Ellis 11/18/10

more scet compare to mc look at peak
More SCET: Compare to MC, look at peak

Distribution qualitatively like MC, but hadronization is relevant (broader)

Peak moves with R and a

LPC @ Fermilab S.D. Ellis 11/18/10

the future for angularities
The Future for Angularities
  • Data – plot for other definition either without mass binning or in multiple mass bins (even with top contamination) – test for systematics noted above
  • Theory – evaluate the double distribution

-3

Pythia Version

With a = -3

0

LPC @ Fermilab S.D. Ellis 11/18/10

finding heavy particles with jets issues
Finding Heavy Particles with Jets - Issues
  • QCD multijet production rate >> production rate for heavy particles
  • In the jet mass spectrum, production of non-QCD jets may appear as local excesses (bumps!) but must be enhanced using analyses
  • Use jet substructure as defined by recombination algorithms to refine jets
  •  Algorithm will systematically shape distributions
  • Use top quark as surrogate new particle.

σttbar≈ 10-3σjj

ttbar

QCD dijet

shaped by the jet algorithm

arb. units

arb. units

falling, no intrinsic large mass scale

LPC @ Fermilab S.D. Ellis 11/18/10

mJ (GeV/c2)

mJ (GeV/c2)

slide22

Reconstruction of Jet Substructure – QCD vs Heavy Particle

  • Want to identify a heavy particle reconstructed in a single jet via substructure
    • Need correct ordering in the substructure and accurate reconstruction (to obtain masses accurately)
    • Need to understand how decays and QCD differ in their expected substructure, e.g., distributions at branchings.

 But jet substructure affected by the systematics of the algorithm, and by kinematics when jet masses/subjet masses are fixed.

The algorithm metric

affects the substructure

- introduces bias

interpret last recombinations

as a heavy particle or QCD jet

LPC @ Fermilab S.D. Ellis 11/18/10

systematics of the jet algorithm
Systematics of the Jet Algorithm
  • Consider generic recombination step: i,j➜p
  • Useful variables: (Lab frame)
  • Merging metrics:
  • Daughter masses (scaled by jet mass) :
  • In terms of z, R, the algorithms will give different kinematic distributions:
    • CA orders only in R: z is unconstrained
    • kTorders in z· R: z and R are both regulated
    • The metrics of kT and CA will shape the jet substructure.

LPC @ Fermilab S.D. Ellis 11/18/10

phase space for 1 2

2 is softer

Phase Space for 1→2

a1= 0, a2= 0

a1= 0.46, a2= 0

  • The allowed phase space in R, zfor a fixed  (mJ and pT) is nearly one-dimensional
  • QCD and decays will weight the phase space differently
  • Cutoffs on variables set by the kinematics, not the dynamics
  • Sample of phase space slices for different subjet masses,

1 is softer

a1= 0.9, a2= 0

a1= 0.3, a2= 0.1

1 never softer

LPC @ Fermilab S.D. Ellis 11/18/10

1 2 decay in a jet
1→2 Decay in a Jet
  • Goal is to identify jets reconstructing a heavy particle and separate them from QCD jets
  • Consider a 1→2 decay (J 1,2) reconstructed in a jet, massless daughters (a1= a2= 0) for now
  • Requirement to be in a jet: R12 < D- algorithm independent
  • Look at the decay in terms of the algorithm variables

Large D is needed to reconstruct jets with a lower boost - use D = 1.0,sweet spot  ~ 3, pT ~3 mJ, xJ ~ 0.1

Recall for QCD jet pT ~5 <mJ >

LPC @ Fermilab S.D. Ellis 11/18/10

slide26

1→2 Decay in a Jet (unpolarized)

No enhancement at the lower limit in - unlike QCD

Enhancement at the lower limit for - like QCD

Decays not reconstructed: small , large

J rest frame

lab frame

J (boost to the lab)

J

2

2

1

beam

direction

LPC @ Fermilab S.D. Ellis 11/18/10

1

slide27

1→2 Decay in a Jet

a1 = 0, a2 = 0

Cutoffs are set by the kinematics

- same between QCD and decay

with fixed

Enhancement at the lower limit for - like QCD

No enhancement at the lower limit in - unlike QCD

LPC @ Fermilab S.D. Ellis 11/18/10

slide28

QCD Splittings

Take a leading-log approximation of QCD:

For small angles - good approximation for a splitting in a jet:

This lets us fix xJ (or  ). Distribution in xJ :

LPC @ Fermilab S.D. Ellis 11/18/10

slide29

QCD Splittings: R12 and z

a1 = 0, a2 = 0

Fix  (xJ), find distributions in R12 and z

Limits set by the kinematics

QCD will have many more soft (small z)

splittings than decays do - QCD

splittings are small z, small xJenhanced

Enhancement at the lower limit in - like decays

Enhancement at the lower limit in z- unlike decays

LPC @ Fermilab S.D. Ellis 11/18/10

summary of dynamics of qcd vs decays
Summary of Dynamics of QCD Vs Decays:
  • Distributions in R very similar (for fixed boost)
  • QCD enhanced at small z, xJ
  • Will these be represented in the last recombinations of a jet?

LPC @ Fermilab S.D. Ellis 11/18/10

effects of the jet algorithm algorithm bias
Effects of the Jet Algorithm – Algorithm Bias
  • Recombination metrics:
  • Recombinations arealmost always monotonic in the metric
    • The algorithm cuts out phase space in (z, Rij) as it proceeds
    • Certain decays will be reconstructed earlier in the algorithm, or not at all

kT

CA

pTp dependent

boundaries

late

late

intermediate

intermediate

early

early

LPC @ Fermilab S.D. Ellis 11/18/10

typical recombinations
Typical Recombinations
  • Late recombinations are set by the available phase space
    • For CA, Rmust be near D, and the phase space tends to create small zrecombinations
    • For kT, z Rwill be larger, with a pT dependent cut
  • The soft (small z) radiation is recombined earlier in kT, meaning it is harder to identify - leads to poorer mass resolution

Matched QCD sample (2, 3, 4 partons) from MadGraph/Pythia, jet pT between 500-700 GeV

last recombination

last recombination

LPC @ Fermilab S.D. Ellis 11/18/10

comparing ca and kt

Matched QCD, jet pT between 500-700 GeV

Comparing CA and kT:
  • Final recombinations for CA not QCD-like - No enhancement at small R
  • Final recombinations for kT more QCD-like - Enhanced at small zandR
  • kT has poorer mass resolution - Soft objects recombined early in algorithm - more merged

tt sample from MadGraph/Pythia

jet pT between 500-700 GeV

-

LPC @ Fermilab S.D. Ellis 11/18/10

summary identifying reconstructed decays in jets
Summary: Identifying Reconstructed Decays in Jets
  • Reconstruction of a decay can be hidden in the substructure
  • Small z recombination unlikely to accurately give decay
  • Small z recombinations also arise from UE and pile-up
  •  The jet algorithm significantly shapes the jet substructure – less so for kTbut has poorer mass resolution
  •  Proposing amethod to deal with these issues: modify the jet substructure to reduce algorithm effects and improve mass resolution, background rejection, and heavy particle identification - pruning

LPC @ Fermilab S.D. Ellis 11/18/10

pruning

SDE, Jon Walsh and Chris Vermilion, 0903.5081, 0912.0033- go to tinyurl.com/jetpruning

Pruning :

Procedure:

  • Start with the objects (e.g. towers) forming a jet found with a recombination algorithm (kT, CA, Anti-kT)
  • Rerun with kT or CA algorithm, but at each recombination test whether soft – large angle:
    • z < zcut and ΔRij > Dcut
  • If true (a soft, large angle recombination), prune the softer branch by NOT doing the recombination and discarding the softer branch (bottom up approach)
  • Proceed with the algorithm

 The resulting jet is the pruned jet

LPC @ Fermilab S.D. Ellis 11/18/10

choices of the pruning parameters
Choices of the pruning parameters

no pruning

After studies we choose:

CA: zcut = 0.1 and Dcut = mJ/PT,J

optimal

pruning

kT: zcut = 0.15 and Dcut = mJ/PT,J

no pruning

over-pruning

 mJ/PT,J is IR safe measureof opening angle of found jet and “adaptive” to the specific jet

over-pruning

shower pruning

LPC @ Fermilab S.D. Ellis 11/18/10

prune fixed order result
Prune Fixed Order Result

“Large” mass tail unchanged(symmetric splitting)

Move “small” masses to zero mass(asymmetric splitting)

LPC @ Fermilab S.D. Ellis 11/18/10

pruning in action
Pruning in Action

Pruning of a QCD jet

near the top mass

with the CA algorithm

a typical jet (see above)

Red is higher pT

Blue is lower pT

Green X is a pruning

Start with cells with

energy > 1GeV

R

Prune

pT: 600 → 590 GeV

mass: 170 → 160 GeV

z

LPC @ Fermilab S.D. Ellis 11/18/10

pruning in action1
Pruning in Action

Pruning of a QCD jet

near the top mass

with the CA algorithm

atypical jet

Red is higher pT

Blue is lower pT

Green X is a pruning

Start with cells with

energy > 1GeV

R

Prune

pT: 600 →550 GeV

mass: 180 →30 GeV

z

LPC @ Fermilab S.D. Ellis 11/18/10

impact of pruning qualitatively just what we want
Impact of Pruning – qualitatively just what we want!

Win

Win

The mass resolution of prunedtop jets is narrower  Pruned QCD jets have lower mass, sometimes much lower

QCD jets

Top jets

CA

kT

LPC @ Fermilab S.D. Ellis 11/18/10

500 < pT < 700 GeV

test pruning in more detail
Test Pruning in more detail:
  • Study of top reconstruction:
    • Hadronic top decay as a surrogate for a massive particle produced at the LHC
    • Use a QCD multijet background based on matched samples from 2, 3, and 4 hard parton MEs
    • ME from MadGraph, showered and hadronized in Pythia, jets found with FastJet
  • Look at several quantities before/after pruning: Mass resolution of reconstructed tops (width of bump),small width means smaller background contribution
    • pTdependence of pruning effect (bin in pT)
    • Dependence on choice of jet algorithm and angular parameter D
    • UE dependence

LPC @ Fermilab S.D. Ellis 11/18/10

defining reconstructed tops search mode
Defining Reconstructed Tops – Search Mode
  • A jet reconstructing a top will have a mass within the top mass window, and a primary subjet mass within the W mass window - call these jets top jets
  • Defining the top, W mass windows:
    • Fit the observed jet mass and subjet mass distributions with (asymmetric) Breit-Wigner plus continuum  widths of the peaks
    • The top and W windows are defined separately for pruned and not pruned - test whether pruning is narrowing the mass distribution

pruned

unpruned

sample

mass fit

LPC @ Fermilab S.D. Ellis 11/18/10

defining reconstructed tops
Defining Reconstructed Tops

fit mass windows to identify

a reconstructed top quark

peak function: skewed Breit-Wigner

fit top jet mass

peak width Γjet

plus continuum background distribution

2Γjet

LPC @ Fermilab S.D. Ellis 11/18/10

defining reconstructed tops1
Defining Reconstructed Tops

fit mass windows to identify

a reconstructed top quark

cut on masses of jet (top mass) and subjet(W mass)

fit top jet mass

peak width Γjet

fit W subjet mass

2Γ1

2Γjet

LPC @ Fermilab S.D. Ellis 11/18/10

defining reconstructed tops2
Defining Reconstructed Tops

fit mass windows to identify

a reconstructed top quark

cut on masses of jet (top mass) and subjet (W mass)

fit top jet mass

fit W subjet mass

window widths for pruned (pX) and unpruned jets

LPC @ Fermilab S.D. Ellis 11/18/10

mass windows and pruning summary
Mass Windows and Pruning - Summary
  • Fit the top and W mass peaks, look at window widths for unpruned and pruned (pX) cases in (200 - 300 GeV wide) pT bins

 Pruned windows narrower, meaning better mass bump resolution - better heavy particle ID

 Pruned window widths fairly consistent between algorithms (not true of unpruned), over the full range in pT

LPC @ Fermilab S.D. Ellis 11/18/10

statistical measures
Statistical Measures:
  • Count top jets in signal and background samples in fitted bins
  • Have compared pruned and unpruned samples with 3 measures:
    • ε, R, S - efficiency, Sig/Bkg, and Sig/Bkg1/2

Here focus on S

D = 1

S > 1 (improved likelihood to see bump if prune), all pT, all bkgs, both algorithms

LPC @ Fermilab S.D. Ellis 11/18/10

heavy particle decays and d
Heavy Particle Decays and D

See also Krohn, Thaler & Wang (0903.0392)

  • Heavy particle ID with the unpruned algorithm is improved when D is matched to the expected average decay angle
  • Rule of thumb (as above): R = 2m/pT
  • Two cases:

D

R

D

R

  • D > R
  • lets in extra radiation
  • QCD jet masses larger
  • D < R
  • particle will not be reconstructed

LPC @ Fermilab S.D. Ellis 11/18/10

improvements in pruning
Improvements in Pruning
  • Optimize D for each pT bin: D = min(2m/pTmin, 1.0)  (1.0,0.7,0.5,0.4) for our pT bins
  • Pruning still shows improvements
  • How does pruning compare between fixed D = 1.0 and D optimized for each pTbin  SD = SD opt/SD=1?
  • Little further improvement obtained by varying D
  • SD = 1 in first bin
  • Pruning with Fixed D does most of the work

LPC @ Fermilab S.D. Ellis 11/18/10

underlying event rejection with pruning
Underlying Event Rejection with Pruning

The mass resolution of pruned jets is (essentially) unchanged with or without the underlying event

top study

no pruning

pruning

CA

kT

LPC @ Fermilab S.D. Ellis 11/18/10

500 < pT < 700 GeV

underlying event rejection with pruning1
Underlying Event Rejection with Pruning

The jet mass distribution for QCD jets is significantly suppressed for pruned jets (essentially) independent of the underlying event

QCD study

no pruning

pruning

CA

kT

LPC @ Fermilab S.D. Ellis 11/18/10

500 < pT < 700 GeV

pruning reveals for hidden truth
Pruning reveals for hidden truth -

LPC @ Fermilab S.D. Ellis 11/18/10

other groomers boost 2010 def n applied to found jets anti kt r 1 0
Other Groomers (Boost 2010 def’n) – applied to “found” jets (Anti-kT, R = 1.0)
  • Trimming – Re-cluster into Anti-kTsubjets with Rsub = 0.35, discard if pTsub < 0.03 pTjetKrohn, Thaler & Wang (0912.1342)
  • Filtering - Re-cluster into C/A subjets with Rfilt= 0.35, kept only 3 hardest subjets Butterworth, Davison, Rubin & Salam (0802.2470) Seymour (Z. Phys. C62 (1994) 127)

LPC @ Fermilab S.D. Ellis 11/18/10

results from boost 2010 mass distribution in pt bins
Results from Boost 2010mass distribution in pT bins

300-400

Suppressed background

Narrower peaks

500-600

LPC @ Fermilab S.D. Ellis 11/18/10

compare top taggers
Compare top taggers

Similar exponential correlation for all “optimized” taggers (lowest mistag rate for given efficiency)

ATLAS, T/W use similar kTreclustering and no grooming

CMS, Hopkins use similar C/A reclustering and filtering

LPC @ Fermilab S.D. Ellis 11/18/10

pruning as top tagger
Pruning as top tagger -

Eff = 20% (use late)

Eff = 50% (use early)

Zcut = 0.05, Dcut = 0.2 m/pT

28 < MW/GeV < 128, 120 < Mtop/GeV < 228

zcut = 0.1, Dcut = 0.4 m/pT

68 < MW/GeV < 88, 150 < Mtop/GeV < 190

By choice not optimizing for Signal/BGK, i.e., broad mass bins

LPC @ Fermilab S.D. Ellis 11/18/10

summary pruning
Summary - Pruning
  • Pruning narrows peaks in jet and subjet mass distributions of reconstructed top quarks
  • Pruning improves both signal purity (R) and signal-to-noise (S) in top quark reconstruction using a QCD multijetbackground
  • The D dependence of the jet algorithm is reduced by pruning - the improvements in R and S using an optimized Dexhibit only small improvement over using a constant D = 1.0 with pruning
  • A generic pruning procedure based on D = 1.0 CA (or kT) jets can
    • Enhance likelihood of success of heavy particle searches
    • Reduce systematic effects of the jet algorithm, the UE and PU
    • Cannot be THE answer, but part of the answer, e.g., use with b-tagging, require correlations with other jets/leptons (pair production), use more than one groomer, etc.

LPC @ Fermilab S.D. Ellis 11/18/10

summary general
Summary general:
    • Systematicsof the jet algorithm are important in studying jet substructure
    • The jet substructure we expect from the kT and CA algorithms are quite different
    • Shaping can make it difficult to determine the physics of a jet
  • Should certify Groomers and Taggers in data by finding tops, W’s and Z’s in single jets in early LHC running and with Tevatron data
  • Should study other shape variables, e.g., angularities, in data

But only 10’s of well boosted tops in 100 pb-1 at 7 TeV (pT > 500 GeV/c)

LPC @ Fermilab S.D. Ellis 11/18/10

summary general1
Summary general:
  • Much left to understand about jet substructure (here?), e.g.,
      • How does the detector affect jet substructure and the systematics of the algorithm? How does it affect techniques like pruning? What are experimental jet mass uncertainties?
      • How can jet substructure fit into an overall analysis? How orthogonal is the information provided by jet substructure to other data from the event?Which tools work best when used together?Better theory tools for multiple scales, pJ and mJ– SCET

LPC @ Fermilab S.D. Ellis 11/18/10

extra detail slides
Extra Detail Slides

LPC @ Fermilab S.D. Ellis 11/18/10

jets a brief history at hadron colliders
Jets – a brief history at Hadron Colliders
  • JETS I – Cone jets applied to data at the ISR, SpbarpS, and Run I at the Tevatron to map final state hadrons onto LO (or NLO) hard scattering, essentially 1 jet 1 parton (test QCD)Little attention paid to masses of jets or the internal structure, except for energy distribution within a jet – studied at leading order or with MC
  • JETS II – Run II & LHC, starting to look at structure of jets: masses and internal structure – a jet renaissance

LPC @ Fermilab S.D. Ellis 11/18/10

some other jet grooming techniques
(Some) Other Jet Grooming techniques:

Specific tagging, e.g., top or Higgs tagging

  • MassDrop/Filtering - Butterworth, Davison, Rubin & Salam (0802.2470) - reprocess jets (top down) to find (fixed number) of subjets (2 or 3)
  • Top Tagging - Kaplan, Rehermann, Schwartz & Tweedie (0806.0848) – look for specific substructure of tops (3 or 4)Thaler & Wang (0806.0023)

Generic Grooming

  • Trimming – Krohn, Thaler & Wang (0912.1342) – reprocess to find primary subjets (pT > pTcut, any number of subjets)
  • Trimming & Pruning, Soper & Spannowsky (1005.0417)

LPC @ Fermilab S.D. Ellis 11/18/10

note top jet with ca
Note: Top jet with CA

No Pruning

Pruning

LPC @ Fermilab S.D. Ellis 11/18/10

consider impact of gaussian 1 smearing
Consider impact of (Gaussian1) smearing

Smear energies in “calorimeter cells” with Gaussian width (300 GeV/c < pT < 500 GeV/c)

QCD

 Pruning still helps (pruned peaks are more narrow), but impact is degraded by detector smearing

1 From P. Loch

LPC @ Fermilab S.D. Ellis 11/18/10

statistical measures1
Statistical Measures:

 Smearing degrades but does not eliminate the value of pruning

LPC @ Fermilab S.D. Ellis 11/18/10

simulated data plots peskin plots
“Simulated” data plots (Peskin plots)
  • Include signal (tops) and bkg (QCD) with correct ratio and “simulated” statistical uncertainties and fluctuations, corresponding to 1 fb-1 (300 GeV/c < pT < 500 GeV/c)

Now a clear signal in jet mass

Find (small) mass bump and cut on it

Find daughter mass bump and cut on it

Pruning enhances the signal, but its still tough in a real search

For known top quark, pruning + 100 pb-1 may be enough (especially with b tags)

LPC @ Fermilab S.D. Ellis 11/18/10