Gluon Tagging and Quark & Gluon Samples

Gluon Tagging and Quark & Gluon SamplesHow well can we do at the 7TeV LHC?

Jason Gallicchio

Harvard

12 April 2011

Jason Gallicchio (Harvard) Gluon Tagging and Quark & Gluon Samples 12 April 2011 1 / 70

Short Outline

Biggest Motivation: Reject Gluey LHC Backgrounds

Part 1: The Gluon Tagger

Part 2: Finding Pure Samples of Quark and Gluon Jets


Gluon Tagging Motivation

Other than “Wouldn’t it be nice to know?”

Most new physics gives quark rather than gluon jets:





8-jet Gluino event: pp → g̃g̃ and each g̃ → qq̄χ01 + qq̄χ0

2

Tagging is especially important without W , Z, γ, ℓ±, B-Tags, or /ET





8-jet Gluino event: pp → g̃g̃ and each g̃ → qq̄χ01 + qq̄χ0

2

Tagging is especially important without W , Z, γ, ℓ±, B-Tags, or /ET

Assignment: Your favorite example of gluon or non-b quark signals.


Motivation

Interesting standard model physics also tends to be quark-heavy

Tops (tt̄ → 4 or 6 quarks)

W ’s decaying hadronically (there’s no b-tag): W+ → ud̄ or cs̄

WW → 4 light quarks


Motivation





Vector Boson Fusion


Motivation





Vector Boson Fusion

The generic catchall: “Understand QCD”

Q & G Jets as backgrounds to boosted top, W , H, etc.


Motivation





Vector Boson Fusion



NOTE!LEP found b-jets look more like gluon jets than light quark jets(in terms of size and particle count)


Motivation





Vector Boson Fusion



NOTE!LEP found b-jets look more like gluon jets than light quark jets(in terms of size and particle count)

Eventually combine Gluon-Tagging with B-Tagging and τ -Tagging


But There’s a Lot of Glue to Get Stuck In

50 100 200 400 800 1600

Chance EACH Jet is Quark

0%

100%

80%

60%

40%

20%

2j3j

4j

pT Cut on All Jets (GeV)

So chance that all 4 jets & 100GeV are quark: (30%)4 = 0.8%


Outline


Part 1: The Tagger

Example Observables: Jet Mass and Charged Track CountEvaluating the power of observables: Background RejectionFamiliar presentation of ‘Jet Shape’ and its problemsMeasuring ‘size’ of jetsCombining 2 or more observables



Disclaimer

There will be NO single way to unambiguously sort jets.


Disclaimer


What’s different now than 20 years ago?


Disclaimer



LHC calorimeter spatial resolution. (Sometimes 10x CDF or D0)


Disclaimer




LHC is very jetty and pp is more gluey.


Disclaimer




LHC is very jetty and pp is more gluey.

The most difficult signals are buried under multi-jet events.


Goal

Which observables are useful:

Individually?

When combined?


Goal

Which observables are useful:

Individually?

When combined?

Interesting variables can be:

Studied theoretically

Verified experimentally


Differences in Quarks vs Gluons

Color Charge: CF vs CA → jet mass, size, track count

Color Connections: 1 vs 2 → eccentricity and pull

Electrical Charge → charge-weighted track pT

Spin: 1/2 vs 1 → not explicitly used

...


Brief QCD Showering Theory

Gluon has a greater effective color charge (squared) than quark:

Gluon adjoint’s CA vs Quark fundamental’s CF

CA

CF=

9

4


Brief QCD Showering Theory

Gluon has a greater effective color charge (squared) than quark:


CA

CF=

9

4

Jet Mass in the small angle limit:

⟨

M2⟩

= Cαs

πp2T R2

where C ∼ CA for gluon jets, and ∼ CF for quark jets.


Jet Mass in Detail

Normalizing by pT (200GeV in this sample) generalizes better.

0 0.05 0.1 0.15 0.2 0.25 0.3 0.350

1

2

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5

6

mass/Pt

QG


Evaluating the Observable: Sliding Cut

0 0.05 0.1 0.15 0.2 0.25 0.3 0.350

1

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mass/Pt

QG

Sliding Cut


ROC Curve

0 0.05 0.1 0.15 0.2 0.25 0.3 0.350

1

2

3

4

5

6

mass/Pt

QG

Sliding Cut

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0.1

0.2

0.3

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1

ROC Curve for mass/Pt

Quark Signal Efficiency

Glu

on B

ackgro

und R

ejec

tion


ROC Curve

0 0.05 0.1 0.15 0.2 0.25 0.3 0.350

1

2

3

4

5

6

mass/Pt

QG

Sliding Cut

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0.1

0.2

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1



Glu

on B

ackgro

und R

ejec

tion

50/50


Other Jet Sizes and pT s

0 0.05 0.1 0.15 0.2 0.25 0.3 0.350

1

2

3

4

5

6

mass/Pt

QG

Sliding Cut0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

0.1

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1



Glu

on B

ackgro

und R

ejec

tion

50/50

0.35

0.40

0.45

0.50

0.4 0.6 0.8 1.0

Bac

kgro

und R

ejec

tion

Jet Size 50 GeV

1600 GeV800 GeV400 GeV

100 GeV200 GeV

0.20

0.2

mass/Pt @ 80% Signal Efficiency

Rather than showing 10 ∗ 6 = 60 ROC Curves, pick 80% point on eachJason Gallicchio (Harvard) Gluon Tagging and Quark & Gluon Samples 12 April 2011 15 / 70

Brief Theory II


CA

CF=

9

4

Lore: quark jet first emits a gluon, and then it dominates the cascade.


Brief Theory II


CA

CF=

9

4


Multiplicity of any particle in a gluon jet should be CA/CF = 9/4times greater (confirmed at LEP).

〈Ng〉〈Nq〉

=CA

CF


Brief Theory II


CA

CF=

9

4


Multiplicity of any particle in a gluon jet should be CA/CF = 9/4times greater (confirmed at LEP).

〈Ng〉〈Nq〉

=CA

CF

σ2g

σ2q

=CA

CF.

(Calculated to N3LO by Capella, et al. hep-ph/9910226)

No detector simulation, but require charged particles pT > 500MeV.


Charged Particles Count

...this old favorite does quite well at high pT :

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1

2

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7

components_jet0_ak05_charged_countcomponents_jet0_ak05_charged_count

0.4 0.6 0.8 1.0

0.45

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0.55

0.60

0.65

0.70

50 GeV

1600 GeV

800 GeV

400 GeV

100 GeV

200 GeV

Bac

kgro

und R

ejec

tion

Jet Size

Charged Track Count @ 80% Signal Efficiency

Higher pT means more charged tracks and more ‘time’ to establishCA/CF .


Visual Differences

Accumulate 3 million back-to-back dijet events

Quark Jets Gluon Jets

(Same total amount of pT , which is hidden by logarithmic color bands.)


Jet Shape

r


Jet Shape

r


Jet Shape Distribution vs Average

0 0.2 0.4 0.6 0.8 10

1

2

3

4

5

Integrated Jet Shape out to r = 0.1 for 100 GeVIntegrated Jet Shape out to r = 0.1 for 100 GeV

Distribution is not narrow gaussian around averageCorrelations between bins is also useful


Radial Moment – a measure of the “girth” of the jet

Weight pT deposits by distance from jet center

Radial Moment, or Girth : g =∑

i∈jet

piTpjetT

|ri|

0 0.1 0.2 0.3 0.4 0.5 0.60

1

2

3

4

5

6

7

radial momentradial moment


Angularities

Jet Angularities : Aa =∑

i∈jet

Ei fa(θ)

fa(θ) = sinaθ (1− cos θ)1−a with θ =π|ri|2R

or just θ

a=+1.9

a=-3a=0

Π

4

Π

2

Π Θ

2 R

1

1.5

fa

Normalization? none, mass, jet energy, jet pT .Jason Gallicchio (Harvard) Gluon Tagging and Quark & Gluon Samples 12 April 2011 23 / 70

Angularities

-3 -2 -1 1 2a

0.50

0.55

0.60

0.65

0.70

0.75

Angularity vs a for different Jet Sizes

Small R=0.2 Jets (red) perform better than Large R=1.0 Jets (pink)


Radial Moments and Their Kernels

Linear Moment Quadratic Moment

Tapered Moment Tapered Radial Kernel


Optimal Kernel

0.1 0.2 0.3 0.4 0.5 0.6∆R

-0.2

0.2

0.4

0.6

0.8

1.0

Optimal Kernel for 200 GeV R=0.7

0 0.1 0.2 0.3 0.4 0.5 0.60

1

2

3

4

5

Optimal Kernel DistributionOptimal Kernel Distribution

Positive kernel weights mean gluon-like.

Overall vertical shift or scaling leads to same distribution.

Quarks have most of their pT near the center.


Optimal Kernel for different pT ’s and R = 1.0

1600GeV → 50GeV

0.2 0.4 0.6 0.8 1.0

-0.5

0.5

1.0


Types of Variables

The menu, including varying jet size

Distinguishable particles/tracks/subjets

multiplicity, 〈pT 〉, σpT, 〈kT 〉,

charge-weighted pT sum

Moments

mass, girth, broadeningangularitiesoptimal kernel2D: pull, planar flow

Subjet properties

Multiplicity for different algorithms and Rsub

First subjet’s pT , 2nd’s pT , etc.Each subjet’s massSplitting kT scale


Best Variables in Each Category

Signal eff0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

0

0.1

0.2

0.3

0.4

0.5

0.6

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0.8

0.9

1

LHC 200 : Background RejectionLHC 200 : Background Rejection

charged ct R=0.5

subjet ct Rsub=0.1

mass/Pt R=0.3

cos moment R=0.5

ang a=1 R=0.5

girth R=0.5

1st subjet pT

decluster kT

optimal R=1.0

pull η R=0.3

—pull— R=0.3

planar flow R=0.3


Best Variables in Each Category

Signal eff0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 11

10

210

310

LHC 200 : Background RejectionLHC 200 : Background Rejection charged ct R=0.5

subjet ct Rsub=0.1

mass/Pt R=0.3

cos moment R=0.5

ang a=1 R=0.5

girth R=0.5

1st subjet pT

decluster kT

optimal R=1.0

pull η R=0.3

—pull— R=0.3

planar flow R=0.3


Story So Far...

Keep only 40% of gluons while keeping 80% quarks

For multiple jets, these fractions exponentiate


Story So Far...



Where to put the Cut? 80%?How do we rank the variables?


Story So Far...




Always demand 80% quark, see how much glue you can reject?


Story So Far...




Always demand 80% quark, see how much glue you can reject?

Optimize S/B?

Optimize S/√B?


Cutting and S/√B

Cutting gives some signal efficiency and some background efficiency.


Cutting and S/√B


If you start with S signal events and B background events,

S

B→ Sǫs

Bǫb


Cutting and S/√B



S

B→ Sǫs

Bǫb

Improvement in statistical significance scales differently

σ =S√B

→ Sǫs√Bǫb

= σǫs√ǫb


Cutting and S/√B



S

B→ Sǫs

Bǫb

Improvement in statistical significance scales differently

σ =S√B

→ Sǫs√Bǫb

= σǫs√ǫb

Cutting can improve significance only to a point...


Significance Improvements for Best Single Variables

Signal eff0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

2.2

LHC 200 : SignificanceLHC 200 : Significance

charged ct R=0.5

subjet ct Rsub=0.1

mass/Pt R=0.3

cos moment R=0.5

ang a=1 R=0.5

girth R=0.5

1st subjet pT

decluster kT

optimal R=1.0

pull η R=0.3

—pull— R=0.3

planar flow R=0.3


Combining Variables: Ex. Girth vs Charged Count

0

50

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250

300

0 2 4 6 8 10 12 14 16 18 20 22 240

0.05

0.1

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QuarkQuark

Charged Count

Girth

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0.05

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GluonGluon

Charged Count

Girth


Combining Variables: Ex. Girth vs Charged Count

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300

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QuarkQuark

Charged Count

Girth

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GluonGluon

Charged Count

Girth

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Likelihood: q/(q + g)Likelihood: q/(q + g)

Charged Count

RadialM

om

ent


ROC Curves for Best Combinations

Signal eff0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

0

0.1

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1

LHC 0100 : Background RejectionLHC 0100 : Background Rejection

1: optimal kernel R=0.31: angularity a=+11: optimal kernel R=0.42: optimal and girth2: a=+1 and <kT >2: optimal and charged count

3:3:3:

444


Final Results: Best Pairs

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 11

10

210

310

20%eff

50 GeV, QT

p

80%eff

50 GeV, QT

p

20%eff

100 GeV, QT

p

80%eff

100 GeV, QT

p

20%eff

200 GeV, QT

p

80%eff

200 GeV, QT

p

20%eff

400 GeV, QT

p

80%eff

400 GeV, QT

p

20%eff

800 GeV, QT

p

80%eff

800 GeV, QT

p

20%eff

1600 GeV, QT

p

80%eff

1600 GeV, QT

p

Gluon Background RejectionGluon Background Rejection

Quark Signal EfficiencyJason Gallicchio (Harvard) Gluon Tagging and Quark & Gluon Samples 12 April 2011 36 / 70

Final Results: Best Pairs

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

0.5

1

1.5

2

2.5

3

3.5

4

20%eff

50 GeV, QT

p

80%eff

50 GeV, QT

p

20%eff

100 GeV, QT

p

80%eff

100 GeV, QT

p

20%eff

200 GeV, QT

p

80%eff

200 GeV, QT

p

20%eff

400 GeV, QT

p

80%eff

400 GeV, QT

p

20%eff

800 GeV, QT

p

80%eff

800 GeV, QT

p

20%eff

1600 GeV, QT

p

80%eff

1600 GeV, QT

p

Significance ImprovementSignificance Improvement

Quark Signal EfficiencyJason Gallicchio (Harvard) Gluon Tagging and Quark & Gluon Samples 12 April 2011 37 / 70

Summary of Gluon Tagging

Can reject 80% of gluons while keeping 80% quarksCan reject 95% of gluons while keeping 50% quarks




We can improve S/√B on quark-only signals by a factor of 2x-3x.





For 4-jets, all of these get raised to the 4th for glue-heavy background.Differences in these variables are worth verifying & calculating.






TODO:

Real Signals and Backgrounds (e.g. SUSY, WW → 4jet)






TODO:


Use tagger to measure Q/G fraction (e.g. W+jets)






TODO:



Encourage Atlas and CMS to squeeze out the most spatialresolution that they can.






TODO:



Encourage Atlas and CMS to squeeze out the most spatialresolution that they can.

Integrate these variables into FastJet or SpartyJet.


Outline


Part 2: The Gluon Tagger


Goal: High Purity with a high Cross SectionUse madgraph tree-level samplesFind kinematic variables to cut on2D combinations and/or Multivariate Techniques99% Quark purity from γ+2jets95% Gluon purity from 3jetsarXiv:1104.1175 [hep-ph] with Matt Schwartz


Detailed Multi-Jet Composition

50 100 200 400 800 1600

2 Jets

0%

100%

80%

60%

40%

20%

GG

QG

QQ

pT Cut on All Jets (GeV)50 100 200 400 800 1600

0%

3 Jets

100%

80%

60%

40%

20%

GGG

QGG

QQG

QQQ


Result of Parton Distribution Functions


X+2jet Composition

50 100 200 400 800 1600

QQ

QG

GG

0%

100%

80%

60%

40%

20%

γ+2jets

pT Cut on All Jets (GeV)50 100 200 400 800 1600

0%

100%

80%

60%

40%

20%

b+2jets

QQ

QG

GG



Starting Samples

50 100 200 400 800 1600

Chance EACH Jet is Quark

0%

100%

80%

60%

40%

20%

2j

Z/W+2j

Z/W+1j

γ+1j

3j

4j b+2j

bb+j

γ+2j

b+j



Cro

ss S

ection

(p

b)

10-6

10-3

1

103

106

50 100 200 400 800 1600

2j

2j1jZ+

2j1jW+

2j1jγ+

3j

4j

b+j

b+2j

bb+j

2j

Z+2jZ+j

W+2j

W+jγ+j

γ+2j

b+j

b+2j

bb+j

3j

4j


tt-

tt-

semi-leptonic

100%10-3

Cro

ssSec

tion

inpb

200 GeV Quark Purity

Quark Purity (zoom)

40% 50% 60% 70% 80% 90% 100

1

103

2jet

3jet hardest

harder softer

softerZ+2jet

γ+1jet

harder

softerW+2jet

harder

Z+1jet

W+1jet

γ+2jet

Quark Purification in γ+1jet

-2 -1 0 1 20

0.2

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Quark

Gluon

ηγ

200 250 300 350 4000

1

2

3

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5

6

Quark

Gluon

pjT

Kinematics are too similar to do much:

gq → γq: Quark signal

qq̄ → γg: Gluon background


Quark Purification in γ+2jet: Look at Softer Jet

2 2.5 3 3.5 4 4.5 50

0.5

1

1.5

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Quark

Gluon

∆Rγj1 (to harder jet)

0.5 1 1.5 2 2.5 3 3.5 4 4.50

1

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3

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Quark

Gluon

∆Rγj2 (to softer jet)

When the softer jet is quark, The photon is often radiated off of it,rather then the harder jet.



-2 -1 0 1 20

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Quark

Gluon

ηγ

-2 -1 0 1 20

0.5

1

1.5

2

2.5Quark

Gluon

ηj1

Other useful kinematics (?)



2D version of the same

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Quark

η j1

ηγ0

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Gluon

η j1

ηγ



2D version of the same

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η j1

ηγ0

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Gluon

η j1

ηγ0

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Likelihood

η j1

ηγ



Approximating the Likelihood Contours with f(x, y) = xy

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Likelihood

η j1

ηγ -2 -1 0 1 2

-2

-1

0

1

2



Approximating the Likelihood Contours with f(x, y) = xy

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Likelihood

η j1

ηγ -2 -1 0 1 2

-2

-1

0

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-4 -2 0 2 4 60

2

4

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10Quark

Gluon

ηγ ηj1



Do it again to find an even better combination

-4 -2 0 2 4 6 8 100

1

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Quark

Gluon

ηγ ηj1 +∆Rγj2


Automating the process with Boosted Decision Trees

Some totally crazy illustrative example from my Higgs+Z work:

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∆θb1,ℓ1

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LikelihoodLikelihood


Automating the process with Boosted Decision Trees


Do Not Fear Boosted Decision Trees

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6

BDT 8BDT 8

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

-3 -2 -1 0 1 2 3

-4

-2

0

2

4

6

BDT 32BDT 32

-0.6

-0.4

-0.2

0

0.2

0.4

-3 -2 -1 0 1 2 3

-4

-2

0

2

4

6

BDT 64BDT 64

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

-3 -2 -1 0 1 2 3

-4

-2

0

2

4

6

BDT 256BDT 256

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

-3 -2 -1 0 1 2 3

-4

-2

0

2

4

6

LikelihoodLikelihood


100%10

-3

Cro

ssSecti

onin

pb

200 GeV Quark Purity

Quark Purity

40% 50% 60% 70% 80% 90%

1

103

2jet

3jet hardest

γ+2jet harderγ+2jet softer

softerZ+2jet

γ+1jetharder

Quark Purity

70% 80% 90% 100%

1

103

10-3

10-6

Cro

ssSec

tion

inpb

Quark Purity for Different pT

50 γjj

100 γjj200 γjj

400 γjj

800 γjj

1600 γjj

50 γj

100 γj

200 γj

400 γj

800 γj

1600 γj

10-1

Cross

Sectionin

pb

200 GeV Gluon Purity

Gluon Purity

2jet

b+jet

3jet softest

3jet hardest

b+2jet hardest b+2jet softest

3jet mid

50% 60% 70% 80% 90% 100%

1

101

102

103

104

0% 20% 40% 60% 80% 100%

1

103

106

10-3

10-6

Cro

ssSec

tion

inpb

Best Samples for Gluon Purity

Gluon Purity

50 jj

100 jj

200 jj

400 jj

800 jj

1600 jj

50 jjj100 jjj

200 jjj

400 jjj

800 jjj

50 bjj

100 bjj

200 bjj

400 bjj

800 bjj

Summary of Finding Samples

Quark samples at 99% purity for γ+jet

Gluon samples at 90%-95% purity for 3jets





Gluon samples at 95%-99% purity for b+2jets with perfectB-Tagging and B-Anti-Tagging





Gluon samples at 95%-99% purity for b+2jets with perfectB-Tagging and B-Anti-Tagging

Now go forth and use these tools for good.... Thanks!


Backup

In case waving my hands proves insufficient ...


History

Number of charged tracks

Jet Shape (shown)

LEP e+e− → Zg → bb̄g


Event Generation

180 185 190 195 200 205 210 215 2200

0.5

1

1.5

2

2.5

3

3.5

ak05_Pt : ak05_Pt > 180 && ak05_Pt < 220

Entries 167796

Mean 196.8

RMS 11.29

ak05_Pt : ak05_Pt > 180 && ak05_Pt < 220

100 150 200 250 3000

2

4

6

8

10

hard_Pt : ak05_Pt > 180 && ak05_Pt < 220

Entries 167796

Mean 188.9

RMS 28.65

hard_Pt : ak05_Pt > 180 && ak05_Pt < 220

Jet pT : anti-kT R=0.5 Hard Parton pT


Moment of Inertia / Covariance / Eccentricity

Covariance Tensor: C =∑

i∈jet

piTpjetT

(

∆ηi∆ηi ∆ηi∆φi

∆φi∆ηi ∆φi∆φi

)


Moment of Inertia / Covariance / Eccentricity

Covariance Tensor: C =∑

i∈jet

piTpjetT

(

∆ηi∆ηi ∆ηi∆φi

∆φi∆ηi ∆φi∆φi

)

Combination of Eigenvalues

Girth: g =√a2 + b2

Determinant: det = a · bRatio: b/a

Eccentricity: ǫ =√a2 − b2

Planar Flow: pf = 4ab(a+b)2

Orientation: θ

Not useful for Q vs G: first emission sets this shape, and has similar2-body kinematics.


Subjets – Smaller is Better

Subjet Algorithm: anti-kT , CA, kTSubjet Size: Darkest is Rsub = 0.1, lightest Rsub = Rjet

R jet

60

70

80

90

100

Grejection

components_jet0_ak_sub__count

(Background Rejection at 50% Quark vs Initial Jet Size)Jason Gallicchio (Harvard) Gluon Tagging and Quark & Gluon Samples 12 April 2011 63 / 70

Explosion of Variables

Explosion of Variables

Different Jet sizes (R = 0.1, 0.2, 0.4, 0.7, 1.0, 1.4, ...)

Different Jet definitions (anti-kt, kt, CA, SisCone)

Different Generators: Pythia vs Herwig

Different Samples: Dijet vs γ+jet vs 8-Jets

Different Subjet sizes and types

Different Powers in the various moments

Charged Tracks or Calorimeter deposits?

And different variables are better for different Jet pT ranges.


Jet Broadening

Jet Broadening similar to linear moment for small-angles: kT ≈ pT r

Bjet =

∑

i |~pi × n̂jet|∑

i |~pi|=

∑

i |~kT i|∑

i |~pi|

0 0.05 0.1 0.15 0.2 0.250

2

4

6

8

10

12

14

16

components_jet0_ak05_particles_broadening Q 0050

G 0050

Q 0100

G 0100

Q 0200

G 0200

Q 0400

G 0400

Q 0800

G 0800

Q 1600

G 1600

components_jet0_ak05_particles_broadening

-12 -10 -8 -6 -4 -2 00

1

2

3

4

5

6

7

8

9

components_jet0_ak05_particles_broadening_beta Q 0050

G 0050

Q 0100

G 0100

Q 0200

G 0200

Q 0400

G 0400

Q 0800

G 0800

Q 1600

G 1600

components_jet0_ak05_particles_broadening_beta


Charge Weighted by pT

-100 -50 0 50 1000

0.5

1

1.5

2

2.5

3

3.5

4

4.5

50GeV50GeV

-200 -150 -100 -50 0 50 100 150 2000

0.5

1

1.5

2

2.5

200GeV200GeV

-300 -200 -100 0 100 200 3000

0.5

1

1.5

2

2.5

400GeV400GeV

-1000 -500 0 500 10000

0.5

1

1.5

2

2.5

1600GeV1600GeV


Compare to ATLAS B-Taggnig


0% 20% 40% 60% 80% 100%

1

103

106

10-3

10-6

CrossSectioninpb

Softest Trijet Gluon Purity

Gluon Purity

50 GeV

100 GeV

200 GeV

400 GeV

800 GeV

Gluon Purification: Lesson about Harsh Cuts

-2 -1 0 1 20

0.5

1

1.5

2

2.5

Quark

Gluon

ηj3


Gluon Purification: Lesson about Harsh Cuts

-2 -1 0 1 20

0.5

1

1.5

2

2.5

Quark

Gluon

ηj3

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.5 1 1.5 2 2.50

0.5

1

1.5

2

2.5

3

3.5

4

4.5

Likelihood

|ηj3 |

|ηj1−

ηj2|

-4 -3 -2 -1 0 1 20

0.5

1

1.5

2

2.5

3

3.5

4

Quark

Gluon

|ηj3 | − |ηj1 − ηj2 |


0% 20% 40% 60% 80% 100%

1

103

106

10-3

10-6

Cro

ssSec

tion

inpb

Trijet Sample with Different Kinematic Cuts

Gluon Purity

BDT

800 jjj

400 jjj

200 jjj

100 jjj

50 jjj

η j3|η

3

η1 −η

2

j

j

j |

|−|

Documents

Gluon Tagging and Quark & Gluon Samples