Optimization of the reconstruction of high energetic τ leptons ...morgenst/DiplomaThesis...standard...

Optimization of the reconstruction of high energeticτ leptons at ATLAS

Marcus Morgenstern

TU Dresden

January 13, 2011Version_05_21

Version_05_20

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/01234/54678994:

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Marcus Morgenstern (TU Dresden) Optimization of the τ reconstruction January 13, 2011 1 / 18

Outline

1 Introduction

2 Tau reconstruction

3 Tau identification

4 Summary/Outlook


The ATLAS detector

Figure: ATLAS detector overview


Standard Model of Particle Physics


Tau characteristics

mτ ∼ 1.7 GeVcτ = 87µm

Hadronic decays are wellcollimated collection of chargedand neutral pions/kaons

Mostly 1 or 3 charged tracks

Leading hadron reproduces τdirection well

τ decays well understood

Provides an excellent probe for ’New Physics’ ...

... if contribution of QCD background is well understood


Tau reconstruction

candidate [GeV]τ of TE0 10 20 30 40 50 60 70 80 90 100

Fra

ctio

n of

can

dida

tes

0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0 10 20 30 40 50 60 70 80 90 1000

0.005

0.01

0.015

0.02

0.025

0.03

0.035

calo seeded only

track seeded only

both seeds

ATLAS Work in Progress

τ variables calculated for static cone size, e.g. number of tracks(∆R =

p(∆η)2 + (∆φ)2 = 0.2)

not optimal for τs from heavy particle decays (Lorentz Boost)


Dynamic cone size for tau reconstruction

Lorentz Boost: Opening angle ∼ 1γ ⇒ ∆R ∼1

pT

∆R(pτT ) determined from MC generator level→ cone size contains 90% of all τ decay products

[GeV]leadTrkT

p0 50 100 150 200 250 300

R∆

0

0.02

0.04

0.06

0.08

0.1

0.12

/ ndf 2χ 0.000106573 / 27

Prob 1

p0 0.0293881± 0.840474

/ ndf 2χ 0.000106573 / 27

Prob 1

p0 0.0293881± 0.840474

MC simulation

leadTrkT

R = 0.04 + 0.84/p∆

leadTrkT

R = a/p∆fit:


add off-set due toresolution effects


Performance estimators

Signal efficiency

�sig =Number of reconstructed τs

Number of generated τs

QCD background rejection

rbkg = 1−Number of reconstructed τs

Number of generated jets

reconstructed objects have to match generated particle

acceptance cuts: ET > 10 GeV, |η| < 2.5generated jets using AntiKt algorithm with R = 0.4


http://iopscience.iop.org/1126-6708/2008/04/063

Performance estimators for 1-prong τs

[GeV]TE0 20 40 60 80 100

effic

ienc

y

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

[GeV]TE0 20 40 60 80 100

effic

ienc

y

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

R off-set = 0.04∆

R off-set = 0.08∆

R off-set = 0.12∆

Standard tauRec

1 prong candidates


(a) signal efficiency

[GeV]TE0 20 40 60 80 100

reje

ctio

n

0.75

0.8

0.85

0.9

0.95

1

1.05

1.1

1.15

[GeV]TE0 20 40 60 80 100

reje

ctio

n

0.75

0.8

0.85

0.9

0.95

1

1.05

1.1

1.15R off-set = 0.04∆

R off-set = 0.08∆

R off-set = 0.12∆

Standard tauRec

1 prong candidates


(b) background rejection

Conclusion

signal sample: Z → ττ , A → ττbackground sample: QCD dijet

Isolation criteria are essential for high pT τs→ further optimization of tau identification required


Tau identification

isolation criteria essential to gain performance

optimization using multivariate techniques

study of additional variables → improve tau identification by newvariables

reference: standard tau identification [ATLAS-CONF-2010-086]

use: cuts, Projective Likelihood, Fisher linear discriminant, BoostedDecision Trees

simple cuts uses only 3 variables, while LLH/BDT use 7/8 variables

optimize tau identification in different pT bins ⇒ Lorentz Boostindependent optimization for 1-prong/3-prong τs

only both-seeded τs considered


http://cdsweb.cern.ch/record/1298857

ATLAS data taking

pp collisions @√

s = 7TeVATLAS started data taking inNovember 2009 @√

s = 900GeVin March 2010 switched to√

s = 7TeVdata used in this analysis from7 TeV run


New identification variables - centrality fraction fcore

coref0 0.2 0.4 0.6 0.8 1 1.2

Nor

mal

ised

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

-1dtL = 607.6 nb∫

Signal

Background

Data

Definition

fcore =

P∆Ri

New identification var. - relative track pT over track pT

isoltrk,Relf

0 0.05 0.1 0.15 0.2 0.25

Nor

mal

ised

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

-1dtL = 607.6 nb∫

Signal

Background

Data

Definition

f isoltrk,Rel =

P∆Ri

New ID variables for both seeded 1-prong τs

emR0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4

Nor

mal

ised

0

0.02

0.04

0.06

0.08

0.1

-1dtL = 607.6 nb∫

Signal

Background

Data

#IntdtL = pb^{-1}

coref0 0.2 0.4 0.6 0.8 1 1.2

Nor

mal

ised

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

-1dtL = 607.6 nb∫

Signal

Background

Data

)Norm

log(I-5 -4 -3 -2 -1 0 1 2 3 4 5

Nor

mal

ised

0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0.04

0.045

-1dtL = 607.6 nb∫

Signal

Background

Data

coretrkf

0 0.5 1 1.5 2 2.5

Nor

mal

ised

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

-1dtL = 607.6 nb∫

SignalBackground

Data

isoltrk,Relf

0 0.05 0.1 0.15 0.2 0.25

Nor

mal

ised

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

-1dtL = 607.6 nb∫

Signal

Background

Data


Background rejection vs signal efficiency, 1-prong τs

Signal efficiency0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Bac

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1

< 30 GeVT

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1-prong

Likelihoodstandard tauRec, standard tauID

R offset = 0.08, standard tauID∆standard tauRec, new tauID

R offset = 0.08, new tauID∆




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3-prong

BDTstandard tauRec, standard tauID




Summary/Outlook

signal reconstruction efficiency is slightly increasing for 1-prong tausdue to better n-prong reconstruction

but collecting more QCD background ⇒ lower background rejectionhigh-pT : QCD jets looks like taus

new identification variables show better performance compared tostandard variables for cut based and all multivariate tau identificationmethods

future task: understand discrepancies between Monte Carlopredictions and data


Backup


Physics with tau leptons in many areas

Standard ModelI Measurement of W/Z production cross sectionI Discovery of Higgs bosons in H → ττ final states

Minimal Supersymmetric Standard Model (MSSM)I h/H/A → ττ excellent discovery potentialI Searches for charged Higgs bosons: H± → τν

Exotic scenariosI E.g. searches for heavy gauge bosons


Tau reconstruction

Both seeded

Use good quality track (pT > 6 GeV)as seed

Candidates with ≤ 8 tracks(pT > 1 GeV) in∆R =

p(∆η)2 + (∆φ)2 < 0.2

Reconstruct η, φ of τ using pTweighting of tracks

Charge consistency check

Find matching cone jet with opening∆R = 0.4 (ET > 10 GeV, |η| < 2.5)as calo seed

ET using cells from calo seed

Reconstruct π0 subclusters + Energyflow algorithm


AntiKt jet algorithm

jet clustering algorithmperformed in inverse momentumspace

infrared/collinear safe

Distance parameter

dij = min(k2pti , k

2ptj )

∆2ijR2

(3)

with ∆2ij = (yi − yj)2 + (φi − φj)2


Performance estimators for 3-prong τs

[GeV]TE0 20 40 60 80 100

effic

ienc

y

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

[GeV]TE0 20 40 60 80 100

effic

ienc

y

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

R off-set = 0.04∆

R off-set = 0.08∆

R off-set = 0.12∆

Standard tauRec

3 prong candidates


(e) signal efficiency

[GeV]TE0 20 40 60 80 100

reje

ctio

n

0.75

0.8

0.85

0.9

0.95

1

1.05

1.1

1.15

[GeV]TE0 20 40 60 80 100

reje

ctio

n

0.75

0.8

0.85

0.9

0.95

1

1.05

1.1

1.15

R off-set = 0.04∆

R off-set = 0.08∆

R off-set = 0.12∆

Standard tauRec

3 prong candidates


(f) background rejection


Boosted Decision Trees

decision trees are well known powerfulmethod, but unstable ⇒ use boostingfor stabilisation

Boosting

start with unweighted events

misclassified event gets weight

second tree is built using new weights

typically build some thousands of trees

Figure: Schematic of a decision tree

calculate scores on which one cuts as follows:

score = 1: event lands in signal leaf

score = -1: event lands in background leaf


Projective likelihood

based on model building out of pdfs

likelihood:

LS,(B) =nvarsYk=0

pS(B),k(xk(i)) (4)

likelihood for being of signal type (for event i):

yL(i) =LS(i)

LS(i) + LB(i)(5)

performs well if correlations are weak


Fisher linear discriminant

linear model which projects data onhyperplane of best separation

separation measured by distingushingmean values under consideration ofsmall variances

unable to handle non-linearcorrelations ⇒ needs decorrelation


New ID variables for both seeded 3-prong τs

)Norm

log(I-5 -4 -3 -2 -1 0 1 2 3 4 5

Nor

mal

ised

0

0.02

0.04

0.06

0.08

0.1-1

dtL = 607.6 nb∫

-1dtL = 607.6 nb∫

SignalBackgroundData

emR0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4

Nor

mal

ised

0

0.02

0.04

0.06

0.08

0.1

0.12

-1dtL = 607.6 nb∫


#IntdtL = pb^{-1}

coretrkf

0 0.5 1 1.5 2 2.5

Nor

mal

ised

0

0.02

0.04

0.06

0.08

0.1

-1dtL = 607.6 nb∫


coretrk,Relf

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1

Nor

mal

ised

0

0.02

0.04

0.06

0.08

0.1

-1dtL = 607.6 nb∫



)coreRel

log(f-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0

Nor

mal

ised

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

-1dtL = 607.6 nb∫



isoltrk NΣ

0 2 4 6 8 10 12 14 16 18 20

Nor

mal

ised

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

-1dtL = 607.6 nb∫


[GeV]trkM0 1 2 3 4 5 6 7 8 9 10

Nor

mal

ised

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0.2

0.22

-1dtL = 607.6 nb∫


[GeV]topoM0 2 4 6 8 10 12

Nor

mal

ised

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

-1dtL = 607.6 nb∫


τtrkW

0 0.001 0.002 0.003 0.004 0.005 0.006 0.007 0.008N

orm

alis

ed0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

-1dtL = 607.6 nb∫


(a) signal efficiencyMarcus Morgenstern (TU Dresden) Optimization of the τ reconstruction January 13, 2011 27 / 18



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Cutsstandard tauRec, standard tauID




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Fisherstandard tauRec, standard tauID






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IntroductionTau reconstructionTau identificationSummary/OutlookTau Reconstruction and Identification

Optimization of the reconstruction of high energetic τ leptons ...morgenst/DiplomaThesis...standard...

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Transcript of Optimization of the reconstruction of high energetic τ leptons ...morgenst/DiplomaThesis...standard...