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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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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
Marcus Morgenstern (TU Dresden) Optimization of the τ reconstruction January 13, 2011 2 / 18
The ATLAS detector
Figure: ATLAS detector overview
Marcus Morgenstern (TU Dresden) Optimization of the τ reconstruction January 13, 2011 3 / 18
Standard Model of Particle Physics
Marcus Morgenstern (TU Dresden) Optimization of the τ reconstruction January 13, 2011 4 / 18
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
Marcus Morgenstern (TU Dresden) Optimization of the τ reconstruction January 13, 2011 5 / 18
Marcus Morgenstern (TU Dresden) Optimization of the τ reconstruction January 13, 2011 6 / 18
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)
Marcus Morgenstern (TU Dresden) Optimization of the τ reconstruction January 13, 2011 7 / 18
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:
ATLAS Work in Progress
add off-set due toresolution effects
Marcus Morgenstern (TU Dresden) Optimization of the τ reconstruction January 13, 2011 8 / 18
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
Marcus Morgenstern (TU Dresden) Optimization of the τ reconstruction January 13, 2011 9 / 18
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
ATLAS Work in Progress
(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
ATLAS Work in Progress
(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
Marcus Morgenstern (TU Dresden) Optimization of the τ reconstruction January 13, 2011 10 / 18
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
Marcus Morgenstern (TU Dresden) Optimization of the τ reconstruction January 13, 2011 11 / 18
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
Marcus Morgenstern (TU Dresden) Optimization of the τ reconstruction January 13, 2011 12 / 18
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
Marcus Morgenstern (TU Dresden) Optimization of the τ reconstruction January 13, 2011 15 / 18
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
kgro
und
reje
ctio
n
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
< 30 GeVT
p
1-prong
Likelihoodstandard tauRec, standard tauID
R offset = 0.08, standard tauID∆standard tauRec, new tauID
R offset = 0.08, new tauID∆
Marcus Morgenstern (TU Dresden) Optimization of the τ reconstruction January 13, 2011 16 / 18
Background rejection vs signal efficiency, 3-prong τs
Signal efficiency0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Bac
kgro
und
reje
ctio
n
0.4
0.5
0.6
0.7
0.8
0.9
1
< 30 GeVT
p
3-prong
BDTstandard tauRec, standard tauID
R offset = 0.08, standard tauID∆standard tauRec, new tauID
R offset = 0.08, new tauID∆
Marcus Morgenstern (TU Dresden) Optimization of the τ reconstruction January 13, 2011 17 / 18
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
Marcus Morgenstern (TU Dresden) Optimization of the τ reconstruction January 13, 2011 18 / 18
Backup
Marcus Morgenstern (TU Dresden) Optimization of the τ reconstruction January 13, 2011 19 / 18
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
Marcus Morgenstern (TU Dresden) Optimization of the τ reconstruction January 13, 2011 20 / 18
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
Marcus Morgenstern (TU Dresden) Optimization of the τ reconstruction January 13, 2011 21 / 18
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
Marcus Morgenstern (TU Dresden) Optimization of the τ reconstruction January 13, 2011 22 / 18
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
ATLAS Work in Progress
(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
ATLAS Work in Progress
(f) background rejection
Marcus Morgenstern (TU Dresden) Optimization of the τ reconstruction January 13, 2011 23 / 18
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
Marcus Morgenstern (TU Dresden) Optimization of the τ reconstruction January 13, 2011 24 / 18
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
Marcus Morgenstern (TU Dresden) Optimization of the τ reconstruction January 13, 2011 25 / 18
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
Marcus Morgenstern (TU Dresden) Optimization of the τ reconstruction January 13, 2011 26 / 18
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∫
SignalBackgroundData
#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∫
SignalBackgroundData
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∫
SignalBackgroundData
SignalBackgroundData
)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∫
SignalBackgroundData
SignalBackgroundData
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∫
SignalBackgroundData
[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∫
SignalBackgroundData
[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∫
SignalBackgroundData
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∫
SignalBackgroundData
(a) signal efficiencyMarcus Morgenstern (TU Dresden) Optimization of the τ reconstruction January 13, 2011 27 / 18
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
kgro
und
reje
ctio
n
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
< 30 GeVT
p
1-prong
Cutsstandard tauRec, standard tauID
R offset = 0.08, standard tauID∆standard tauRec, new tauID
R offset = 0.08, new tauID∆
Signal efficiency0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Bac
kgro
und
reje
ctio
n
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
< 30 GeVT
p
1-prong
Likelihoodstandard tauRec, standard tauID
R offset = 0.08, standard tauID∆standard tauRec, new tauID
R offset = 0.08, new tauID∆
Signal efficiency0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Bac
kgro
und
reje
ctio
n
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
< 30 GeVT
p
1-prong
BDTstandard tauRec, standard tauID
R offset = 0.08, standard tauID∆standard tauRec, new tauID
R offset = 0.08, new tauID∆
Signal efficiency0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Bac
kgro
und
reje
ctio
n
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
< 30 GeVT
p
1-prong
Fisherstandard tauRec, standard tauID
R offset = 0.08, standard tauID∆standard tauRec, new tauID
R offset = 0.08, new tauID∆
Marcus Morgenstern (TU Dresden) Optimization of the τ reconstruction January 13, 2011 28 / 18
Background rejection vs signal efficiency, 3-prong τs
Signal efficiency0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Bac
kgro
und
reje
ctio
n
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
< 30 GeVT
p
3-prong
Cutsstandard tauRec, standard tauID
R offset = 0.08, standard tauID∆standard tauRec, new tauID
R offset = 0.08, new tauID∆
Signal efficiency0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Bac
kgro
und
reje
ctio
n
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
< 30 GeVT
p
3-prong
Likelihoodstandard tauRec, standard tauID
R offset = 0.08, standard tauID∆standard tauRec, new tauID
R offset = 0.08, new tauID∆
Signal efficiency0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Bac
kgro
und
reje
ctio
n
0.4
0.5
0.6
0.7
0.8
0.9
1
< 30 GeVT
p
3-prong
BDTstandard tauRec, standard tauID
R offset = 0.08, standard tauID∆standard tauRec, new tauID
R offset = 0.08, new tauID∆
Signal efficiency0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Bac
kgro
und
reje
ctio
n
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
< 30 GeVT
p
3-prong
Fisherstandard tauRec, standard tauID
R offset = 0.08, standard tauID∆standard tauRec, new tauID
R offset = 0.08, new tauID∆
Marcus Morgenstern (TU Dresden) Optimization of the τ reconstruction January 13, 2011 29 / 18
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
kgro
und
reje
ctio
n
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
< 60 GeVT
30 GeV < p
1-prong
Cutsstandard tauRec, standard tauID
R offset = 0.08, standard tauID∆standard tauRec, new tauID
R offset = 0.08, new tauID∆
Signal efficiency0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Bac
kgro
und
reje
ctio
n
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
< 60 GeVT
30 GeV < p
1-prong
Likelihoodstandard tauRec, standard tauID
R offset = 0.08, standard tauID∆standard tauRec, new tauID
R offset = 0.08, new tauID∆
Signal efficiency0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Bac
kgro
und
reje
ctio
n
0.4
0.5
0.6
0.7
0.8
0.9
1
< 60 GeVT
30 GeV < p
1-prong
BDTstandard tauRec, standard tauID
R offset = 0.08, standard tauID∆standard tauRec, new tauID
R offset = 0.08, new tauID∆
Signal efficiency0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Bac
kgro
und
reje
ctio
n
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
< 60 GeVT
30 GeV < p
1-prong
Fisherstandard tauRec, standard tauID
R offset = 0.08, standard tauID∆standard tauRec, new tauID
R offset = 0.08, new tauID∆
Marcus Morgenstern (TU Dresden) Optimization of the τ reconstruction January 13, 2011 30 / 18
Background rejection vs signal efficiency, 3-prong τs
Signal efficiency0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Bac
kgro
und
reje
ctio
n
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
< 60 GeVT
30 GeV < p
3-prong
Cutsstandard tauRec, standard tauID
R offset = 0.08, standard tauID∆standard tauRec, new tauID
R offset = 0.08, new tauID∆
Signal efficiency0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Bac
kgro
und
reje
ctio
n
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
< 60 GeVT
30 GeV < p
3-prong
Likelihoodstandard tauRec, standard tauID
R offset = 0.08, standard tauID∆standard tauRec, new tauID
R offset = 0.08, new tauID∆
Signal efficiency0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Bac
kgro
und
reje
ctio
n
0.5
0.6
0.7
0.8
0.9
1
< 60 GeVT
30 GeV < p
3-prong
BDTstandard tauRec, standard tauID
R offset = 0.08, standard tauID∆standard tauRec, new tauID
R offset = 0.08, new tauID∆
Signal efficiency0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Bac
kgro
und
reje
ctio
n
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
< 60 GeVT
30 GeV < p
3-prong
Fisherstandard tauRec, standard tauID
R offset = 0.08, standard tauID∆standard tauRec, new tauID
R offset = 0.08, new tauID∆
Marcus Morgenstern (TU Dresden) Optimization of the τ reconstruction January 13, 2011 31 / 18
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
kgro
und
reje
ctio
n
0
0.2
0.4
0.6
0.8
1
< 100 GeVT
60 GeV < p
1-prong
Cutsstandard tauRec, standard tauID
R offset = 0.08, standard tauID∆standard tauRec, new tauID
R offset = 0.08, new tauID∆
Signal efficiency0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Bac
kgro
und
reje
ctio
n
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
< 100 GeVT
60 GeV < p
1-prong
Likelihoodstandard tauRec, standard tauID
R offset = 0.08, standard tauID∆standard tauRec, new tauID
R offset = 0.08, new tauID∆
Signal efficiency0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Bac
kgro
und
reje
ctio
n
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
< 100 GeVT
60 GeV < p
1-prong
BDTstandard tauRec, standard tauID
R offset = 0.08, standard tauID∆standard tauRec, new tauID
R offset = 0.08, new tauID∆
Signal efficiency0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Bac
kgro
und
reje
ctio
n
0
0.2
0.4
0.6
0.8
1
< 100 GeVT
60 GeV < p
1-prong
Fisherstandard tauRec, standard tauID
R offset = 0.08, standard tauID∆standard tauRec, new tauID
R offset = 0.08, new tauID∆
Marcus Morgenstern (TU Dresden) Optimization of the τ reconstruction January 13, 2011 32 / 18
Background rejection vs signal efficiency, 3-prong τs
Signal efficiency0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Bac
kgro
und
reje
ctio
n
0
0.2
0.4
0.6
0.8
1
< 100 GeVT
60 GeV < p
3-prong
Cutsstandard tauRec, standard tauID
R offset = 0.08, standard tauID∆standard tauRec, new tauID
R offset = 0.08, new tauID∆
Signal efficiency0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Bac
kgro
und
reje
ctio
n
0.2
0.4
0.6
0.8
1
< 100 GeVT
60 GeV < p
3-prong
Likelihoodstandard tauRec, standard tauID
R offset = 0.08, standard tauID∆standard tauRec, new tauID
R offset = 0.08, new tauID∆
Signal efficiency0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Bac
kgro
und
reje
ctio
n
0
0.2
0.4
0.6
0.8
1
< 100 GeVT
60 GeV < p
3-prong
BDTstandard tauRec, standard tauID
R offset = 0.08, standard tauID∆standard tauRec, new tauID
R offset = 0.08, new tauID∆
Signal efficiency0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Bac
kgro
und
reje
ctio
n
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
< 100 GeVT
60 GeV < p
3-prong
Fisherstandard tauRec, standard tauID
R offset = 0.08, standard tauID∆standard tauRec, new tauID
R offset = 0.08, new tauID∆
Marcus Morgenstern (TU Dresden) Optimization of the τ reconstruction January 13, 2011 33 / 18
IntroductionTau reconstructionTau identificationSummary/OutlookTau Reconstruction and Identification