-ID at ATLAS
Consequences of TeV4LHC

Michael Heldmann

University of Freiburg

- TauID at ATLAS

- TeV4LHC

Tau Identificaton

How can one identify -leptons ?

most important decay modes

Leptonical decay modes

→ + e + e (17.4%)

→ +  +  (17.8%)

Hadronical decay modes

1 prong

→ + c (11.0%)

→ + c + × 0 (36.2%)

3 prong

→ + 3 c + × 0 (15.2%)

→ s are colimated calorimeter objects with one or three associated tracks, leptonic decay difficult to distinguish from prompt leptons → concentrate on hadronical decay

1 track

only difference

to prompt leptons:

impact parameter

1 track, impact parameter

shower shape, energy sharing

find the photon cluster

3 track, impact parameters,

secondary vertex

TauID(cluster based) at ATLAS

There are two complementary algorithms available: one starts from calorimeter clusters, one starts from tracks.

tauRec starts from clusters found by a sliding window algorithm

The following quantities are used to discriminate s against jets

Rem= Radius of the cluster in the em-calorimeter R=0.4

ET12 = Fraction of the transverse Energy between R=0.2 and R=0.1 around the center of the cluster

Ntr = Number of Tracks within 0.3, pT>2GeV

Nem / Nstrip= Number of Hits in EM calo/-Strip, ET>200MeV

ET,width,strip = Width in the -Strip

ET,em/ET, Charge, ET,had/pT(tracks)

Lifetime Signed Impact Parameter

for 3 prong decays: secondary vertex

TauID(cluster based) at ATLAS

All variables are then combined into a LikelihoodRatio

preselection cut before the Llh: 1≤NTr≤3

3 discreet variables, Ntr,Nstrip,Charge, Llh directly from histograms

4 continous variables, Rem, ET12, ET,width,strip, A0/σ, fitted with arbitrary functions (normaly gaus*polynom )

10 pT bins from 15 to 600 GeV, no eta bins so far

Tau identification – energy flow approach

Track-based and Energy Flow approach

for low mass Higgs (soft taus ET< 70 GeV) , in this range superior tracker resolution with respect to calorimeter

identify a leading hadronic track with pT > 9GeV , associate cells from calorimeter

use Energy Flow for energy calibration (using Tracker + Calo)

compute set of discriminating variables

variable cut/multi-variate classification technique for tau-id

available are cut based, neural network and PDRS

good results for energy determination of taus

slides: courtesy of E.Richter-Was

and A.Kaczmarska

Tau identification – energy flow approach

various ET bins

various eta bins

For ε(τ)=30%,

15< pT< 60, |eta|< 2.5

Rej(QCD jets) = 600 - 1 000

in both algorithms

many new elements under

investigation like e.g.

- explicit reconstruction of + 0

- tau / electron separation

- tau / muon separation

in addition, analysis tailored

tau-id exists for VBF

h→

for ATLAS also the question of how to treat taus in the fast

simulation is under discussions

-> important for charged higgs searches because of QCD bgd

TeV4LHC, Motivation

For charged Higgs searches a high performant reconstruction and identification of hadronically decayed tau leptons is essential for the H+tau nu channel

At the moment we have to rely on Monte Carlo simulations to predict this performance

How much can we trust them ? Is there a way to understand more about these uncertainties ?

Idea : learn from the experience at the Tevatron (from D0 in our case)

Question : can we transfer this knowledge to ATLAS ?

Collaboration with Ingo Torchiani from D0 to investigate these questions

Strategy for TeV4LHC

The goal is to understand what difference we can expect from the results in MC to performance with real data

Goal was to understand these steps

D0 Algorithm on D0 data → D0 Algorithm on D0 MC → D0 Algorithm on ATLAS MC →ATLAS Algorithm on ATLAS MC

Steps on the way to establish the chain

select signal sample in D0 data

select background sample in D0 data

study preselection (called reconstruction step in ATLAS)

study TauID in D0 with few key variables

use the same few key variables in ATLAS

compare, if similar, we should expect a similar agreement between between Monte Carlo and data

W  selection in D0 data

D0: signal sample is Z , background is W 

for the background only 100pb-1 used up to now, x4 available

W  is used because

it is a relevant background for many studies

it allows to obtain unbiased jets (using a single  trigger) down to rather low pT

preselection

pT() > 25 GeV, |eta()| < 1.5

pT(jet) > 15 GeV, |eta(jet)| < 1.5

MET > 20 GeV, mT > 30 GeV

m(, track) < 60 GeV

(MET, jet) > 0.4

ATLAS: signal sample is Z , background is W 

same preselection, sample was produced specifically for TeV4LHC, using PYTHIA inclusive W like D0

tight cuts to

obtain clean signal

W  jets

D0

pT,jet

ATLAS

for this study we are interested in jets

the pT spectrum is very important since all shape properties depend very much on pT

similar comditions at D0 and ATLAS can be observed since we are looking at a radiated jets

 we are not facing very different kinematics here

 jets should possess similar shape properties

Preselection

Preselection means the step from jet  -candidate

D0 and ATLAS use both a seperate cluster-finder for the s

at least 1 track is required

question 1: are low multiplicity jets well described in the MC ?

question: is the 1-3-track fraction correctly described in the MC ?

even low multiplicity region is in good agreement between MC and data

This is after a common efficiency correction for track reconstruction

Also ATLAS Monte Carlo shows a very similar environment

ATLAS

Input variables for a "SlimID"

Strategy is to build a TauID ("SlimID") based on three main D0 input variables

Profile = (ET(Tower1) + ET(Tower2))/ET(0.5), Towers defined on  x =0.1x0.1 granularity

Isolation = ( ET(0.5) + ET(0.3) ) / ET(0.3)

TrackIsolation = pT( tracks) / pT(all tracks)

-Tracks are defined in the following way

only tracks with R < 0.5, pT > 1.5 GeV are considered, and sorted in pT

the first track is always a  track, the second/third tracks are  tracks if their invariant mass together < 1.1 / 1.7 GeV

side remark : all D0 variables were implemented in ATLAS, to investigate their performance

Comparison of input variables

D0 and ATLAS show again nice agreement for the input variables

TrackIsolation shows biggest difference  comes from the tecnical drawback that at ATLAS there are only tracks > 1 GeV readily available

variables remarkably similar  we don't probe a very different phase space region in D0 and ATLAS of the jet shape

ATLAS

ATLAS

ATLAS

D0

D0

D0

D0

tracks > 200 MeV

tracks > 1 GeV

Results

SlimID was carried out and the difference between MC and data at D0 have been investigated

having implemented the D0 variables in ATLAS and having shown that the environment is very similar

we think an agreement of similar quality can be expected between MC and data for ATLAS

we interprete the deviation MC - data at D0 directly as an uncertainty of the prediction for ATLAS

a more involved tau-id like e.g. a LLH ratio might behave differently, nevertheless it is included here with the same uncertainty

45%

Summary

ATLAS has two well studied complementary algorithms for the reconstruction and identification of hadronically decayed tau-leptons

Both perform sufficiently well for the "standard" physics analyses

Traditionaly the track based approach was thought more appropriate for the medium to low energy tau-leptons and the cluster based approach more for the medium to high energy tau-leptons

Nevertheless the overlap is significant may help to understand both better

TeV4LHC offers a (rough, but nervertheless) estimate of what uncertainties one might expect for the predection of tau-id performance in ATLAS from Monte Carlo

the prediction from only Monte Carlo seems to exhibit an uncertainty as large as a factor of 2 on the rejection

when data becomes available this should decrease drastically and the question arises if we can measure tau efficiency on data (and with what precision)

Backup Slides

Strategy for TeV4LHC (2)

Implementation of D0 tauid variables in ATLAS:

due to differences in the detector design the “translation” of variables is not uniquely defined

our convention:

D0 EM3 (finely granulated layer in the EM) → ATLAS EM2

D0 EM1, EM2 → ATLAS -strip layer

tower granularity in both cases 0.1x0.1

energy thresholds have been adjusted to match different cell sizes, noise levels and so on ...

ATLAS Calorimeter and ID

The  identification makes use of tracks and calorimeter objects

Atlas has a presampler (0.025x0.1), -strip-layer (0.003x0.1), middle (0.025x0.025) , back layer (0.05x0.025) and three hadronical layers with 0.1x0.1 and 0.2x0.1

The ID has three pixel layers, four stereo microstrip layers and a straw tube tracker

ATLAS

D0 Calorimeter and ID

D0 has a an EM with four layers of 0.1x0.1, 0.1x0.1, 0.05x0.05, 0.1x0.1 and four hadronical layers 0.1x0.1

The ID consists of a silicon tracker with four stereo layers and eight stereo layers for the fiber tracker

the ID of D0 covers || < 3.0 !

D0

TauID at ATLAS

Impactparametersignificance A0/ip : only 2d information, no reconstructed primary vertex (soon to come)

Sign is defnied as sign(sin(track – cluster))

ET/pT : Ratio of total to charged transverse Energy of the Jet

Shows ET dependance for QCD-Jets but none for -Jets

-jets

qcd-jets

15 ≤ pT ≤ 40

115 ≤ pT ≤ 140

415 ≤ pT ≤ 440

2004-03-23T11:11:25