Standard model, Tracking, vertexing, btagging, taus

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Standard model,Tracking, vertexing, b-tagging,
taus
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Standard Model

• Focussed on early analysis of W and Z xsect
• W and Z xsect with electrons
• W and Z xsect with muons
• Many aspects of analysis are data-driven
• Triggering and reconstruction efficiencies from
  tag and probe
• Electron fake rate from QCD ET-miss spectrum
• Very tightly coupled to combined performance
  groups
• Will be application of standard performance
  analysis eg efficiencies of isolated lepton
  trigger
• Some work on taus
• Discuss more
• No mention of pure QCD, this still wide open

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Interplay between groups

• Can not be exhaustive ? illustrate this
  interplay through several inputs/outputs
• Well operating detector (DAQoffline)
• Signal reconstruction
• Trigger (HLT pass-through at beginning)
• Alignment
• EM energy scale
• EM calo intercalibration
• Material in front of EM calo
• Trigger and identification efficiencies
• MC tuning (detector description, physics param.,
  )
• Background control with data
• Estimate of all systematic uncertainties

Detector (trigger)
egamma
SM group

• Not a step by step program !! Iterations needed
• Need good cooperation between communities

W/Z electron channel, F.Hubaut
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Z(?ee) extraction

• Fast and robust extraction of the signal in
  early data taking phase
• Trigger not discussed here (see dedicated
  meeting)
• Large part of initial bandwidth dedicated to
  leptons, no isolation criteria

• Selection steps
• e10 trigger (single electron trigger to measure
  efficiency from data, see next slide)
• Kinematics 2 EM clusters pTgt15 GeV, hlt2.47,
  exclude largely around crack (1.3lt h lt1.6)
• Loose identification cuts robustness when
  detector perf. not understood in detail.
  Can even use simple criteria based on EM
  calorimeter only ? unbiased tracker studies

• 24 800 200 signal events with 50 pb-1

CSC

• Large sample, stat. error lt1
• At 10 TeV, reduced by 1/3rd

• Data-driven background determination

• Fit exponential slope after kinematical cuts
  ? normalise on side-bands
• 2300 400 events estimated for 50 pb-1

W/Z electron channel, F.Hubaut
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Z(ee) sample ? Efficiency determination with
data
More details in eg session

• Measure trigger/reconstruction/identification
  efficiencies with Z(ee) data sample
• Well known TagProbe method
• Single lepton trigger to allow unbiased probe
• Background contamination taken into account

Medium identification efficiency

• Reproduce differential structures

• 2 error on overall efficiency per electron with
  50 pb-1

(Integrated over whole spectrum. Mainly limited
by Z sample statistics.)
W/Z electron channel, F.Hubaut
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Z(?ee) cross section measurement
s (Nsignal - Nbackground) / (A etot Lumi)
Acceptance uncertainty (mainly limited
knowledge of underlying physics ISR, PDFs, )
? determined with MC
Previous slides

• Overall uncertainty for 50 pb-1 0.8 (stat)
  3.5 (syst) dL/L

• Systematic errors dominate, even with 50 pb-1
• Main systematics from electron selection
  efficiency (except luminosity)
• Comparable to muon channel
• Extrapolation to 1 fb-1

• estimated directly on data

• limited to 1.5 by acceptance uncertainties
  (PDF, ISR, )

• use differential cross sections (vs h and pT)

W/Z electron channel, F.Hubaut
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W(?en) extraction

• Selection steps
• e20 trigger
• Electron ? 1 EM cluster with pTgt25 GeV, hlt2.47
  exclude crack region
• Medium identification
  criterion
• Missing ET ? gt25 GeV

1 electron only increase pT cut and tigthen
identification criteria

• Not discussed here, but need detailed detector
  understanding

CSC

• 217 100 400 signal events with 50 pb-1

• Large sample, stat. error ltlt1
• At 10 TeV, reduced by 1/3rd

MTW (GeV)

• QCD background level and shape must be estimated
  directly with data

W/Z electron channel, F.Hubaut
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Data driven background determination

• Dominant background jets
• Large uncertainties, difficult to simulate, poor
  MC statistics
• Must be measured directly on data

• Principle of the method
• QCD enriched sample (98) g trigger (g20)
  and same kinematical EM cluster selection ?
  missing ET shape parametrization
• Normalise to side-band in electron sample (Zee
  removed)

• Uncertainty on background contamination 4
  (9200 events) with 50 pb-1

(limited by MC stat.)
W/Z electron channel, F.Hubaut
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W(?en) cross section measurement
s (Nsignal - Nbackground) / (A etot Lumi)

• Acceptance uncertainty
• only theoretical (ISR, PDFs, )
• impact of missing ET scale and resolution
  uncertainties has to be quantified

• Overall uncertainty for 50 pb-1 0.2 (stat)
  5 (syst) dL/L

• Systematic errors dominate largely with 50 pb-1
• main from background uncertainty (except
  luminosity)
• Luminosity uncertainty vanishes in s ratios,
  e.g. sW/sZ
• Comparable precision to muon channel (for which
  background less important, Z?mm dominates)
• Extrapolation to 1 fb-1

• estimated directly on data

• stringent test of QCD

• limited to 2.5 by acceptance uncertainties
  (PDF, ISR, )

W/Z electron channel, F.Hubaut
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Hadronic taus means for identification
Tracking ? object with low track multiplicity
( 1 ?? or 3 ?? ) ? tracks more collimated
than for average jet, (invariant mass,
weighted width of tracks system) ? decay
length makes it possible to use impact parameter
and transverse flight path (three-prong)
? isolation cone from other tracks Calorimetry
? collimated deposition in EM (radius, width
in strips) use shower shape variables
? strong EM component for single prong
(50 energy by ?0) ? reconstruct ?0
subclusters ? isolation cone ? both EM
and HAD components
E.Richter-Was, UJ/IFJ-PAN ATLAS CP
Week, 9 June 2008
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Reconstruction
Track-seeded and calo-seeded algorithms
integrated for rel.14.2.0
A.Kaczmarska, S. Lai, N. Meyer, L. Janyst

• Track-seed and calo-seed
• - use good quality tracks (pTgt6 GeV) as initial
  seed
• - candidates with 1-8 quality tracks (pTgt1 GeV)
  in ?Rlt0.2 from the seed
• - the ?,f using pT weighting of tracks, check
  charge consistency (Q 2)
• find matching cone 0.4 TopoJets (gt10 GeV, DR lt
  0.2) as calo-seed
• ET (calorimetric) using H1-style calibration on
  cells from calo-seed
• ETeflow with energy-flow method (EM calo -
  separating neutral/charged sources of energy)
• - reconstruct p0 subclusters

• Calo seed only
• - use cone 0.4 TopoJets (gt10 GeV) as calo-seed,
  matching seed not found from tracking
• define the ?,f using calo-seed (h corrected for
  z vertex),
• looser tracks-quality selection, track pTgt 1
  GeV
• - ET (calorimetric) using H1-style calibration
  on cells from calo-seed

Track-seed only small fraction (few ) of
total only
E.Richter-Was, UJ/IFJ-PAN ATLAS CP
Week, 9 June 2008
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Reconstruction
Only calo-seed
Both seeds
QCD J2 pThard35-70 GeV
Only track-seed
QCD J2 sample pT35-70 GeV
Z? tt
Overall purity in the sample Z-gttt 57 for
both seeds (yellow),
23 for only calo
seeds (red)
E.Richter-Was, UJ/IFJ-PAN ATLAS CP
Week, 9 June 2008
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Tracking-1

• Many new changes in release 14
• Improved tuning of tracking following CSC
• New functionality BackTracking, TRTOnly,
  ConversionFinder, V0Finder, LowPtTracking
• Geometry updates
• Detector condition information introduced
• Tracking for startup single-beam, beam-halo

Tracking summary M.Elsing
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Tracking-2

• Handling real data
• Noisy SCT and TRT modules
• Condition service introduced, allows bad
  channel/module masking
• This links in with monitoring work from Saverio,
  Dan, Aidan and Mary
• Running in different configurations
  PixelSCTTRT or SCTTRT
• Need tuning

Tracking summary M.Elsing
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Tracking-3

• Mass resolution after alignment is a worry
• Z-mass is sensitive to weak eigenmodes

Tracking summary M.Elsing
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Vertexing

• Primary vertex and beamspot
• How to use beamspot to find PV and then PV find
  BS
• Pileup
• Vertex code can handle pileup
• But more testing required, eg identification of
  correct PV for b-tagging, identification of
  correct PV in high levels of pileup
• Technical issues
• What to do?
• Study pileup in minbias Craig
• Study reconstruction of PV in pileup for
  b-tagging --?
• Maybe using a top sample?

Vertex summary A.Wildauer
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B-tagging at startup

• Avoid using PDFs
• Related to PV finding discussed earlier

b-tagging summary L.Vacavant
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Commissioning

• Once we have some understanding of
  tracking/alignment (previous talks)
• First taggers
• Track counting no calibration
• JetProb negative d0/s from data
• SV0 simple inclusive secondary vertex
• taggers relying on LR for b,u(,c) hypotheses next
• Simple baseline switch on progressively the
  extra features
• V0 rejection
• Dedicated treatment for shared tracks, other
  categories
• Samples
• min.bias, QCD resolution function for JetProb
• QCD, bbbar JetProb, SV0
• muonjet b-tagging efficiency measured in data
• ttbar

Monitor with jet events
b-tagging summary L.Vacavant
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Early jet taggers

• DCP with BS using jets
• Look at track jets
• Optimise tracks for use in this
• Introduce PV, remove V0s and conversions
• Secondary vertex taggers
• Introduce L3D/sigma, no pdf used
• Very strong links with tracking groups
• Optimise track selection, tracking performance in
  jets, vertex reconstruction

b-tagging summary L.Vacavant
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What to do?

• Kenny
• Top in dileptons
• Start looking at robust early tagger jetprob
• Mary
• SV0 and then SV1 etc
• Saverio will talk to Richard Hawkings
• Tracking interface via Craig and Will

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TODO List for taus (I)
E.Richter-Was, UJ/IFJ-PAN ATLAS CP
Week, 9 June 2008
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TODO List for taus (II)
E.Richter-Was, UJ/IFJ-PAN ATLAS CP
Week, 9 June 2008
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TODO List for taus (III)
E.Richter-Was, UJ/IFJ-PAN ATLAS CP
Week, 9 June 2008
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