September 20, 2002

1
TARC Report from the Mini-Workshop

• September 20, 2002
• All DZero Meeting
• (Jianming Qian), Valentine Kouznetsov, Avto
  Karchilava, Rick Van Kooten, HTD

2
Tracking Algorithm Recommendation Committee Charge

• Collect information on performance of various
  tracking algorithms about
• Efficiencies, fake rates, and misreco rates using
  standard procedures developed by the global
  tracking group and standard (both beam and MC
  generated) datasets
• Reconstruction logistics such as CPU time per
  event, memory, and luminosity dependence
• Input from physics/id/algorithm groups to be
  solicited

3
Tracking Algorithm Recommendation Committee Charge

• Make recommendation on how we should run tracking
  in p13 on the taking into account the farm
  resources available in October assumed to be
• 25 Hz events
• 58 seconds on 500 Mhz machine where 29 seconds is
  available for tracking
• Note p13 is frozen October 1st, implying the TARC
  should move as quickly as is possible following
  this meeting

4
Acknowledgements

• A great deal of help and cooperation was
  available.
• Special notes of thanks to
• The people who make SAM work including Lee
  Lueking
• The D0 Data Farm Reco Group and Heidi Schellman
  and Mike Diesburg
• Tracking Group esp. V. Kouznetsov
• Mark Sosebee and the UTA farms
• Mike Strauss
• The physics, ID, and algorithm groups including
  the speakers from Wednesdays workshop
• The Tracking Algorithm Developers

5
Contents of this Talk

• Data Samples Procedures
• Definitions
• Presentations from the Mini-workshop
• Tracking Algorithms and performance report (S.
  Khanov)
• Lifetime B-tagging (B. Wijngaarden)
• Tau Reconstruction (S. Duensing)
• B Hadron Reconstruction (V. Jain)
• EM ID Issues (R. Zitoun)
• Dimuon Studies (R. Hooper)
• Higgs Gp. Report (L. Feligioni)
• Secondary Vertex B-Tagging in Top Samples (A.
  Schwartzman)
• Top Gp. Report (E. Chabalina)
• Common Elements in their reports
• Summary

6
Data and Monte Carlo Samples

• Data files in SAM (picked events have been
  merged)
• Run 155554 test run of 10,000 events
• Run 157708 - 90,000 events TV7.31 w/ inst. lum
  5e30
• SMT grade C, CFT in the good run range with full
  stereo readout, prior to calor zero suppression
  change.
• 38,000 dimuon events picked by B-physics Group
  for J/psi post full stereo readout
• 16,000 mujet events picked by BID group
• 5,400 picked high pT dimuons prior to full stereo
  readout
• 6,800 picked high pT diem events post full
  stereo readout

7
SAM Definitions Data

• Run 155554 reco_all_0000155554tk-p11.11-.root
• Run 157708 reco_all_0000157708tk-p11.11-.root
• Mujets merge_mujettk-p11.11-.root
• J/Psi to dimuons dimuon_third_mergedtk-p11.11-
  .root
• Z to ee pick_diemtk-p11.11-.root
• Z to mumu (not isolated) pick_dimuontk-p11.11-
  .root

The third s above are gtr, htf, gtrela,
htfela, gtrhtf, aa, aa_vtx, or trkall.
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Data and Monte Carlo Samples

• Monte Carlo files in SAM
• 5,000 Z to ee
• B MC includes
• 8,000 Bs to Ds eX
• 8,000 B to J/psi(muons) Ks
• 5,600 Bs to Ds pi
• 210,000 Top Group lepton jets with average of
  0.5 and 2.5 additional minbias
• 10,000 bbH to bbbb Higgs events
• 10,000 hadronic tau events
• A light quark sample?
• Not all same simulation used in generation

9
SAM Definitions Monte Carlo

• 5,000 Z to ee z-eetk-p11.11-.root
• B MC bbbarQQtk-p11.11-.root
• 210,000 Top Group lepton jets with average of
  0.5 and 2.5 additional minbias ttbar-wjjwlnutk-
  p11.11-.root
• 10,000 bbH to bbbb Higgs events
  bbh-bbbbtk-p11.11-.root
• 10,000 hadronic tau MC tau_tauhcwtk-p11.11-.ro
  ot
• A light quark sample?

The third s above are gtr, htf, gtrela,
htfela, gtrhtf, aa, aa_vtx, or trkall.
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Links to Workshop Talks
11
Track Finders
smt cft Ptcut GeV/c
gtr cft-smt XOR smt-cft 2d 1d(2d) 0.4
htf cft-smt OR smt-cft 2d 2d(1d) 0.5
ela global 2d 2d -
aa smt-cft 1d 1d 0.18

• Experts may be willing to describe their
  algorithms in more detail.
• We asked the algorithm developers to set their
  own parameters.

Stuff on this and next few pages from S. Khanovs
talk at the mini-workshop.
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Reconstruction Procedure

• All samples were reconstructed with p11.11
• Individual and Combinations
• gtr (no H-disks in data, yes MC)
• htf with grt refit (all but aa did that)
• gtrela gtr (no overlap) elastic on leftover
  hits
• htfela htf elastic on leftovers
• gtrhtf OR of gtr (no overlap) htf
• aa aa (some samples had no vertex info, look for
  aa_vtx, also gtr-refit now available also aa
  tracks have wrong chi2 and d0hitmask)
• Trkall (all 6) for cross checks
• 15 failed to finish the two steps
• Main problems are not thought to be the fault of
  the tracking algorithms

13
Analysis Tools

• gtr_analyze
• Filled roottuples with reco tracks and their
  parameters
• Fills info needed for comparison with MC
• Some MC samples didnt retain the MC hits so a
  hit-by-hit comparison could not be made.
• gtr_examine (M.S.)
• Root macros which calculate track efficiency and
  fake rates
• Tons of plots
• Primarily for MC samples

14
Definitions

• Track Quality is described by a c2.
• Good Tracks have matching c2 lt 25
• Misreco Tracks 25 lt c2 lt 500
• Fake Tracks c2 gt 500

15
Tracking Algorithm CPU vs memory

• Algorithms take similar amounts of time in
  tracking except AA, which is 3 times faster.
  D0Reco time spec. met. Improves possible.
• No hard info on occupancy dependence. Top MC took
  longer than spec. for some combos.

aa
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Results from Tracking Algo. Gp. (S. Khanov)

• A lot of material was presented.
• It was clear from the first that no algorithm or
  combination solved our problems.
• Studies were shown of data and MC efficiencies
  and fake rates.
• Number and distributions of tracks in eta and phi
  for all algorithms and combinations.
• Z to dimuons, J/psi to dimuons, psi, phi to KK
  bumps shown and fitted with background estimates.
• Detailed comparison of Z to ee including a
  diagram comparing which Zs were identified
  between three algorithms.

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Results from Tracking Algo. Gp. (S. Khanov)
1) Bumps
2) Split J/psi mass in Eta regions
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BID Group Results (Bram W.)

• Studied B-tagging in Jets in Run 157708 and the
  mu-jet data
• Number of tracks per jet and number of good
  tracks (positive DCA, pTgt1.5 GeV/c) in jets with
  an 0.5 cone
• Efficiency is fraction of b-jets (defined using
  pTrel mu-jet) tagged
• Mistag Rate is fraction of jets in Run 157708
  that are tagged.
• Fleura studied top Monte Carlo
• Found the tagging probabilities and mistag rates
  for 1- and 2- tagged jets for each algorithm
• Presented a clear table.

19
BID Group Results (Bram W.)

• Example plot (one of several)

Effy
Mistag Rate.
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Tau ID group (Silke and Yuri)

• Studied the hadronic tau MC (signal) and the 4b
  MC (backgd)
• Noted tau ID is highly sensitive to the
  efficiency
• Counted the number of 1 3-prongs
• Looked at S(pT) of additional tracks in cones
  around the tau
• Mass distributions of matched tracks (seemed
  independent of algorithm)
• Studied tracking efficiency in 1-prong vs 3-prong
  vs pT
• Mapped lost track eta-phi distribution

21
Tau ID group (Silke and Yuri)

• Left Plot shows the of tracks in 1 3 prong
  tau events
• Right plot shows the effy vs pT of the prongs
  for 3 prong taus.

22
B Hadron Reconstruction (Vivek)

• Analyzed the dimuon data sample to search for
  J/psi, Ks, and L.
• Interested in low pT to reconstruct the pion in
  the lambda decay.
• Showed the mass resolution, signal and
  background for the 6 cases.
• Analyzed the three B MC samples Bs to Ds-p, B0
  to D-p, and Bs to Ds-eX.
• Showed direct comparisons of the effy, widths,
  misreco rates in the samples.

23
EM ID (Robert Z.)

• Studied Z to ee data and MC and applied the
  track-matching used in obtaining the W and Z
  cross sections recently shown at ICHEP.

Plots for p11.09 gtr Effy depends on c2 cut.
Select p(c2)gt1.
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EM ID

• Studied Z to ee data and MC and applied the
  track-matching used in obtaining the W and Z
  cross sections recently shown at ICHEP in the
  region etalt 0.8
• Studied eta dependence of the efficiencies in the
  Monte Carlo

Efficiencies are per track
25
EM ID

• TARC Z events run with p11.11
• various algorithms
• hlt0.8

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New Phenomena and Muon ID (Ryan H.)

• Concentrated on the large dimuon data sample
• Compared the 6 cases against J/Psi, upsilon, and
  Z to dimuons
• Number identified
• Mass and width

27
New Phenomena and Muon ID (Ryan H.)
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Higgs Group Report (Lorenzo F.)

• Studied the bh to bbbb MC sample with all
  tracking algorithms and combinations
• Tracking effy vs pT and eta
• Misreconstruction and Fake rates vs pT and eta
• DCA resolution for various pT min.
• Effy and fake rate for Track reconstruction in
  jets
• B-tagging effy and mis-tagging rate

A lot of information!
29
Higgs Group Report (Lorenzo F.)
30
Higgs Group Report (Lorenzo F.)
31
Higgs Group Report (Lorenzo F.)
32
Higgs Group Report (Lorenzo F.)
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Secondary Vertex B-Tagging (Ariel)

• Studied b-tagging in ttbar MC events for all 6
  cases.
• B-quark vs light quark tagging effy vs jet pT,
  eta, jet-track multiplicity, and jet multiplicity

34
Secondary Vertex B-Tagging (Ariel)

• Same plot, knee of curve

35
Tracking in Top Samples (E. Chabalina)

• Studied Tracking Effy, mis-reco rate, fake rate,
  and purity in top MC events using gtr_examine
  using all cases
• 0.5 and 2.5 additional min-bias events overlaid
• pT dependence, eta dependence, pT jet dependence
  
• Studied the Z to ee data
• Numerically rated the 6 cases in tables of
  criteria!

36
Tracking in Top Samples (E. Chabalina)
0.5 mb (500 events)
gtr htf gtrela htfela gtrhtf aa
eff 0.769 0.856 0.834 0.898 0.878 0.776
purity 0.959 0.974 0.948 0.960 0.967 0.984
goodeff 0.617 0.742 0.658 0.698 0.777 0.602
misreco 0.069 0.073 0.094 0.088 0.072 0.118
fake 0.041 0.026 0.052 0.040 0.033 0.016
2.5 mb (gt 1000 events)
gtr htf gtrela htfela gtrhtf aa
eff 0.753 0.854 0.819 0.898 0.873 0.776
purity 0.954 0.961 0.937 0.948 0.947 0.988
goodeff 0.584 0.690 0.615 0.675 0.691 0.595
misreco 0.064 0.069 0.089 0.085 0.069 0.137
fake 0.046 0.039 0.061 0.052 0.053 0.012
37
Tracking in Top Samples (E. Chabalina)
gtr htf gtrela htfela gtrhtf aa
Reconstruction efficiency of isolated leptons in data Reconstruction efficiency of isolated leptons in data Reconstruction efficiency of isolated leptons in data Reconstruction efficiency of isolated leptons in data Reconstruction efficiency of isolated leptons in data Reconstruction efficiency of isolated leptons in data Reconstruction efficiency of isolated leptons in data
Z(ee) 4 3 1 1 2 4
Z(µµ) 4 5 2 1 3 5
sum 8 8 3 2 5 9
Reconstruction/b-tagging efficiency in MC Reconstruction/b-tagging efficiency in MC Reconstruction/b-tagging efficiency in MC Reconstruction/b-tagging efficiency in MC Reconstruction/b-tagging efficiency in MC Reconstruction/b-tagging efficiency in MC Reconstruction/b-tagging efficiency in MC
All tracks 5 2 4 3 1 6
All tracks 4 2 5 4 3 1
tracks in jets 4 2 3 1 1 5
tracks in jets 4 2 5 4 3 1
IP b-tag 3 1 4 5 2 3
SV b-tag 3 1 4 2 1 5
Sum MC 23 10 25 19 11 21

Total 31 18 28 21 16 30
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Summary

• I described the data samples, procedure and
  summarized the mini-workshop
• Common Elements in the presentations
• No magic algorithm
• For high pT physics the 3 combinations outperform
  any single one and arent strikingly different
• For low-pT physics there was consensus that
  combinations including htf had best effy vs
  mistag fraction
• This is a start but isnt as good as wed like to
  be!