Amnon Harel [e-mail]

1
International Workshop on Top Quark PhysicsLa
Biodola, Isola dElba, Italy18-24 May, 2008
MC for top backgroundat the Tevatron

• Amnon Harelaharel_at_fnal.gov
• University of Rochester

Thanks to Kirsten Tollefson, Lisa Shabalina,
Christopher Neu, Tom Junk, Ann Heinson, Jason
Nielsen, Michael Begel, Ulrich Husemann, Kevin
Lanon, Gustavo Garzon, Gerald Grenier, and
apologies to those whose names I accidentally
left out
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Outline
Backgrounds
Measurements
Matched MC
The cost of matching

• BTWs
• multijets
• Detector simulation

Reweighting the MC
3
The backgrounds top pair
Hard to simulate misreconstructions ? Data
driven estimation
Dilepton channel
Small ? A rough simulation suffices
A.k.a. Zjets
Leptonjets channel
Havent discovered that one yet
4
Vjets top pair
Dilepton channel

• Z(ee/µµ)jets - main background in ee and µµ
  channels, Z(??)jets in eµ
• Very important at the early stages of event
  selection
• After final selection Zjets contribution is
  small
• Most measurements do not use b tagging
• Flavor composition usually not important

Leptonjets channel

• This channel provides the most precise
  measurements
• Wjets - main background
• Very important at all stages of selection
• Most of measurements use b-tagging
• Knowledge of flavor composition is a limiting
  factor for precision measurements

5
The backgrounds single top
H.F. fractions of Wjets crucial
Wlight
Multijet

• BTW WH Higgs search is also sensitive to Wjets
  H.F. fractions

CDF
6
Vjets cross sections
7
Wbb measurements
What have we measured? Though conclusions from
WHF measurements not yet applied to top analyses
CDF preliminary from Monday (1.9fb-1) Harsh
b-tagging and MET (gt25GeV) selections Jet
definitions Jet selection Result Alpgen
prediction 0.78pb
,
,
DØ preliminary from 2005 (382pb-1)
, ,
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Wc measurements
CDF Data MCFM
DØ Data AlpgenPythia
Phys. Rev. Lett. 100, 091803 (2008)
arXiv 0803.2259v1hep-ex
Recent MCFM calculations (John Campbell) for DØ
cuts yield R(LO)3.7, R(NLO)3.4
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Wjets measurement

• Dedicated Wjets measurement.

SMPR CKKW matched Madgraph Pythia MLM MLM
matched Alpgen Herwig MCFM NLO predictions
will be a recurring theme
10
Matched MC
For top physics, we usually simulate Wjets (also
Zjets) using MLM matched Alpgen. Implementations
differ

• light partons jets are MLM matched
• with pTgt8GeV
• MLM stable to the chosen pT
• Discard events with additional heavy quarks from
  the PS MC
• done in post processing
• Generate 14 samples
• WbbNlp, with N0,1,2, or 3
• WccNlp, with N0,1,2, or 3
• WNlp, with N0,1,2,3,4, or 5
  (includes Wcjets ? massless c quarks)

• MLM matched (pTgt15GeV) within each class of
  events
• cc / bb pairs within the same parton jet are
  taken only from the PS MC those in different
  jets taken only from the ME MC
• Generate 15 samples
• WbbNlp, with N0,1, or 2
• WccNlp, with N0,1, or 2
• WcNlp, with N0,1,2, or 3
• WNlp, with N0,1,2,3, or 4

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Zjets Simulation

• Zjets appears at a lower rate (10), but has
  much less background ? a good process for tuning
  the simulations.
• NormalizationUsually it suffices to normalize
  according to cross sections predicted from MCFM
  or NNLO calculations.
• Dependency on kinematic cuts!
• Some analyses require normalization from data,
  e.g., CDFs top FCNC search in Zjets
• KinematicsCan be tuned using data
• Example ResBos described DØs Z pT data well.
  DØ is starting to use it as a surrogate to the
  data and to re-weight ALPGENPYTHIA to match the
  same spectrum. The same re-weighting is carried
  over to Wjets.

Alpgen qfacktfac1
arXiv/0712.0803
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Zjets Shape Pythia Sherpa - I
Sherpa uses CKKW matching
T
Normalized to data without parametrizing in
Njet Pythia is a parton shower generator ? not
enough radiation JES uncertainties dominate
systematics (and are a bit conservative)
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Zjets Shape Pythia Sherpa - II
Again Pythia spectrum is too soft
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Zjets Shape Pythia Sherpa - III
Again Pythia spectrum is too soft
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Zjets Shape Pythia Sherpa - IV
Well return to the simulation of dijet angles
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Zjets vs. NLO

• Dedicated Zjets measurement.

Phys. Rev. Lett. 100, 102001 (2008)
MCFM hiding behind data points
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Normalizing Wjets
Wjets and WHF are normalized to data, after
other backgrounds (multijets, dibosons, etc.) are
subtracted.

• multijets estimated from data samples with
  leptons that pass only a looser selection (mostly
  looser isolation)
• loose-tight
• several approaches, typically
• Wjets normalized to data before b tagging
• or fitted in signal samples
• WHF fractions normalized to data after b tagging

• multijets estimated from data samples with
  leptons that pass only a looser selection
• anti-electrons, non-isolated
• Studies with dijet multijet events
• Method 2 inherited from Run I
• Wjets normalized to data before b tagging
• WHF fraction fitted to data after b tagging

Assumptions KbbKcc, and Kc1 (consistent with
studies)
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Wjets Normalization _at_ CDF

• Three parameter fit to Bottom-Charm-Light
  templates of jet-flavor separating distributions
  (NN output, SecVtx mass) in W2 jet data
• yields KHF1.40.4
• relative to Alpgen H.F. fraction
• Light flavor yield with prediction from
• per jet fake b tag rates (estimated from
  inclusive multijet data)
• either W LF MC or data, pre b tag yields

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Wjets Normalization _at_ DØ

• First data driven normalization single-top
  evidence analysis

PRL 98, 181802 (2007)

• For Wjets
• Normalize to pretag data
• Channel dependence?
• single-top vs. top-pair analysis
• same in ejets µjets within uncertainties
• Njet dependence?

• For WHF
• Normalize to data after b-tagging
• in 0 b tag bin ? negligible signal
• Defined relative to Alpgen H.F. fraction

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Wjets Normalization _at_ DØ - II

• Procedure refined for later tt?ljets
  measurements
• tighter selection cuts
• normalize MC to match the fraction of 0 tag
  events in 1 and 2 jet bins

Studies of systematic uncertainties

• Sensitive to extra backgrounds included
• Sensitive to selection cuts on jets
• single top ?lt3.4 ttbar ?lt2.5
• Consistent between e/µ channels
• Studied several cuts on b tag output

• Separated Wc for Wlp and varied by 20 (not
  enough?)
• Dependence on the multijet background
  contribution
• Tried to extract corrections to Wcc and Wbb
  separately

Results

• Switched from Alpgen 2.05 to 2.12
• Several bug fixes including one in Wcc / Wbb
  generation
• Compared shapes the only noticeable difference
  is ?R(j,j)
• Cross sections differ by a factor of 2!
• New KHF factor 1.9

• Measured HF scale factor (uncertainty from all
  deviations observed in the studies)
• KHF 1.17?0.18
• Used for summer results

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Wjets Normalization for BYSM

• Normalizing Wjets to data with the same jet
  multiplicity as signal is not trivial for BYSM
  searches, or even for cross section measurements.
• Examples
• searches for resonant top pair production
• Iterative procedure in CDFs
• Analytical work around in D0s
• Charged Higgs search

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Wjets Other Reweightings
Some discrepancies between Alpgen and data are
showing up with the increasing statistics. Correct
ed for / treated as systematic uncertainties in
recent measurements. E.g. possible
missimulation of the angular distributions of
the jet with the 2nd highest pT Plot from
latest single top analysis
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DØ experience with matching
At the time, Alpgen was the only matched MC that
CDF DØ could run, integrated with their
software, mass produce. It allows us to produce
physics results! Large scale use cutting edge
technology pain Outlining the DØ experience to
identify lessons.

• Must add together the parton-jet bins with the
  correct weights. Unfortunately, weights are
  sample dependent
• Must freeze samples
• Post processing in DØ due to overlaid zero bias
  data HF removal
• Book keeping nightmare (ttbar, Zjets in HF
  MZ bins, systematic variations)
• ?Error prone
• Large relative weights
• necessary for multiple (additional) jets
• but complicates statistics (some physics
  required)

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DØ experience with matching

• Using matched Alpgen extensively for the last
  couple of years
• Weve made several mistakes, e.g.,
• Random seeds outside legal range
• Imperfect HF removal
• Study Interplay with MC tunes
• Some bugs found along the way, had to find
  workarounds
• Best case need to look at the right plot in the
  right channel
• Recent case why does that matching weight look
  odd?
• Slow turn around times
• Alpgen release ? DØ release ? Production (large
  0lp samples, slow 5lp samples) ? Postproduction ?
  Analyses
• 6-12 months
• Limits our ability to generate sufficient
  samples to study systematics

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BTW1 MC in multijet modeling

• Though multijet modeling is data driven, it is
  (mostly) based on samples with 3 jets which are
  reconstructed as lepton 2 jets.
• Can check the methodology using simulated
  multijets
• Lepton ID cuts
• Lepton ID efficiencies and their
  parametrization
• MET (triangle) cuts
• Sample composition

Example of bad agreement from recent DØ studies
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BTW2 Detector simulation

• An ubiquitous problem, often taken for
  granted.Data based modeling for
• misreconstructed leptons, i.e., multijet
  background
• b tagging rates
• relative jet energy scale

• This talk focused on the generator parts of
  simulation rather than on detector simulation.
• Modeling of multijet background is covered in
  analysis presentations
• the last two items will be covered in two talks
  about tools for top later today (CDF _at_ 1530,
  D0 _at_ 1600).

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Conclusions

• Simulation of Wjets and Zjets
• is not trivial
• can be analysis dependent
• One size can almost fit all
• Dont always need the most sophisticated
  treatment
• Several approaches to estimating the heavy
  flavor contribution
• Can fit and/or normalize Wjets
• New physics can complicate matters
• Using matched Alpgen extensively for the last
  couple of years
• Able to meet all our physics needs!
• Higgs / Top pairs / single top / WHF / ZHF
• LeptonjetsMET / di-lepton final states (Zjets
  important)
• possible inaccuracies (e.g. Z pT, 2nd jet ?R)
• difficulties adapting to this technology
• Technical lesson avoid any post processing that
  can break the matching
• Other generators seem promising
• but have received much less scrutiny

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• Back up slides

29
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Other weights

• To estimate PDF uncertainties we reweight our MC
  after the fact, rather than regenerate it.
• Is this compatible with MLM matching?
• Yes private communications with Mangano
• We simulate additional collisions by overlaying
  zero-bias data (i.e. free of trigger biases)
  onto simulated collisions. Since instantaneous
  luminosities are still rising, we update the
  simulation by giving more weight to simulated
  events whose overlaid data event had a high inst.
  lumi.

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Matching _at_ CDF
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Calibrating the Simulation

• We verify each aspect the simulation, mostly on
  appropriate data samples, and correct the
  simulation as needed.
• Jets
• Relative energy scale
• Energy resolution (ET smearing)
• Reco. ID Efficiencies
• b-tagging rates
• Corrections for gluon splitting
• Instantaneous Luminosity
• Primary Vertex Z-coordinate
• Electrons
• Resolutions (ET smearing)
• Reco. ID efficiencies
• Muons
• Resolutions (pT smearing)
• Reco., ID and isolation efficiencies

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Normalizing to Data _at_ DØ

• In several double-top ljets property analyses
• WHF normalized to data as in previous slides
• Wjets fit to data
• topological likelihood separates top pair from
  Wjets

Example from PRL 100, 062004 (2008)
Backgrounds (mostly Wjets)
Top Pair
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Normalizing Wjets _at_ DØ HZ

• Another example Higgs search in HZ?bbX channel
• Heavy Flavor in (W/Z)jet AlpgenPythia
  predictions were multiplied by K-factors (NLO/LO)
  calculated with MCFM
• (W/Z)jets normalized to data before b-tagging

After b-tagging
Before b-tagging
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Terminology

• DØ is trying to converge on terminology for
  normalization factors
• K-factor normalizes LO to match (N)NLO
• K-factor normalizes MC to match (N)NLO
• S-factor normalizes MC to match pretag data
• SHF normalized heavy-flavor MC to match
  b-tagged data
• At DØ
• For Zlp and ZHF
• Using K-factors, normalize to theory
• Some analyses (e.g. )
  override with S-factors
• For Wlp Using S factors
• For WHF Using S SHF factors

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Know-how I

• Generate 14 samples
• WbbNlp, with N0,1,2 or 3
• WccNlp, with N0,1,2 or 3
• WNlp, with N0,1,2,3,4 or 5 (includes
  Wcjets)
• Individual samples can not be used any more!

Add all parton-jet bins together with weights Fi
36
Know-how II

• Sample has to be frozen
• Large relative weights
• Complicates statistics
• Post processing
• Data quality selection due to zero bias overlay
• Discard events with additional heavy quarks
  created by Pythia
• Generate 14 samples
• WbbNlp, with N0,1,2 or 3
• WccNlp, with N0,1,2 or 3
• WNlp, with N0,1,2,3,4 or 5

No skimming
Book keeping is a nightmare!
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From generated MC to data

• Generate multi-parton MEs with Alpgen
• Add showering and hadronization from Pythia
• b-fragmentation model
• underlying event model
• Run full D0 detector simulation and
  reconstruction
• Add zero bias events to match luminosity profile
  in data

• Apply to simulated events
• JES
• Jet removal
• Smear jets, electrons and muon
• Propagate to missing ET
• Correction factors
• Trigger efficiency
• Electron and muon ID efficiency
• For b-tagging
• Taggability RF
• Tag rate function
• Reweightings lumi, z vertex, etc

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Zjets _at_ CDF - I

• FCNC analysis used this for a systematic
  uncertainty.
• negligible doesnt appear in the tables

Alpgen qfacktfac2.0
Alpgen qfacktfac0.5
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MCFM Zjet _at_ DØ

• An example
• Results for 60-75 GEV

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MCFM Wjets _at_ DØ

• Method
• All calculations come from the MCFM author (John
  Campbell)
• K-factor sigma(NLO)/sigma(LO)
• Parameters
• PDF CTEQ6L1(LO), CTEQ6M (NLO)
• Factorization scale Renormalization scale MW
• Conclusions
• MCFM calculations show that K-factor for Wlight
  is stable
• K-factors for Wc, Wcc, Wbb decrease as jet pT
  increases
• MCFM does not support any additional HF scale
  factor in addition to Wlight k-factor
• The last conclusion contradicts our observation
  from data

41
DØ Zjets Normalizations

• When estimating the Wjets background in
  LeptonjetsMET channels, we predict Zjets from
  the simulation studies on di-lepton channels.
• For Zlp and ZHF
• Typically weigh AlpgenPythia to MCFM / NNLO
• NNLO from Hamberg et.al., Nucl.Phys.B359
  Martin et.al. hep-ph/0308087
• Agree to within 10
• Recently calculate the K-factors with MCFM (is
  shaping needed?)
• K-factors in the theoretical sense NLO/LO
• Cuts crucial in W2HF (e.g. )
• Evaluate effect of quark masses at LO 10-40

42
2nd jet angles _at_ 1 fb-1
Clearly theres nothing to be excited about in
this plot. Very preliminary analysis of later
data shows similar trends.
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DØ Zjets Shape Corrections
arXiv/0712.0803

• Starting to use ResBos as a surrogate to our data
• Reweighting AlpgenPythia MC to fit the ResBos
  spectrum
• Applying the lesson from Zjets to Wjets

• Other approaches
• Scale directly to data as a function of Njet
• Consider other event source
• Measure reweight as a function of
• ZpT (reconstruction vs. particle level)
• jet observables

44
Wc measurements
CDF Data MCFM
DØ Data AlpgenPythia
Phys. Rev. Lett. 100, 091803 (2008)
arXiv 0803.2259v1hep-ex
Recent MCFM calculations (John Campbell) for DØ
cuts yield R(LO)3.7, R(NLO)3.4
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Title

• Text