Amnon Harel aharel@fnal University of Rochester
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Transcript of Amnon Harel aharel@fnal University of Rochester
International Workshop on Top Quark PhysicsLa Biodola, Isola d’Elba, Italy
18-24 May, 2008
MC for top backgroundat the Tevatron
Amnon HarelAmnon [email protected]@fnal.gov
University of RochesterUniversity 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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OutlineOutlineBackgrounds Matched MC
The cost of matching BTWs:
• multijets
• Detector simulation
Reweighting the MC
Measurements
ccbb
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The backgrounds – top pairThe backgrounds – top pairHard to simulate
misreconstructions Data driven estimation
A.k.a.: Z+jets
Small A rough simulation suffices
Haven’t discovered that one yet
Dilepton channelDilepton channel
Lepton+jets channelLepton+jets channel
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V+jets – top pairV+jets – top pair
Dilepton channelDilepton channel
Lepton+jets channelLepton+jets 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 Z+jets contribution is small • Most measurements do not use b tagging
• Flavor composition usually not important
• This channel provides the most precise measurements • W+jets - 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
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The backgrounds – single topThe backgrounds – single top
BTW: WH Higgs search is also sensitive to W+jets H.F. fractions
CDF
H.F. fractions of W+jets crucial
W+light
Multijet
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V+jets cross sectionsV+jets cross sections
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CDF preliminary from Monday (1.9fb-1):
Harsh b-tagging and MET (>25GeV) selections
Jet definitions:
Jet selection:
Result:
Alpgen prediction: 0.78pb
, &
WW++bbbb measurements measurements
DØ preliminary from 2005 (382pb-1): pbWbbpp 64.GeVpbT 20jet 52.jet b 750. bbR ..%@ LC95 , , &
pb
lWBRbWppb
)syst(.)stat(..
jetsjets
420270742
GeVE jetT 20 02.jet
40.coneR 40. bjR
, &
What have we measured? Though conclusions from W+HF measurements not yet applied to top analyses
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WW++cc measurements measurements
GeVp jetcT 20
52. jetc
DØ Data: Alpgen+Pythia:
jetsWpp
jetcWppR
GeVp jetcT 8
03. jetc
lWBRjetcWpp
Phys. Rev. Lett. 100, 091803 (2008)
arXiv: 0803.2259v1[hep-ex]
Recent MCFM calculations (John Campbell)for DØ cuts yield R(LO)=3.7, R(NLO)=3.4
CDF Data:
MCFM:
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W+jets measurementW+jets measurementDedicated W+jets measurement. 0240 .,. jetjet yR
SMPR = CKKW matched Madgraph + PythiaMLM = MLM matched Alpgen + HerwigMCFM = NLO predictions
will be a recurring theme
....
41jetsWMC
data
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• MLM matched (pT>15GeV) 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:• W+bb+Nlp, with N=0,1, or ≥2
• W+cc+Nlp, with N=0,1, or ≥2
• W+c+Nlp, with N=0,1,2, or ≥3
• W+Nlp, with N=0,1,2,3, or ≥4
• light partons jets are MLM matched• with pT>8GeV
• MLM stable to the chosen pT
• Discard events with additional heavy quarks from the PS MC
• done in post processing
Generate 14 samples:•W+bb+Nlp, with N=0,1,2, or ≥3•W+cc+Nlp, with N=0,1,2, or ≥3•W+Nlp, with N=0,1,2,3,4, or ≥5 (includes W+c+jets massless c quarks)
Matched MCMatched MCFor top physics, we usually simulate W+jets (also Z+jets) using MLM matched Alpgen. Implementations differ:
cc bb
cc
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Alpgen qfac=ktfac=1Alpgen qfac=ktfac=0.5,could be a JES issue
ZZ+jets Simulation+jets SimulationZ+jets 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., CDF’s top FCNC search in Z+jets
KinematicsCan be tuned using dataExample: ResBos described DØ’s Z pT data well.
DØ is starting to use it as a surrogate to the data and to re-weight ALPGEN+PYTHIA to match the same spectrum. The same re-weighting is carried over to W+jets.
arXiv:/0712.0803
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ZZ+jets Shape: Pythia & Sherpa - I+jets Shape: Pythia & Sherpa - I
Normalized to data without parametrizing in Njet
Pythia is a parton shower generator not enough radiationJES uncertainties dominate systematics (and are a bit conservative)
1950 fbLdt .
Sherpa uses CKKW matching
T
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ZZ+jets Shape: Pythia & Sherpa - II+jets Shape: Pythia & Sherpa - II
1950 fbLdt .
Again Pythia spectrum is too soft
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ZZ+jets Shape: Pythia & Sherpa - III+jets Shape: Pythia & Sherpa - III
1950 fbLdt .
Again Pythia spectrum is too soft
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ZZ+jets Shape: Pythia & Sherpa - IV+jets Shape: Pythia & Sherpa - IV
1950 fbLdt .
We’ll return to the simulation of dijet angles…
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ZZ+jets vs. NLO+jets vs. NLODedicated Z+jets measurement. 123070 .,,. jetjet
Tjet yGeVpR
Phys. Rev. Lett. 100, 102001 (2008)
MCFM hiding behind data
points
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•multijets estimated from data samples with leptons that pass only a looser selection (mostly looser isolation)
• “loose-tight”
•several approaches, typically:• W+jets normalized to data before b tagging
• or fitted in signal samples• W+HF fractions normalized to data after b tagging
Normalizing W+jetsNormalizing W+jetsW+jets and W+HF 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
• “anti-electrons”, ”non-isolated”
• Studies with dijet & multijet events• “Method 2” inherited from Run I:
• W+jets normalized to data before b tagging• W+HF fraction fitted to data after b tagging
Assumptions: Kbb=Kcc, and Kc=1 (consistent with studies)
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W+jets Normalization @ CDFW+jets Normalization @ CDF•Three parameter fit to Bottom-Charm-Light templates of jet-flavor separating distributions (NN output, SecVtx mass) in W+2 jet data
•yields KHF=1.4±0.4 • 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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First data driven normalization: single-top evidence analysissingle-top evidence analysisW+jets Normalization @ DØW+jets Normalization @ DØ
For W+jets:• Normalize to pretag data• Channel dependence?
• single-top vs. top-pair analysis• same in e+jets & μ+jets within uncertainties
• Njet dependence?
For W+HF:• Normalize to data after b-tagging
• in 0 b tag bin negligible signal• Defined relative to Alpgen H.F. fraction
PRL 98, 181802 (2007)
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W+jets Normalization @ DØ - IIW+jets Normalization @ DØ - II
• Sensitive to extra backgrounds included
• Sensitive to selection cuts on jets – single top ||<3.4; ttbar ||<2.5
• Consistent between e/μ channels• Studied several cuts on b tag output
• Measured HF scale factor (uncertainty from all deviations observed in the studies):
KHF = 1.170.18
• Used for summer results
Procedure refined for later ttl+jets measurements• tighter selection cuts• normalize MC to match the fraction of 0 tag events in 1 and 2 jet bins
Studies of systematic uncertainties• Separated Wc for W+lp 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
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Normalizing W+jets 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 CDF’s– Analytical work around in D0’s
• Charged Higgs search
W+jets Normalization for BYSMW+jets Normalization for BYSM
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W+jets: Other ReweightingsW+jets: Other ReweightingsSome discrepancies between Alpgen and data are showing up with the increasing statistics.
Corrected 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
22 jjr
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DØ experience with matchingDØ experience with matchingAt 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!It allows us to produce physics results!
Large scale use + cutting edge technology = painOutlining 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, Z+jets 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 matchingDØ experience with matchingUsing matched Alpgen extensively for the last couple of years:• We’ve 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 modelingBTW1: MC in multijet modelingThough 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:
Work in progress – Example of failed model
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BTW2: Detector simulationBTW2: Detector simulationAn 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 @ 15:30, D0 @ 16:00).
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ConclusionsConclusionsSimulation of W+jets and Z+jets• is not trivial• can be analysis dependent:
• One size can almost fit all• Don’t always need the most sophisticated treatment• Several approaches to estimating the heavy flavor contribution• Can fit and/or normalize W+jets• 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 / W+HF / Z+HF• Lepton+jets+MET / di-lepton final states (Z+jets important)
- possible inaccuracies (e.g. Z pT, 2nd jet ΔR)
- 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
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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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Other weightsOther weights
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Matching @ CDFMatching @ CDF
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Calibrating the SimulationCalibrating the SimulationWe 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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In several double-top l+jets “property” analysesIn several double-top l+jets “property” analyses• W+HF normalized to data as in previous slides
• W+jets fit to data – topological likelihood separates top pair from W+jets
Normalizing to Data @ DØ Normalizing to Data @ DØ
Top PairBackgrounds (mostly W+jets)
Example from PRL 100, 062004 (2008)
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Another example: Higgs search in HZHiggs search in HZbbX channelbbX channel• Heavy Flavor in (W/Z)+jet Alpgen+Pythia predictions were
multiplied by K-factors (NLO/LO) calculated with MCFM
• (W/Z)+jets normalized to data before b-tagging
Normalizing W+jets @ DØ HZNormalizing W+jets @ DØ HZ
After After bb-tagging-taggingBefore Before bb-tagging-tagging
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TerminologyTerminologyDØ 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 Z+lp and Z+HF: • Using K’-factors, normalize to theory• Some analyses (e.g. ) override with S-factors
For W+lp: Using S factors
For W+HF: Using S & SHF factors
Xeebbtt
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Know-how IKnow-how I
• Generate 14 samples:– W+bb+Nlp, with N=0,1,2
or ≥3– W+cc+Nlp, with N=0,1,2 or
≥3– W+Nlp, with N=0,1,2,3,4
or ≥5 (includes W+c+jets)
• Individual samples can not be used any more!
Add all parton-jet bins together with weights Fi
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Know-how IIKnow-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:
– W+bb+Nlp, with N=0,1,2 or ≥3
– W+cc+Nlp, with N=0,1,2 or ≥3
– W+Nlp, with N=0,1,2,3,4 or ≥5
cc
cc bb
No skimming
Book keeping is a nightmare!Book keeping is a nightmare!
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From generated MC to dataFrom 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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Z+jets @ CDF - IZ+jets @ CDF - I
FCNC analysis used this for a systematic uncertainty.
negligible – doesn’t appear in the tables
Alpgen qfac=ktfac=1 Alpgen qfac=ktfac=0.5 Alpgen qfac=ktfac=2.0
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MCFM Z+jet @ DØMCFM Z+jet @ DØAn example:
Results for 60-75 GEV
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MCFM W+jets @ DØMCFM W+jets @ DØMethod• 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 W+light 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 W+light k-factor• The last conclusion contradicts our observation from data
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DØ ZDØ Z+jets Normalizations+jets NormalizationsWhen estimating the W+jets background in Lepton+jets+MET channels, we predict Z+jets from the simulation + studies on di-lepton channels.
For Z+lp and Z+HF:• Typically weigh Alpgen+Pythia 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 W+2HF (e.g. )• Evaluate effect of quark masses at LO: 10-40%
bbR ,
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2nd jet angles @ 1 fb2nd jet angles @ 1 fb-1-1
Clearly there’s nothing to be excited about in this plot.Very preliminary analysis of later data shows similar trends.
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DØ ZDØ Z+jets Shape Corrections+jets Shape Corrections
Starting to use ResBos as a surrogate to our data• Reweighting Alpgen+Pythia MC to fit the ResBos spectrum• Applying the lesson from Z+jets to W+jets…
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
arXiv:/0712.0803
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WW++cc measurements measurements
GeVp jetcT 20
52. jetc
DØ Data: Alpgen+Pythia:
jetsWpp
jetcWppR
GeVp jetcT 8
03. jetc
lWBRjetcWpp
Phys. Rev. Lett. 100, 091803 (2008)
arXiv: 0803.2259v1[hep-ex]
Recent MCFM calculations (John Campbell)for DØ cuts yield R(LO)=3.7, R(NLO)=3.4
CDF Data:
MCFM:
(4:4§ 0:3(PDF ))% 9.8§ 2:8(stat:)+1:4¡ 1:6 (syst:) § 0:6(lumi:) pb
11:0+1:4¡ 3:0pb
(7:1§ 1:7)%
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