International Workshop on Top Quark Physics La Biodola, Isola d’Elba, Italy 18-24 May, 2008. MC for top background at the Tevatron. Amnon Harel [email protected] University of Rochester.
MC for top backgroundat the Tevatron
Amnon [email protected]
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
Reweighting the MC
The cost of matching
Hard to simulate misreconstructions Data driven estimation
Small A rough simulation suffices
Haven’t discovered that one yet
H.F. fractions of W+jets crucial
What have we measured? Though conclusions from W+HF measurements not yet applied to top analyses
CDF preliminary from Monday(1.9fb-1):
Harsh b-tagging and MET (>25GeV) selections
Alpgen prediction: 0.78pb
DØ preliminary from 2005(382pb-1):
, , &
Phys. Rev. Lett. 100, 091803 (2008)
Recent MCFM calculations (John Campbell)
for DØ cuts yield R(LO)=3.7, R(NLO)=3.4
Dedicated W+jets measurement.
SMPR = CKKW matched Madgraph + Pythia
MLM = MLM matched Alpgen + Herwig
MCFM = NLO predictions
will be a recurring theme
For top physics, we usually simulate W+jets (also Z+jets) using MLM matched Alpgen. Implementations differ:
Sherpa uses CKKW matching
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)
Again Pythia spectrum is too soft
Again Pythia spectrum is too soft
We’ll return to the simulation of dijet angles…
Dedicated Z+jets measurement.
Phys. Rev. Lett. 100, 102001 (2008)
MCFM hiding behind data points
W+jets and W+HF are normalized to data, after other backgrounds (multijets, dibosons, etc.) are subtracted.
Assumptions: Kbb=Kcc, and Kc=1 (consistent with studies)
PRL 98, 181802 (2007)
First data driven normalization: single-top evidence analysis
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 resultsW+jets Normalization @ DØ - II
Procedure refined for later ttl+jets measurements
Studies of systematic uncertainties
Normalizing W+jets to data with the same jet multiplicity as signal is not trivial for BYSM searches, or even for cross section measurements.
Some 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
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.
Example of bad agreement from recent DØ studies:
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.
Matching @ CDF
In several double-top l+jets “property” analyses
Example from PRL 100, 062004 (2008)
Backgrounds (mostly W+jets)
Another example: Higgs search in HZbbX channel
Add all parton-jet bins together with weights Fi
Book keeping is a nightmare!
Add showering and hadronization from Pythia
underlying event model
Run full D0 detector simulation and reconstruction
Add zero bias events to match luminosity profile in data
Apply to simulated events:
Smear jets, electrons and muon
Propagate to missing ET
Electron and muon ID efficiency
Tag rate function
Reweightings: lumi, z vertex, etcFrom generated MC to data
Results for 60-75 GEV
Clearly there’s nothing to be excited about in this plot.
Very preliminary analysis of later data shows similar trends.