Data-based background predictions using forward events
Victor Pavlunin and David Stuart
University of California
Santa Barbara
July 10, 2008
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MotivationWe are interested in signature specific model independent searches, e.g., Z+jets.
Challenge is suppressing and predicting the SM Z+jets background.
Modeling uncertainties from:
NNNLO, PDFs, detector response, jet energy scale and bugs.
Only trust Monte Carlo as far is it can be validated with data.
Validate background with a control sample that has little signal contamination.
and/or
Measure background with a control sample that has little signal contamination.
E.g., Z+0jets, Z+1jet, or Z+multijets with low jet thresholds or low Z pT.
We have been exploring a method that uses forward events as a background dominated sample to validate and measure the SM background.
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Motivating Forward
Rapidity is flat for production of a low mass particle, e.g., of pions in Minbias
SM Z rapidity is ≈ flat since the Z is light.
By contrast, a Z produced in decays of a massive particle will be centrally peaked.
Use forward events with forward Z’s to predict the SM background in events with central Z’s.
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Motivating Forward
Rapidity is flat for production of a low mass particle, e.g., of pions in Minbias
SM Z rapidity is ≈ flat since the Z is light.
By contrast, a Z produced in decays of a massive particle will be centrally peaked.
Use forward events with forward Z’s to predict the SM background in events with central Z’s.
After acceptance cuts the conclusion is the same.
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MethodDefine the fraction of central events with:
RNJ = NJCentral / (NJ
Central + NJForward)
where we define central and forward splitting at |=1.3
Fit RNJ as a function of the number of jets.
Prediction high NJ central events from the number of forward events with high NJ and the fit prediction at high NJ.
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Does it work?Check self consistency in Monte Carlo…
Predicted
Actual
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Does it work?Check self consistency in other Monte Carlo…
Predicted
Actual
Z W
multijets
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Does it work robustly?Check for robustness against mis-modeling. E.g.,
• Eta dependence of lepton efficiencies.• Eta dependence of jet efficiencies.• Changes in higher order Monte Carlo effects.
Expect robustness since data-based prediction:
• Measures lepton efficiencies in the low NJ bins
• Measures jet effects in events with forward Z’s.
• Measures NJ dependence in the fit.
As long as correlations between lepton and jet effects are a slowly varying function of NJ, the RNJ fit will account for it.
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Does it work robustly?Tests with artificially introduced mis-modeling.
Z W j
Alpgen #partons Lepton inefficiencies Jet inefficiencies
Pulls are shown for two highest ET jet bins for each test.Alpgen test = even #partons only and odd #partons only.Lepton test = 30% efficiency changes globally and forward only.Jet test = 30% efficiency changes globally and forward only.
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Missing ETIn addition to a generic Z+jets search, one could require MET.
Modeling the MET is difficult, but forward events can measure it.
We test this with artificially introduced jet mis-measurements:
• Introduce holes in jet acceptance.
• Smear jet energy according to a pdf.
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Missing ET robustnessWe expect robustness with MET because the method measures the effect of MET with forward events. That measurement is invalid only if there is a correlation between the Z and the MET, which is less true at large NJ.
Z W j
Alpgen #partons Jet holes Jet resolution tails
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SensitivityNot focused on sensitivity to any specific model
(more focused on insensitivity to any mis-modeling).
But, using LM4 as a benchmark:
L = 1 fb-1
Predicted w/o signal
Predicted w/ signal
Actual w/ signal
Without MET cut.
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SensitivityNot focused on sensitivity to any specific model
(more focused on insensitivity to any mis-modeling).
But, using LM4 as a benchmark:
L = 1 fb-1
Predicted w/o signal
Predicted w/ signal
Actual w/ signal
Without MET cut.
MET is not powerful at high NJ, as expected. But prediction remains valid.
With MET cut.
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SensitivityNot focused on sensitivity to any specific model
(more focused on insensitivity to any mis-modeling).
But, using LM4 as a benchmark:
L = 1 fb-1
Predicted w/o signal
Predicted w/ signal
Actual w/ signal
Without MET cut.
Note that signal contribution would bias the RNJ fit for NJ>3.
The forward events remain signal free, but central events are “contaminated”.
With MET cut.
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W+jetsAs shown already, this approach can also be used for predicting the W+jets background.
W
Predicted
Actual
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W+jetsAs shown already, this approach can also be used for predicting the W+jets background.
W
Predicted
ActualPredicted
Actual
But, the ttbar contribution is dominantly central, because top is heavy and produced mostly at rest.
This biases the prediction if we use NJ>2, for the same reason that SUSY biased Z+jets for NJ>3.
Since top and Mtop are large, it is a significant central background.
top
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W+jetsAs shown already, this approach can also be used for predicting the W+jets background.
Fitting with NJ<3 gives a prediction for the W+jets background to a top signal.
This is a SM sample to validate the effectiveness of the method in the presence of a signal.(See, e.g., a related CDF measurement in Phys.Rev.D76:072006,2007).
Predicting W+jets and ttbar together is more complicated because ttbar is heavy. Another talk…
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RNJThe central fraction, RNJ, is potentially of general interest.
E.g., min bias is 1/2because flat in .
Here, “NJ” uses tracksabove 3 GeV as jet proxies.
The highest pT track is therapidity tag.
Minbias
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RNJThe central fraction, RNJ, is potentially of general interest.
W, Z, , QCD are lightand so similar to MinBias.
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RNJThe central fraction, RNJ, is potentially of general interest.
W, Z, , QCD are lightand so similar to MinBias.
Top and SUSY are heavyand central.
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RNJ(-1)
Finally, we have explored another variable that tries to take advantage of the general expectation that the NJ spectrum should be falling.
L = 1 fb-1
Predicted w/o signal
Predicted w/ signal
Actual w/ signal
Without MET cut.
Clear signal when there is an increase with NJ, or even a decrease in the slope.
RNJ(-1) = NJ
Central / (NJCentral + NJ-1
Forward)
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RNJ(-1)
Finally, we have explored another variable that tries to take advantage of the general expectation that the NJ spectrum should be falling.
Z+jetsZ+jets plus LM4
≈ S
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RNJ(-2)
Can “upgrade” that to use the forward events from two jet bins previous.
Z+jetsZ+jets plus LM4
≈ S2
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Summary
We have explored a data-based background prediction that:
• Attempts to avoid generator and detector modeling uncertainties by measuring a ratio.
• Takes advantage of the fact that the SM is light at the LHC, so it is ≈ uniform in rapidity.
• Consistency checks find that it fails to discover anything
that it shouldn’t, even when reality bites.
• Find that the central fraction could be generally useful in understanding signals.
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