data-based background predictions using forward events

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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L = 1 fb-1


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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L = 1 fb-1


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

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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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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W, Z, , QCD are lightand so similar to MinBias.

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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/ 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.

data-based background predictions using forward events

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