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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. Motivation. We are interested in signature specific model independent searches, e.g., Z+jets.

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Data-based background predictions using forward events

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  1. Data-based background predictions using forward events Victor Pavlunin and David Stuart University of California Santa Barbara July 10, 2008

  2. Motivation • We 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.

  3. 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.

  4. 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. After acceptance cuts the conclusion is the same.  Use forward events with forward Z’s to predict the SM background in events with central Z’s.

  5. Method Define the fraction of central events with: RNJ = NJCentral / (NJCentral + 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.

  6. Does it work? Check self consistency in Monte Carlo… Predicted Actual

  7. Does it work? Check self consistency in other Monte Carlo… Predicted Actual Z W  multijets

  8. 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.

  9. Does it work robustly? Tests with artificially introduced mis-modeling. Z W j  Jet inefficiencies Lepton inefficiencies Alpgen #partons 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.

  10. Missing ET • In 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.

  11. Missing ET robustness We 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  Jet resolution tails Jet holes Alpgen #partons

  12. Sensitivity Not focused on sensitivity to any specific model (more focused on insensitivity to any mis-modeling). But, using LM4 as a benchmark: Without MET cut. L = 1 fb-1 Predicted w/o signal Predicted w/ signal Actual w/ signal

  13. Sensitivity Not focused on sensitivity to any specific model (more focused on insensitivity to any mis-modeling). But, using LM4 as a benchmark: Without MET cut. L = 1 fb-1 Predicted w/o signal Predicted w/ signal Actual w/ signal With MET cut. MET is not powerful at high NJ, as expected. But prediction remains valid.

  14. Sensitivity Not focused on sensitivity to any specific model (more focused on insensitivity to any mis-modeling). But, using LM4 as a benchmark: Without MET cut. L = 1 fb-1 Predicted w/o signal Predicted w/ signal Actual w/ signal With 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”.

  15. W+jets As shown already, this approach can also be used for predicting the W+jets background. Predicted Actual W

  16. W+jets As shown already, this approach can also be used for predicting the W+jets background. Predicted Actual Predicted Actual W top 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.

  17. W+jets As 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…

  18. RNJ The central fraction, RNJ, is potentially of general interest. Minbias E.g., min bias is 1/2 because flat in . Here, “NJ” uses tracks above 3 GeV as jet proxies. The highest pT track is the rapidity tag.

  19. RNJ The central fraction, RNJ, is potentially of general interest. W, Z, , QCD are light and so similar to MinBias.

  20. RNJ The central fraction, RNJ, is potentially of general interest. W, Z, , QCD are light and so similar to MinBias. Top and SUSY are heavy and central.

  21. RNJ(-1) Finally, we have explored another variable that tries to take advantage of the general expectation that the NJ spectrum should be falling. Without MET cut. L = 1 fb-1 Predicted w/o signal Predicted w/ signal Actual w/ signal Clear signal when there is an increase with NJ, or even a decrease in the slope. RNJ(-1) = NJCentral / (NJCentral + NJ-1Forward)

  22. 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+jets Z+jets plus LM4 ≈ S

  23. RNJ(-2) Can “upgrade” that to use the forward events from two jet bins previous. Z+jets Z+jets plus LM4 ≈ S2

  24. 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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