Data-based background predictions for new particle searches at the LHC

1. Data-based background predictions for new particle searches at the LHC David Stuart Univ. of California, Santa Barbara Texas A&M Seminar March 24, 2010

2. Motivation • Searching for new physics at the LHC. • Potentially fast. • With a large step in energy, the LHC could start up with a bang.

3. Motivation • Searching for new physics at the LHC. • Potentially fast. • But many models; on which to bet? • Do they have something in common?

4. Motivation • Searching for new physics at the LHC. • Potentially fast. • But many models; on which to bet? • Do they have something in common? • (other than being wrong)

5. Motivation • Searching for new physics at the LHC. • Even within 1 model, many parameters… • Signature driven searches are more general. • But, which signature is best?

6. Motivation • Searching for new physics at the LHC. • Search broadly for any non-SM in all signatures?

7. Motivation • Searching for new physics at the LHC. • Search broadly for any non-SM in all signatures? • But signatures are not precisely predicted. • pdfs, higher orders, detector effects… • e.g., Z+jets m+ Z q m-

8. Motivation • Monte Carlo predictions? • Sophisticated, higher order modeling, • e.g., ALPGEN. • Elaborate simulation of detector response.

9. Motivation • Monte Carlo predictions? • Sophisticated, higher order modeling, • e.g., ALPGEN. • Elaborate simulation of detector response. • Both are software…Only trust in so far as validated with data.

10. Motivation • Data validation challenges: • Slow. • Fit away signal?

11. Motivation • Data validation challenges: • Slow. • Fit away signal? • Would be nice to turn off new physics temporarily.

12. A simple discriminator Most new physics is high mass Most SM physics is low mass

13. A simple discriminator • Most new physics is high mass • Produced at threshold, i.e. at rest. • Decay products ≈ isotropic • Decay products peaked at zero rapidity • Most SM physics is low mass •  Produced ≈ uniform in rapidity

14. A simple discriminator Validate SM in forward events and Search for new physics in central events

15. A simple signature • Start with the Z+jets signature • Insert favorite model motivation here. • Clean dilepton signature • Easy to trigger and reconstruct • Very little background

16. A simple signature • Start with the Z+jets signature • Insert favorite model motivation here. • Clean dilepton signature • Easy to trigger and reconstruct • Very little background • …except Z+jets.

17. Z+jets SM falls ≈ exponentially with NJ. Signal would appear at large NJ.

18. Forward control sample SM Z rapidity is ≈ flat since the Z is light. Forward events are a control sample for ≈ all NJ. Signal is central. ALPGEN+Pythia+PYCELL

19. Forward control sample SM Z rapidity is ≈ flat since the Z is light. Forward events are a control sample for ≈ all NJ. Signal is central. After acceptance cuts the conclusion is the same.

20. Method Define the fraction of central events with: R(NJ) = ncentral(NJ) / (ncentral(NJ) + nforwardNJ)) where we define central as |<1 and forward as |>1.3 Measure R(NJ) at low NJ. Extrapolate linear fit to high NJ.

21. Method • Predict number of central events with high NJ as: • ncentral(NJ) = nforward(NJ) * R(NJ) / (1-R(NJ)) { { Measured From low NJ fit. Dominant uncertainty is from fluctuations in nforward(NJ).

22. Does it work? Check self consistency in Monte Carlo… L = 1 fb-1 Predicted Actual

23. Does it work with signal? Not focused on sensitivity to any specific model, but using LM4 as a benchmark: L = 1 fb-1 Predicted w/o signal Predicted w/ signal Actual w/ signal

24. Generalizing The basic premise (low-mass  broad rapidity range) generalizes beyond Z’s.

25. Does it work, generally? Check self consistency in each mode… Predicted Actual Z W  multijets

26. 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 R(NJ) fit will account for it.

27. 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.

28. R(NJ) Beyond using R(NJ) to predict the central yield and count events there, R(NJ) is potentially of general interest as a search variable.

29. R(NJ) The central fraction, R(NJ), is potentially of general interest. “Minbias” example: Here, “NJ” uses tracks above 3 GeV as jet proxies. The highest pT track is the rapidity tag. R(NJ) ≈ 1/2 because tracks flat in  and central ≈ forward for tracking coverage. Changing  bounds would move R(NJ) but not change its shape. R(NJ)

30. R(NJ) The central fraction, R(NJ), is potentially of general interest. W and Z are light and so similar to Minbias. Acceptance difference apparent.

31. R(NJ) The central fraction, R(NJ), is potentially of general interest. W and Z are light and so similar to Minbias. Acceptance difference apparent. +jets and jet+jets are non-flat but still linear.

32. R(NJ) The central fraction, R(NJ), is potentially of general interest. W and Z are light and so similar to Minbias. Acceptance difference apparent. +jets and jet+jets are non-flat but still linear. SUSY model points are dominantly central.

33. R(NJ)(-1) We have also 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. R(NJ)(-1) = ncentral(NJ) / (ncentral(NJ) + nforward(NJ-1))

34. R(NJ)(-1) We have also 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

35. R(NJ)(-2) Can “leverage” that to use the forward events from two jet bins previous. Z+jets Z+jets plus LM4 ≈ S2 This really just represents our generic expectation that for the SM, NJ should ≈ fall exponentially and be uniform in rapidity, while for a heavy particle production is central and increases with NJ. Similar plots can be made for , jet, W.

36. What about Missing ET? Would like to predict V+jets+MET for a Supersymmetry search. Is there a SUSY-less sample from which to measure MET?

37. Missing ET in Z+jets The Z is well measured. The MET comes from the detector’s response to the jet system.

38. Missing ET in Z+jets For each Z+jet event, find an event w/ a comparable jet system and use its MET as a prediction. Huge QCD x-section makes such events SUSY free.

39. Missing ET in Z+jets For each Z+jet event, use a MET template measured from events with a comparable jet system in O(1) pb-1. Templates measured in bins of NJ and JT = Sj ET.

40. Missing ET in Z+jets Example of template parameterization For each data event... Data distribution Background prediction

41. Missing ET in Z+jets Example of template parameterization For each data event, look up the appropriate template. Sum these, each with unit normalization, to get the full background prediction Data distribution Background prediction

42. Missing ET in Z+jets, MC closure test

43. Missing ET in Z+jets, MC closure test

44. Missing ET in Z+jets, MC closure test

45. Missing ET in Z+jets, MC closure tests “Scaled” includes a low MET normalization, which is important for low NJ.

46. Missing ET in g+jets, MC closure test

47. Missing ET in g+jets, MC closure test

48. Missing ET in g+jets, MC closure test

49. Missing ET in g+jets, MC closure tests “Scaled” includes a low MET normalization, which is important for low NJ.

50. Missing ET in Z/g+jets, robustness tests Various detector effects could add MET tails. Check robustness with MC tests, applied equally to all samples.