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

7. 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 q Z ++ --

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

9. 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. 10 Motivation Data validation challenges: Slow. Fit away signal?

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

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

13. 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. 14 A simple discriminator Validate SM in forward events and Search for new physics in central events

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

16. 16 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. A simple signature

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

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

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

20. 20 Method Define the fraction of central events with: R(N J ) = n central (N J ) / (n central (N J ) + n forward N J )) where we define central as |  1.3 Measure R(N J ) at low N J. Extrapolate linear fit to high N J.

21. 21 Method Predict number of central events with high N J as: n central (N J ) = n forward (N J ) * R(N J ) / (1-R(N J )) From low N J fit. { { Measured Dominant uncertainty is from fluctuations in n forward (N J ).

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

23. 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. 24 Generalizing The basic premise (low-mass  broad rapidity range) generalizes beyond Z’s.

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

26. 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 N J bins Measures jet effects in events with forward Z’s. Measures N J dependence in the fit. As long as correlations between lepton and jet effects are a slowly varying function of N J, the R(N J ) fit will account for it.

27. 27 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 E T 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. 28 R(N J ) Beyond using R(N J ) to predict the central yield and count events there, R(N J ) is potentially of general interest as a search variable.

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

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

31. 31 R(N J ) The central fraction, R(N J ), 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. 32 R(N J ) The central fraction, R(N J ), 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. 33 R(N J ) (-1) We have also explored another variable that tries to take advantage of the general expectation that the N J 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 N J, or even a decrease in the slope. R(N J ) (-1) = n central (N J ) / (n central (N J ) + n forward (N J-1 ))

34. 34 R(N J ) (-1) We have also explored another variable that tries to take advantage of the general expectation that the N J spectrum should be falling. Z+jets Z+jets plus LM4 ≈  S

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

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

37. 37 Missing E T in Z+jets The Z is well measured. The MET comes from the detector’s response to the jet system.

38. 38 Missing E T 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. 39 Missing E T 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 N J and J T =  j E T.

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

41. 41 Missing E T in Z+jets Example of template parameterization Background prediction Data distribution For each data event, look up the appropriate template. Sum these, each with unit normalization, to get the full background prediction N JETS pT>50 GeV sumET Bin 1 sumET Bin 2 sumET Bin 3 sumET Bin 4… 2 3 4…

42. 42 Missing E T in Z+jets, MC closure test

43. 43 Missing E T in Z+jets, MC closure test

44. 44 Missing E T in Z+jets, MC closure test

45. 45 Missing E T in Z+jets, MC closure tests “Scaled” includes a low MET normalization, which is important for low N J.

46. 46 Missing E T in  +jets, MC closure test

47. 47 Missing E T in  +jets, MC closure test

48. 48 Missing E T in  +jets, MC closure test

49. 49 Missing E T in  +jets, MC closure tests “Scaled” includes a low MET normalization, which is important for low N J.

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

51. 51 Missing E T in Z/  +jets, robustness tests  R=0.8 hole at (h,f)=(0,0) Double gaussian smearing Randomly add 50-100 GeV “noise jets” Vary n J slope by ±50%. Jet energy scale sensitivity.

52. 52 Missing E T in W(  )+jets W Can predict W+jets, with forward/central, but not tt  W+jets because top is heavy.

53. 53 Missing E T in W(  )+jets W Templates can predict the fake MET in W+jet events, but we also need to predict the real MET, i.e., the p T. Can predict W+jets, with forward/central, but not tt  W+jets because top is heavy.

54. 54 Missing E T in W(  +jets W Templates can predict the fake MET in W+jet events, but we also need to predict the real MET, i.e., the p T. But, p T spectrum is ≈ same as  p T spectrum, if we ignore V-A or randomize W polarization. Can predict W+jets, with forward/central, but not tt  W+jets because top is heavy.

55. 55 Missing E T in  +jets Pretend we could detect and apply templates. Mismatch due to b-jet dominance. But, neutrino p T dominates MET.

56. 56 Missing E T in  +jets Combining template prediction with  p T spectrum gives a prediction for the full MET distribution.

57. 57 Missing E T in  +jets The same approach predicts W shape, if polarization is random.

58. 58 Comparison with signal Benchmark points (LM4 and LM1) stand out with 200/pb at 14 TeV. LM4=(m 0 =210,m 1/2 =285); LM1=(60,250). tan(  )=10.

59. 59 Summary Explored data-based background predictions that avoid reliance on MC. Rapidity is a simple discriminator that relies only on kinematics. It provides a data-based background prediction that: Avoids generator and detector modeling uncertainties by measuring a ratio. Fails to discover anything that it shouldn’t, even when reality bites. QCD based templating gives an in situ prediction of MET distribution. Charged lepton p T predicts neutrino p T. We will validate these methods with low N J data soon. Work done by Victor Pavlunin. More details are available in: PRD78:035012 arXiv:0806.2338 & PRD81:035005 arXiv:0906.5016

61. Standard Model backgrounds to Z+jets + + + q Z e+e+ e-e-