Search for b Ø bz. CDF note Adam Scott, David Stuart UCSB. 1 Exotics Meeting. Blessing

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1 Search for b Ø bz CDF note 8465 Adam Scott, David Stuart UCSB Exotics Meeting Blessing 1 Exotics Meeting

2 Analysis in a Nutshell Looking for new particles decaying to Z+jets Select Z s in the dielectron and dimuon channels Standard tight leg/loose leg cuts 81 < M ll < 1 GeV N jet30 3 J T > X, where we scan X in 50 GeV steps Developed and used a fit procedure to get the background from data alone (Data - Fit)/Error Events/GeV Z ee and µµ data, 1.1 fb rd 3 highest jet E T (GeV) Param # Param name Param name value value ave error error - error + error 01 Norm. factor Exp. param 4.37e / e Exp. param / Power Power param param / chi2 ndf chi2/ndf Probability / 12 = Exotics Meeting

3 Looking for new particles decaying to Z+jets Select Z s in the dielectron and dimuon channels Standard tight leg/loose leg cuts 81 < M ll < 1 GeV N jet30 3 J T > X, where we scan X in 50 GeV steps Developed and used a fit procedure to get the background from data alone Analysis in a Nutshell Events/50 GeV Events/50 GeV 3 Exotics Meeting 2 CDF Run II Preliminary, 1.1 fb Data Nominal prediction error band (GeV) CDF Run II Preliminary, 1.1 fb Data J T Nominal prediction error band (GeV) J T

4 Changes Changes to this analysis (only one change): Evaluated acceptance uncertainties for limit (went over in VEP meeting) Source Luminosity Monte Carlo Statistics Jet Energy Scale PDF s I/F SR Lepton ID Total Uncertainty 5.9 % 2.4 % 6.7 % 0.5 % < 2.4 % 3 % % 4 Exotics Meeting

5 Updated Limit Acceptance uncertainty of % does very little to limit Background uncertainty, ~35%, dominates ] (pb) 2 bz)) σ [ 1-(1-BR(b 2 limit, no acceptance uncert limit, with acceptance uncert. theoretical cross section b mass (GeV) 5 Exotics Meeting

6 Bkg. Prediction Compared to Pythia Ben K. asked me to compare my background prediction to other Monte Carlo s, e.g. Pythia Not surprisingly, Pythia underpredicts the background Total Pythia prediction: 43.5 events Data: 80 p-value: 4.6E-7 Events/50 GeV Events/50 GeV 6 Exotics Meeting 2 Data CDF Run II Preliminary, 1.1 fb Nominal prediction error band Pythia Prediction (GeV) CDF Run II Preliminary, 1.1 fb 50 Data -1-1 J T Nominal prediction error band Pythia Prediction (GeV) J T

7 ] (pb) 2 bz)) σ [ 1-(1-BR(b Really, I want to bless the analysis as a whole Want to starting publication process now Regardless, here are a few plots for others to show at conferences -1 CDF Run II Preliminary, 1.1 fb 2 95% CL limit Plots to Bless Theoretical LO calculation b mass (GeV) Events/50 GeV Events/50 GeV 7 Exotics Meeting 2 CDF Run II Preliminary, 1.1 fb Data Nominal prediction error band (GeV) CDF Run II Preliminary, 1.1 fb Data J T Nominal prediction error band (GeV) J T

8 Conclusion Searching for Z+X, where here X is multijets from the decay of massive objects Main challenge is a trustworthy Z+jets background prediction Developed and validated a background prediction that uses only data Details in CDF note 8465 Unblinded, saw no excess Set limit Only small changes to analysis since pre-blessing 8 Exotics Meeting

9 9 Exotics Meeting

10 Backup, Previous Talks Exotics Meeting

11 Search for b Ø bz CDF note 8465 Adam Scott, David Stuart UCSB Exotics Meeting Preblessing 11 Exotics Meeting

12 Z 0 + X Many possible discriminators Previous work includes: Z+b tag (focused b search) J. G. da Costa et. al. (Run I) L xy J.R. Peterson et. al. (Run I) A. Scott et. al. (Run II) Z s with high p T or other final states A. Paramonov et. al. ZZ resonances B. Brau et. al. ZH searches Electroweak cross section measurements E.g. Z+g 12 Exotics Meeting This analysis: Beats down SM bkg by requiring jets in conjunction with the Z

13 Selection Select Z s in the dielectron and dimuon channels Standard tight leg/loose leg cuts 81 < M ll < 1 GeV N jet30 3 J T > X, where we scan X in 50 GeV steps Backgrounds Standard Model Z+jets Standard Model WZ/ZZ+jets Standard Model tt + jets QCD multi-jet events with two fake leptons Multi-jet events with a cosmic 13 Exotics Meeting

14 Background Prediction Need to predict: Number of background events in N jet30 3 region Shape of J T QCD and cosmic background estimable with data Largest background is from Z+jets Unreliable modeling using Monte Carlo Pythia does not have higher orders Higher order calculations don t model the data well in control region either Common problem finding background in higher N jet bins E.g., top discovery Try to find a way to estimate it from data 14 Exotics Meeting

15 Background Prediction Need to predict: Total number of background events in N jet30 3 region Shape of J T distribution 15 Exotics Meeting

16 Fit to N jet Distribution? Need to predict: Total number of background events in N jet30 3 region Shape of J T distribution Possible to fit N jet30 distribution? No difficult to figure out the correct parameterization Only have 3 data points: 0, 1 and 2 jet bins Large systematic on simple exponential parameterization 16 Exotics Meeting

17 Jet E T distribution Need to predict: Total number of background events in N jet30 3 region Shape of J T distribution The number of jets are counted above an E T threshold N jet distribution is completely determined from the jet E T distribution New idea: use the jet E T spectrum to find the background in the N jet30 3 region 17 Exotics Meeting

18 Jet E T distribution Need to predict: Total number of background events in N jet30 3 region Shape of J T distribution Which jet E T distribution? Plot the 3 rd highest jet E T distribution for events with N jet Exotics Meeting

19 Jet E T distribution Need to predict: Total number of background events in N jet30 3 region Shape of J T distribution Which jet E T distribution? Plot the 3 rd highest jet E T distribution for events with N jet30 2 And N jet30 3 Separates jet E T distribution at E T = 30 GeV Can thus extrapolate the E T (jet 3) spectrum from E T < 30 to E T > 30 to obtain the total background in the N jet30 3 region 19 Exotics Meeting

20 Fit the Jet E T distribution for N jet30 Bkg. Need to predict: Total number of background events in N jet30 3 region Shape of J T distribution Use parameterization determined and validated using Monte Carlo and data Clear from the plot that the fit predicts the jet E T distribution pretty well in the region E T > 30 Fit predicts: Bkg = Observed in Monte Carlo: Bkg = 19.8 ± 1.6 Consistent at 2.3s level 20 Exotics Meeting

21 Fit the Jet E T distribution for N jet30 Bkg. Need to predict: Total number of background events in N jet30 3 region Shape of J T distribution 21 Exotics Meeting

22 Need to predict: Total number of background events in N jet30 3 region Shape of J T distribution J T Shape We also want to predict the J T shape in the N jet30 3 region J T = Sum of jets E T s J T is simply the sum of the jets E T So, we only need to find the jet E T distribution in the N jet30 3 bins How does the jet E T shape change vs. N jet30 bin? 22 Exotics Meeting

23 Jet E T Shape vs. N jet Need to predict: Total number of background events in N jet30 3 region Shape of J T distribution How does the jet E T shape change vs. N jet30 bin? In Monte Carlo and (blinded) data, the high E T tail gets harder in the higher N jet30 bins We model this by extrapolating a fit parameter linearly 23 Exotics Meeting

24 Jet E T Shape vs. N jet Need to predict: Total number of background events in N jet30 3 region Shape of J T distribution How does the jet E T shape change vs. N jet30 bin? In Monte Carlo and (blinded) data, the high E T tail gets harder in the higher N jet30 bins We model this by extrapolating a fit parameter linearly Fit in N jet30 = 1 bin 24 Exotics Meeting

25 Jet E T Shape vs. N jet Need to predict: Total number of background events in N jet30 3 region Shape of J T distribution How does the jet E T shape change vs. N jet30 bin? In Monte Carlo and (blinded) data, the high E T tail gets harder in the higher N jet30 bins We model this by extrapolating a fit parameter linearly Fit in N jet30 = 2 bin 25 Exotics Meeting

26 Jet E T Shape vs. N jet Need to predict: Total number of background events in N jet30 3 region Shape of J T distribution How does the jet E T shape change vs. N jet30 bin? In Monte Carlo and (blinded) data, the high E T tail gets harder in the higher N jet30 bins We model this by extrapolating a fit parameter linearly Parameter Extrapolation 26 Exotics Meeting

27 J T Prediction Need to predict: Total number of background events in N jet30 3 region Shape of J T distribution We now have a jet E T distribution prediction in each N jet30 bin If we had an estimate for the relative number of events with N jet30 = 3, 4, 5, we would have all the information required for the J T shape in the N jet30 3 bins Can fit the N jet30 distribution to get this estimate The J T normalization is still determined by the 3 rd highest E T jet fit 27 Exotics Meeting

28 J T Prediction Need to predict: Total number of background events in N jet30 3 region Shape of J T distribution Log scale Can run toy Monte Carlo to get the J T prediction in the N jet30 3 bins Shoot a random value of N jet œ [3, ] from the N jet fit If N jet = 3, generate 3 jet E T s from the N jet = 3 jet E T distribution extrapolation Linear scale Sum these jet E T s to get the J T Normalize J T shape using the 3 rd highest E T jet fit Get the J T uncertainties by changing fit parameter within their errors and rerunning the toy 28 Exotics Meeting

29 J T Prediction Need to predict: Total number of background events in N jet30 3 region Shape of J T distribution Log scale Can run toy Monte Carlo to get the J T prediction in the N jet30 3 bins Shoot a random value of N jet œ [3, ] from the N jet fit If N jet = 3, generate 3 jet E T s from the N jet = 3 jet E T distribution extrapolation Linear scale Sum these jet E T s to get the J T Normalize J T shape using the 3 rd highest E T jet fit Get the J T uncertainties by changing fit parameter within their errors and rerunning the toy 29 Exotics Meeting

30 Fit Validation QCD JET20 Fit developed using Monte Carlo Would like to verify that the fit works on data Can t use Z+jet data because of signal bias Fit only uses jet information Try using other X+jet data Try using a QCD sample: JET20 triggers Fake Z made from 2 jets Integrated over J T : Prediction: Bkg = Observed: Bkg = 165 Consistent at 0.4s level Events/50 GeV Events/50 GeV 30 Exotics Meeting JET20 data nominal fit error band (GeV) 30 J 00 T JET20 data nominal fit error band (GeV) J T

31 Fit Validation QCD Lepton+fake Fit developed using Monte Carlo Would like to verify that the fit works on data Can t use Z+jet data because of signal bias Fit only uses jet information Try using other X+jet data Another X+jet QCD sample Invert ID cuts on one lepton leg Integrated over J T : Prediction: Bkg = Observed: Bkg = 4509 Consistent at 0.1s level Events/50 GeV Events/50 GeV 31 Exotics Meeting Electron+fake data nominal fit error band (GeV) J T Electron+fake data nominal fit error band (GeV) J T

32 Fit Validation W+jets/Top Another X+jet sample: W+jets This sample has a real signal in it, from top Good test that method finds a signal in data Standard cuts: Single muon w/ p T > 20 GeV Missing E T > 25 GeV This is the Lepton+jets channel, with no b-tagging Same N jet modeling problem as Z+jet data In N jet30 distribution, there is an excess of data at high N jet30 But also at N jet30 = 2, where very little top is expected Events/GeV Events 32 Exotics Meeting W+jet data (GeV) tt W+jet Monte Carlo Try to use fit procedure to predict -1 W+jets background in N jet30 > E T W+jet data tt W+jet Monte Carlo E T N jet

33 Fit Validation W+jets/Top 3 rd highest E T jet fit Monte Carlo Events/GeV rd 3 highest jet E T (GeV) (Data - Fit)/Error Param # Param name Param name value value ave error error - error + error 01 Norm. factor Exp. param e / e Exp. param / Power Power param param / chi2 ndf chi2/ndf Probability / 7 = Exotics Meeting

34 Fit Validation W+jets/Top 3 rd highest E T jet fit Data Events/GeV rd 3 highest jet E T (GeV) (Data - Fit)/Error Param # Param name Param name value value ave error error - error + error 01 Norm. factor Exp. param 4.432e / e Exp. param / Power Power param param / chi2 ndf chi2/ndf Probability.2134 / 7 = Exotics Meeting

35 3 rd highest E T jet fit Data Prediction: Observed: 762 Excess: Fit Validation W+jets/Top Assuming excess from top, take Ae from MC: Ae = % L = 36/pb s = pb (stat only) (Data - Fit)/Error Events/GeV rd 3 highest jet E T (GeV) Param # Param name Param name value value ave error error - error + error 01 Norm. factor Exp. param 4.432e / e Exp. param / Power Power param param / chi2 ndf chi2/ndf Probability.2134 / 7 = Exotics Meeting

36 Fit Validation W+jets/Top 3 J T shape consistent with background prediction from fit + shape from top MC Top MC normalized to measured cross section of 9.1 pb Fit works in: fl Monte Carlo samples fl Data, all-background samples fl Data, with signal Events/50 GeV Events/50 GeV W+jet data tt Prediction from fit (GeV) J T W+jet data tt Prediction from fit 36 Exotics Meeting (GeV) J T

37 Remaining Backgrounds Muon channel Most other backgrounds easy to evaluate with Monte Carlo There are also cosmics and QCD bkg. We evaluate cosmic bkg. using timing information from the COT We evaluate QCD bkg. using the sidebands of M ll It has same shape as Z+jet bkg, so it will be included in the fit See note for details Z+jets background dominates Electron channel 37 Exotics Meeting

38 Changes Since Full-Status Electron and muon channel combination Changed systematic errors on prediction 38 Exotics Meeting

39 Electron and Muon Channel Combination Two (easy) possible ways to combine channels: Add histograms after doing fits in each channel separately Do fit procedure once on combined sample We do the fit procedure once, but the two give the same result Events/50 GeV 2 combined fit nominal value 1-1 combined fit error band simple sum nominal value simple sum error band (GeV) J T 39 Exotics Meeting

40 Prediction Error Change Total error on prediction is combination of: Fit statistical error Change fit parameters within their errors and re-run the toy Fit systematic error Vary fit ranges, re-fit, and rerun the toy This systematic method can inflate the error if new fit ranges are chosen to be too small 40 Exotics Meeting

41 Prediction Error Change Split Monte Carlo up into subsets (each corresponds to about 1/fb of lumi) Found pull distribution RMS consistent one with fit statistical error only In Monte Carlo, systematics dominating the error Changed two fit range variations that dominated the J T shape error in Monte Carlo 3 rd highest E T jet fit: Old: [15,30] Ø [20,30] New: [15,30] Ø [17,30] N jet30 = 2 fit: Old: [30, ] Ø [60, ] New: [30, ] Ø [50, ] Events/50 GeV 41 Exotics Meeting Mean pull RMS Underflow 0 Overflow 0 Integral nominal prediction new fit ranges old fit ranges (GeV) J T

42 Background prediction Unblinding Events/50 GeV Nominal prediction error band Events/50 GeV (GeV) J T Nominal prediction error band 42 Exotics Meeting (GeV) J T

43 Background prediction Unblinding Events/50 GeV 2 CDF Run II Preliminary, 1.1 fb Data Nominal prediction error band And the data (For blessing) 30 J (GeV) T Events/50 GeV CDF Run II Preliminary, 1.1 fb Data Nominal prediction error band 43 Exotics Meeting (GeV) J T

44 Significance Scanning the J T cut over the distribution: 44 Exotics Meeting

45 Limit Set limit by requiring (chosen a priori): J T > m b To do: evaluate systematic error on b acceptance Cross Section (pb) 2 limit, no acceptance uncert. 1 theoretical cross section b mass (GeV) 45 Exotics Meeting

46 Limit Set limit by requiring (chosen a priori): J T > m b To do: evaluate systematic error on b acceptance Cross Section (pb) 2 limit, no acceptance uncert. 1 limit, 20% acceptance uncert. theoretical cross section b mass (GeV) 46 Exotics Meeting

47 Conclusion Searching for Z+X, where here X is multijets from the decay of massive objects Main challenge is a trustworthy Z+jets background prediction Developed and validated a background prediction that uses only data Details in CDF note 8465 Unblinded, saw no excess Set limit 47 Exotics Meeting

48 Backup 48 Exotics Meeting

49 Interested in new physics that couples to Z 0 Using dielectron and dimuon channels Study in the context of the 4 th generation model: b b Z Z 0 + X Need to understand how to discriminate between signal and Standard Model Signal lies orders of magnitude below SM background Need to find discriminators that are reject this background but keep signals 49 Exotics Meeting

50 Optimization of N jet and J T To thoroughly answer these questions, performed sensitivity scan on jet E T thresholds and J T All possible square cuts on jet E T s and J T Steps of GeV on jet E T s Best possible sensitivity Simple cuts Steps of 50 GeV on J T Did for b masses in GeV range Compared absolute best sensitivity to sensitivity of simple cuts, with uniform jet E T threshold of 30 GeV 50 Exotics Meeting

51 For all masses except 0, sensitivity of uniform 30 GeV jet threshold, with an optimized J T cut, was close to best possible sensitivity For m=0 GeV, the sensitivity found was still pretty good: S = 65 events B = 20 events Interested in higher masses Final kinematic selection: N jet30 3 Mass-dependent J T cut I.e., use shape of J T 51 Exotics Meeting

52 Parameterization Motivation Given p 1 and p 2 what is M? Not necessarily in CM frame Simple calculation using 4- momentum conservation: M 2 = Hp 1 + p 2 L 2 -Hp 1 - p 2 L 2 To get cross section, have to weight by 1/s = 1/M 2 (Generic behavior of cross sections) Make toy MC, taking p 1 and p 2 from PDF from hep-ph/ : 52 Exotics Meeting

53 Parameterization Motivation Given p 1 and p 2 what is M? Not necessarily in CM frame Simple calculation using 4- momentum conservation: M 2 = Hp 1 + p 2 L 2 -Hp 1 - p 2 L 2 To get cross section, have to weight by 1/s = 1/M 2 (Generic behavior of cross sections) Make toy MC, taking p 1 and p 2 from PDF from hep-ph/ (CTEQ Collab.): 53 Exotics Meeting

54 Parameterization Motivation Resulting toy has a shape very similar to the chosen parameterization x 1 = p 1 /p beam Mass, weighted by 1/s 54 Exotics Meeting

55 Cross Section Vs. Run Muons Electrons 55 Exotics Meeting

56 Inv. Mass Distributions 56 Exotics Meeting

57 Dominant background is Z+jet After a survey of different variables, chose: N jet J T = Sum jet E T Optimization problem non-trivial Would like maximal sensitivity Would like simplicity Uniform jet E T thresholds Would like sensitivity to all masses Kinematic cuts must change with mass Selection N jet30 3 J T > X, where we scan X in 50 GeV steps 57 Exotics Meeting

58 Fit to N jet Distribution? Possible to fit N jet30 distribution? No difficult to figure out the correct parameterization Only have 3 data points: 0, 1 and 2 jet bins Large systematic on simple exponential parameterization In data: Full fit, N jet = [0,2]: Bkg = 21.3 N jet = [0,1] Bkg = 19.1 N jet = [1,2] Bkg = Exotics Meeting

59 Fit to N jet Distribution? Possible to fit N jet30 distribution? No difficult to figure out the correct parameterization Only have 3 data points: 0, 1 and 2 jet bins Large systematic on simple exponential parameterization In data: Full fit, N jet = [0,2]: Bkg = 21.3 N jet = [0,1] Bkg = 19.1 N jet = [1,2] Bkg = Exotics Meeting

60 Jet E T Parameterization Choice What parameterization to use? Look at highest E T jet in Monte Carlo Reconstructed level Log plot Behaves as exponential at high E T 60 Exotics Meeting

61 Jet E T Parameterization Choice What parameterization to use? Look at highest E T jet in Monte Carlo Reconstructed level Log-Log plot Behaves as power law at low E T 61 Exotics Meeting

62 Jet E T Parameterization Choice Power law as E T Ø 0 Exponential as E T Ø Function with correct limiting behavior: f HE T L = N e-e Tê p 1 HE T L p 2 Parameterization fits well 62 Exotics Meeting

63 Jet E T Parameterization Choice Look at generator level Find reconstructed jet s true 4- vector by summing HEPG particle s 4-vectors in a window of Delta R Fits generator level E T well 63 Exotics Meeting

64 Jet E T Parameterization Choice Look at data Look at Z+jet highest E T jet distribution (Blinded to events with N jet30 3) Fits distribution well in data Appendix A of note (# 8465) has more discussion 64 Exotics Meeting

65 Jet E T Shape vs. N jet How does the jet E T shape change vs. N jet30 bin? In data and Monte Carlo, the high E T tail gets harder in the higher N jet30 bins We model this by extrapolating the exponential parameter linearly Jet E T distribution is an exponential at high E T Fit in N jet30 = 3 bin 65 Exotics Meeting

66 J T Prediction With Uncertainties Or course, the fits have errors To find the contribution of each parameter to the J T shape, we change each parameter within its error and re-run the toy We estimate a parameterization mismatch by changing the E T range, refitting, and re-running the toy We add the uncertainty from each parameter-change in quadrature, bin-by-bin 66 Exotics Meeting

67 J T Prediction With Uncertainties Or course, the fits have errors To find the contribution of each parameter to the J T shape, we change each parameter within its error and re-run the toy We estimate a parameterization mismatch by changing the E T range, refitting, and re-running the toy We add the uncertainty from each parameter-change in quadrature, bin-by-bin 67 Exotics Meeting

68 Fit Validation QCD Electron+fake Fit developed using Monte Carlo Would like to verify that the fit works on data Can t use Z+jet data because of signal bias Try using other X+jet data Try using a QCD sample Make fake leptons using electron data and inverting one of the electron cuts on one leg Invert the Had/EM and release other cuts on one leg, keep the other leg tight Make a fake Z 0 from the electron+fake Veto 81 < M ee < 1 Look at N jet30 68 Exotics Meeting

69 Fit Validation QCD Electron+fake Total J T distribution prediction Prediction matches data quite well Integrated over J T (no J T shape errors): Prediction: Bkg = Observed: Bkg = 4509 Consistent at 0.1s level 69 Exotics Meeting

70 Fit Validation QCD JET20 Another X+jet QCD-rich sample: JET20 triggers Make a fake Z 0 from two jets Look at N jet30 distribution of remaining jets 70 Exotics Meeting

71 Fit Validation QCD JET20 Total J T distribution prediction Prediction matches data quite well Integrated over J T (no J T shape errors): Prediction: Bkg = Observed: Bkg = 165 Consistent at 0.4s level 71 Exotics Meeting

72 Fit Validation W+jets/Top Another X+jet sample: W+jets Single muon w/ p T > 20 GeV Missing E T > 25 GeV This is the Lepton+jets channel, with no b-tagging Use W Ø mn channel only This sample has a real signal in it, from top Good test that method finds a signal in data Same N jet modeling problem as Z+jet data In N jet30 distribution, there is an excess of data at high N jet30 But also at N jet30 = 2, where very little top is expected Try to use fit procedure to predict W+jets background in N jet30 72 Exotics Meeting

73 3 rd highest E T jet fit Fit Validation W+jets/Top Monte Carlo 73 Exotics Meeting

74 3 rd highest E T jet fit Fit Validation W+jets/Top Data 74 Exotics Meeting

75 Fit Validation W+jets/Top 3 rd highest E T jet fit Data Prediction: Observed: 762 Excess: Assuming excess from top, take Ae from MC: Ae = % L = 36/pb s = pb (stat only) 75 Exotics Meeting

76 Fit Validation W+jets/Top J T shape consistent with background prediction from fit + shape from top MC Top MC normalized to measured cross section of 9.1 pb 76 Exotics Meeting

77 Fit Validation Photon+Jet Alexei suggested looking at photon+jet for an additional cross check of the background prediction Photons always detected with a minimum p T threshold E.g., photon p T > 20 Need to recoil with something, usually a jet Z s have p T > 0 Fit not designed for such environments Useful cross-check tells us what the limits of the fit procedure are At low J T, the fit does not work well, as one would expect At high J T, away from threshold, the tail is modeled reasonably well 77 Exotics Meeting

78 Sources Of Acceptance Uncertainty s = N L A Luminosity (5.9 %) Monte Carlo statistics (currently 2.4%) Jet Energy Scale Parton Distribution Functions Initial and Final State Radiation Lepton ID efficiency 78 Exotics Meeting

79 Jet Energy Scale Requiring: N jet30 3 Sum Jet E T > X Changing the jet energy will change the acceptance Varied jet energy scale within its errors using standard prescription Error bars here nearly completely correlated Take difference as systematic: pos. error: 5.5% neg. error: 6.7% 79 Exotics Meeting

80 Parton Distribution Functions Use standard prescription: For each eigenvector, weight events to get PDF s corresponding to 1s of that eigenvector Calculate acceptance with given PDF Implemented with code on Joint Physics page pos. error: 0.2% neg. error: 0.5% 80 Exotics Meeting

81 Initial and Final State Radiation Varied Pythia parameters according to joint physics 1s variations Regenerated signal samples Found variation shown in the plot Here, the error bars are completely statistical, and completely uncorrelated The variation is consistent with statistical fluctuations 81 Exotics Meeting

82 Initial and Final State Radiation Varied Pythia parameters according to joint physics 1s variations Regenerated signal samples Found variation shown in the plot Here, the error bars are completely statistical, and completely uncorrelated The variation is consistent with statistical fluctuations 82 Exotics Meeting

83 Initial and Final State Radiation Can turn off initial and final state radiation completely to understand if there is any effect. For ISR-off, set Pythia parameter: MSTP(61) = 0 For FSR-off, set Pythia parameter: MSTP(71) = 0 For ISR-off, get no change above stat. fluct. For FSR-off, do get a change above stat. fluct.: (A no FSR A def )/A def = 23% (Gross overestimate) From this, can say that I/F SR uncertainty is less than uncertainty from statistical fluctuations: D(A I/FSR ) < 2.4% 83 Exotics Meeting

84 Muon ID Efficiency Took scale factors and trigger efficiencies from CDF note 8262 Total SF is a weighted sum of the CMUP, CMX, and stubless scale factors ID SF: Trigger eff: Exotics Meeting

85 Electron ID Efficiency Scale factors from CDF note 8274 Trigger Eff s from CDF note 7939 Total SF is a weighted sum of the tight and loose SF s ID SF: Trigger eff: Exotics Meeting

86 Acceptance Summary Source Luminosity Monte Carlo Statistics Jet Energy Scale PDF s I/F SR Lepton ID Total Uncertainty 5.9 % 2.4 % 6.7 % 0.5 % < 2.4 % 3 % % 86 Exotics Meeting