Chapter VII: Monte Carlo-based razor search on 2015 data
7.10 Signal modeling
The search is interpreted in terms of simplified SUSY models as described in Sec- tion 3.4. Signal MC samples are generated using MadGraph 5 interfaced with Pythia 8, with detector simulation performed by the CMS Fastsim framework [36].
The Fastsim MC events are reweighted to correct the efficiencies for identifying lep- tons andb-jets, and to obtain a distribution of the number of pileup vertices similar to that in data. An additional correction is applied to compensate for an observed mismodeling of the ISR jet multiplicity distribution.
Simplified SUSY models
We interpret the null search result as 95% confidence level limits on the pair produc- tion of heavy gluinos with decays to quarks and the LSP. We consider two scenarios:
• The gluino decays to the neutralino and two third-generation quarks. This
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Figure 7.24: Comparison between the MC-based prediction (marked ‘Method A’) and the fit-based prediction (marked ‘Method B’) in each b-tag category of the Muon Multijet region. The predictions are shown in unrolled format with dashed vertical lines delineating bins inMR. The bottom panel of each plot shows the ratio of the two predictions.
scenario encompasses the T1tttt and T1bbbb models, as well as T1ttbb with arbitrary gluino branching ratios tott¯χ˜01,bb¯χ˜01, andbt¯χ˜+1/tb¯χ˜−1. We compute limits for several choices of branching ratios; the considered values are indi- cated in the diagram in Figure 7.32. In addition to displaying limits for each of these decay scenarios, we also show a conservative limit that corresponds to the worst-case limit among the considered branching ratios.
• The gluino decays to the neutralino and two first- or second-generation quarks.
This corresponds to the T1qqqq simplified model.
The masses of the gluino and the LSP in the simplified models can be specified arbitrarily. Limits on each model are computed for a two-dimensional grid of (mgluino,mLSP) values, and interpolation is performed to obtain the smooth exclu- sion contours shown in the exclusion plots. Where relevant, the lightest chargino is taken to be 5 GeV heavier than the LSP. Theoretical cross sections for pair pro-
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Figure 7.25: Comparison between the MC-based prediction (marked ‘Method A’) and the fit-based prediction (marked ‘Method B’) in each b-tag category of the Electron Multijet region. The predictions are shown in unrolled format with dashed vertical lines delineating bins inMR. The bottom panel of each plot shows the ratio of the two predictions.
duction of gluinos are obtained at NLO and next-to-leading logarithmic accuracy from [129].
Signal systematics
The signal MC is subject to the same instrumental and theoretical uncertainties as the background MC (see Table 7.2). In addition, there are uncertainties from the corrections to the Fastsim lepton andb-jet identification efficiencies and to the distribution of ISR jet multiplicity. The lepton andb-jet uncertainties are typically less than 10%. The ISR jet uncertainty is 15-30% and affects events with hadronic recoil of 400 GeV or higher.
Signal contamination
The control samples used in the MC-based background prediction method are de- signed to be relatively free of SUSY signal events. This is achieved via themT cut
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Figure 7.26: Observed data counts in each bin of the Multijet 0 (top) and 1 (bottom) b-tag categories, compared with the MC-based background prediction.
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Figure 7.27: Observed data counts in each bin of the Multijet 2 (top) and≥3 (bot- tom)b-tag categories, compared with the MC-based background prediction.
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Figure 7.28: Observed data counts in each bin of the Muon Multijet 0 (top) and 1 (bottom)b-tag categories, compared with the MC-based background prediction.
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Figure 7.29: Observed data counts in each bin of the Muon Multijet 2 (top) and≥3 (bottom)b-tag categories, compared with the MC-based background prediction.
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Events
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Data / pred. [Method A]
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Figure 7.30: Observed data counts in each bin of the Electron Multijet 0 (top) and 1 (bottom)b-tag categories, compared with the MC-based background prediction.
[0.15, 0.20] [0.20, 0.25] [0.25, 0.30] [0.30, 0.41] [0.41, 0.52] [0.52, 1.50] [0.15, 0.20] [0.20, 0.25] [0.25, 0.30] [0.30, 0.41] [0.41, 0.52] [0.52, 1.50] [0.15, 0.20] [0.20, 0.25] [0.25, 0.30] [0.30, 0.41] [0.41, 0.52] [0.52, 1.50] [0.15, 0.20] [0.20, 0.25] [0.25, 0.30] [0.30, 0.41] [0.41, 1.50] [0.15, 0.20] [0.20, 0.25] [0.25, 0.30] [0.30, 0.41] [0.41, 1.50] [0.15, 0.20] [0.20, 0.25] [0.25, 1.50] [0.15, 0.25] [0.25, 1.50]
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Events
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Figure 7.31: Observed data counts in each bin of the Electron Multijet 2 (top) and
≥3 (bottom)b-tag categories, compared with the MC-based background prediction.
Figure 7.32: Branching ratios of gluinos decaying to third-generation quarks con- sidered in the interpretation of the analysis. The branching ratios x = BR( ˜g → bb¯χ˜01) and y = BR( ˜g → tt¯χ˜01) can be freely varied between zero and one; the branching ratio for mixed decays to two quarks and a chargino is 1−x −y.
that is applied in all leptonic control regions. Despite this, some SUSY signal events could populate the control regions in non-negligible quantities. Signal contamina- tion in the one-lepton tt¯+jets and W(→ `ν)+jets control regions would increase the number of data events in those samples, which would increase the magnitudes of the MC correction factors derived in these control regions. This in turn would increase the number of predicted tt¯+jets and W(→ `ν)+jets events in the search regions, thus biasing the background estimate.
We account for signal contamination using the reduced efficiency method, which works as follows. For each SUSY signal considered, we estimate the effect on the measured MC correction factors due to the presence of signal in the control regions.
The impact on the signal region background prediction is, on average,
∆B = NS,MC × ∆NC,Data
NC,MC (7.8)
= NS,MC × NC,SUSY
NC,MC
, (7.9)
where NC,SUSYis the expected number of SUSY events in control region. We cor- rect the background prediction explicitly by subtracting∆Bfrom it.
If the SUSY signal cross section isµtimes the nominal theoretical cross section, the amount of signal contamination is µ∆B. The predicted yield in the search region is then
(NS,SM− µ∆B)+ µNS,SUSY (7.10)
= NS,SM− µ(NS,SUSY−∆B). (7.11)
In the second line, we have rearranged the terms to show that subtracting µ∆B from the background prediction is equivalent to subtracting ∆B from the nominal
SUSY signal yield. This demonstrates that the∆Bcorrection reduces the effective efficiency of the signal region for SUSY events. In this way, the possibility of signal contamination leads to worsened limits.