Chapter V: Impact of data scouting
6.6 Validation of the fit
The background composition of each search category is illustrated in Figure 6.7.
Some notable features are:
• tt¯+jets production is the dominant background in the b-tagged leptonic and hadronic event categories;
• Z(→ νν)+jets production is the dominant background in the Multijet 0b-tag category;
• W(→`ν)+jets production contributes significantly in the 0b-tag leptonic and hadronic event categories;
• QCD multijet production is a small (10-20%) background in the Multijet cat- egory for allb-tag multiplicities.
The individual SM backgrounds will be discussed in greater detail in the context of the MC-based background prediction (Chapter 7).
Selection cut optimization
The value of the∆φRcut in the Multijet search region, and that of themT cut in the Muon and Electron regions, are optimized by considering the expected exclusion limit on a number of SUSY simplified models. Example results are shown in Fig- ure 6.8. We find that the expected limit in the Multijet category is optimized when the ∆φR cut is 2.8. On the other hand, applying any∆φR cut in the Electron and Muon categories is seen to hurt the limit. The signal sensitivity of the one-lepton categories is optimized when the mT cut is 120 GeV. These conclusions are seen to hold for gluino simplified models having both small and large mass splittings between the gluino and the LSP.
Sideband Signal Sensitive Region
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CMS Preliminary MultiJet Box 0 b-tag s=13 TeV, L = 2 fb-1
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CMS Preliminary MultiJet Box 1 b-tag s=13 TeV, L = 2 fb-1
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CMS Preliminary MuonMultijet Box 0 b-tag s=13 TeV, L = 2 fb-1
Sideband Signal Sensitive Region
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TTJets 2L TTJets 1L WJetsToLNu SingleTop ZJetsToNuNu DYJetsToLL QCD Other
CMS Preliminary MuonMultijet Box 1 b-tag s=13 TeV, L = 2 fb-1
Figure 6.7: Fractional composition of the background in the sideband and extrapo- lation region of the 0b-tag (left column) and 1b-tag (right column) subcategories of the Multijet (top) and Muon Multijet (bottom) search categories. The single top quark, Z(→``)+jets, and rare process backgrounds (multiboson andtt¯+V) are indi- cated in addition to the main backgrounds discussed in the text. The categories with 2 and≥ 3b-tags contain mainlytt¯+jets background and are not displayed here.
Figure 6.8: Left: expected limit on the T1bbbb model, with gluino and LSP masses set to 1500 GeV and 100 GeV, respectively, as a function of the∆φRcut. They-axis values indicate the excluded cross section divided by the theoretical cross section.
Right: expected limit on the T1tttt model, with gluino and LSP masses set to 1200 GeV and 800 GeV, respectively, as a function of themT cut.
and its uncertainty adequately describe the background shape. The goodness of the fit is evaluated by comparing the fitted function with the background MC. The
fitted shapes are seen to describe the background within uncertainty in all analysis regions. Example fits to MC are illustrated in Figure 6.9.
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Figure 6.9: Sideband fits to the MC mock dataset in the Muon Multijet (left), Elec- tron Multijet (right), and Multijet (bottom) event categories, projected onto MR. The yellow, magenta, red, and green lines show the contributions from the 0, 1, 2, and≥3b-tag fit functions, and the blue line indicates the sum of the four contribu- tions.
To test the robustness of the fit method to variations in the background composition, we vary the fraction of each background physics process up and down by specified amounts and repeat the fit. We test the following variations:
• 30% upward and downward variations of the tt¯+jets, W(→ `ν)+jets, and Z(→ νν)+jets backgrounds;
• 50% and 100% upward variations of the QCD multijet background;
• 100% upward and 50% downward variations of the rare process backgrounds (multiboson andtt¯+V production).
The fit function describes the background well under all of the variations tested, and the fit predictions do not change significantly with the composition of the back- ground.
Signal injection test
We perform a signal injection test to check that the full signal-plus-background fit can detect a signal and accurately extract its strength. To do this, we use the background model obtained from the best fit to the MC simulation to generate a dataset corresponding to 4 fb−1of integrated luminosity, and inject simulated SUSY events with a specified cross section into it. We fit this simulated dataset to estimate the injected signal strength. This procedure is repeated several times to build up a distribution of estimated signal strength values.
The test is performed for several different SUSY models and for a range of different signal cross sections. In Figure 6.10 we show plots of extracted versus injected signal strength, normalized to the theoretical cross section, for two models of gluino production. The fitted cross sections match the injected cross sections closely and do not exhibit bias.
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Figure 6.10: Fitted vs. extracted signal strengths for the T1bbbb simplified model with gluino and LSP masses set to 1500 and 100 GeV (left plot), or 1000 and 900 GeV (right plot). The error bars show the standard deviations of the extracted signal strengths over the ensemble of toy experiments.
Sideband fit bias study
We test for bias in the predictions of the sideband fit, which does not have access to the data information in the highMRand R2region. To do this, we conduct many pseudo-experiments using the following procedure:
1. Generate a mock dataset of size 2.1 fb−1 by sampling (MR, R2) pairs from the best fit function obtained with the full-region fit.
2. Perform both sideband and full fits to the mock dataset, and compute the percent difference between the sideband fit and full fit predicted yields in a large aggregate region: MR > 700 GeV and R2 > 0.41 for the zero-lepton category, and MR > 600 GeV andR2 >0.25 for the one-lepton categories.
We find that the yields predicted by the sideband fit are on average 5-20% smaller than those from the full fit in all analysis categories. We enlarge the systematic uncertainty on the sideband fit yields to account for this small bias. This has a minimal effect on the search sensitivity; the bias is small compared with the size of the systematic uncertainty on the yield, which varies from 40% to 200% depending on the category.