CMS-PAS-HIG-22-011 | ||
Search for ZZ and ZH production in the $ \rm{b}\bar{\rm{b}}\rm{b}\bar{\rm{b}} $ final state using proton-proton collisions at $ \sqrt{s}= $ 13 TeV | ||
CMS Collaboration | ||
4 December 2023 | ||
Abstract: A search for ZZ and ZH production in the $ \mathrm{b}\bar{\mathrm{b}}\mathrm{b}\bar{\mathrm{b}} $ final state is presented. The search uses an event sample of proton-proton collisions corresponding to an integrated luminosity of 133 fb$ ^{-1} $ at a center-of-mass energy of 13 TeV with the CMS detector at the LHC. This analysis introduces several novel techniques for deriving and validating the data-driven background model using synthetic data sets, derived from the hemisphere-mixing technique. A multi-class multivariate classifier customized for the $ \mathrm{b}\bar{\mathrm{b}}\mathrm{b}\bar{\mathrm{b}} $ final state is developed to both extract the signal and derive the background model. The data are found to be consistent, within uncertainties, with the standard model (SM) predictions. The observed (expected) upper limit at 95% confidence level corresponds to 3.8 (3.8) and 5.0 (2.9) times the SM prediction for the ZZ and ZH production cross sections, respectively. The ZZ and ZH $ \rightarrow \mathrm{b}\bar{\mathrm{b}}\mathrm{b}\bar{\mathrm{b}} $ processes share the experimental challenges of the HH $ \rightarrow \mathrm{b}\bar{\mathrm{b}}\mathrm{b}\bar{\mathrm{b}} $ analysis. The novel techniques developed and demonstrated in the ZZ and ZH search are used to analyze the HH $ \rightarrow \mathrm{b}\bar{\mathrm{b}}\mathrm{b}\bar{\mathrm{b}} $ background. The ZZ, ZH, and HH $ \rightarrow \mathrm{b}\bar{\mathrm{b}}\mathrm{b}\bar{\mathrm{b}} $ expected sensitivities are extrapolated to an integrated luminosity of 3000 $ \mathrm{fb}^{-1} $. | ||
Links:
CDS record (PDF) ;
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These preliminary results are superseded in this paper, Submitted to EPJC. The superseded preliminary plots can be found here. |
Figures & Tables | Summary | Additional Figures | References | CMS Publications |
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Figures | |
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Figure 1:
Signal (left) and four-tag data (right) passing the event selection. The signal region is defined by the union of the red dashed lines. |
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Figure 1-a:
Signal (left) and four-tag data (right) passing the event selection. The signal region is defined by the union of the red dashed lines. |
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Figure 1-b:
Signal (left) and four-tag data (right) passing the event selection. The signal region is defined by the union of the red dashed lines. |
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Figure 2:
Event selection acceptance times efficiency as a function of the generated four body mass $ m_{4\mathrm{b}} $-gen for the ZZ (left) and ZH (right) signal. The plots show the cumulative efficiency with respect to the inclusive sample. The expected $ m_{4\mathrm{b}} $-gen distribution of the inclusive ZZ and ZH\ signal is given in gray with arbitrary normalization. |
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Figure 2-a:
Event selection acceptance times efficiency as a function of the generated four body mass $ m_{4\mathrm{b}} $-gen for the ZZ (left) and ZH (right) signal. The plots show the cumulative efficiency with respect to the inclusive sample. The expected $ m_{4\mathrm{b}} $-gen distribution of the inclusive ZZ and ZH\ signal is given in gray with arbitrary normalization. |
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Figure 2-b:
Event selection acceptance times efficiency as a function of the generated four body mass $ m_{4\mathrm{b}} $-gen for the ZZ (left) and ZH (right) signal. The plots show the cumulative efficiency with respect to the inclusive sample. The expected $ m_{4\mathrm{b}} $-gen distribution of the inclusive ZZ and ZH\ signal is given in gray with arbitrary normalization. |
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Figure 3:
A high-level sketch of the HCR classifier architecture. Boson-candidate jets are shown on the left with the three possible jet pairings. The HCR architecture is shown in the right. The boxes represent pixels, with the labels indicating which jet, dijet, or quadjet the pixel refers to. The different jet pairings on the left are each represented within the network, as indicated by the color coding. |
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Figure 4:
Jet (left) and b-tagged jet (right) multiplicity distributions in the sideband region. The black data points show the observed four-tag data, the blue distribution the $ \mathrm{t} \overline{\mathrm{t}} $ simulation, and the yellow the three-tag multijet prior to the JCM corrections. The red distribution shows the result of the fitted JCM model. |
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Figure 4-a:
Jet (left) and b-tagged jet (right) multiplicity distributions in the sideband region. The black data points show the observed four-tag data, the blue distribution the $ \mathrm{t} \overline{\mathrm{t}} $ simulation, and the yellow the three-tag multijet prior to the JCM corrections. The red distribution shows the result of the fitted JCM model. |
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Figure 4-b:
Jet (left) and b-tagged jet (right) multiplicity distributions in the sideband region. The black data points show the observed four-tag data, the blue distribution the $ \mathrm{t} \overline{\mathrm{t}} $ simulation, and the yellow the three-tag multijet prior to the JCM corrections. The red distribution shows the result of the fitted JCM model. |
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Figure 5:
Distributions illustrating one of the key differences between the three- and four-tag samples shown with the JCM correction but before kinematic reweighting. The $ \Delta\text{R}(j,j) $ distribution of the two boson-candidate jets with the smallest angular separation is shown on the left. The $ \Delta\text{R}(j,j) $ distribution of the other two bosons-candidate jets is shown on the right. |
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Figure 5-a:
Distributions illustrating one of the key differences between the three- and four-tag samples shown with the JCM correction but before kinematic reweighting. The $ \Delta\text{R}(j,j) $ distribution of the two boson-candidate jets with the smallest angular separation is shown on the left. The $ \Delta\text{R}(j,j) $ distribution of the other two bosons-candidate jets is shown on the right. |
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Figure 5-b:
Distributions illustrating one of the key differences between the three- and four-tag samples shown with the JCM correction but before kinematic reweighting. The $ \Delta\text{R}(j,j) $ distribution of the two boson-candidate jets with the smallest angular separation is shown on the left. The $ \Delta\text{R}(j,j) $ distribution of the other two bosons-candidate jets is shown on the right. |
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Figure 6:
Signal probabilities for ZZ (left) and ZH (right) events in the sideband with the JCM correction but before kinematic reweighting. |
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Figure 6-a:
Signal probabilities for ZZ (left) and ZH (right) events in the sideband with the JCM correction but before kinematic reweighting. |
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Figure 6-b:
Signal probabilities for ZZ (left) and ZH (right) events in the sideband with the JCM correction but before kinematic reweighting. |
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Figure 7:
The $ \Delta\text{R}(j,j) $ distributions shown in Fig. 5 after including the FvT corrections. |
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Figure 7-a:
The $ \Delta\text{R}(j,j) $ distributions shown in Fig. 5 after including the FvT corrections. |
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Figure 7-b:
The $ \Delta\text{R}(j,j) $ distributions shown in Fig. 5 after including the FvT corrections. |
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Figure 8:
Signal probabilities for ZZ (left) and ZH (right) events in the sideband after including the FvT corrections. |
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Figure 8-a:
Signal probabilities for ZZ (left) and ZH (right) events in the sideband after including the FvT corrections. |
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Figure 8-b:
Signal probabilities for ZZ (left) and ZH (right) events in the sideband after including the FvT corrections. |
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Figure 9:
Signal probabilities for ZZ (upper row) and ZH (lower row) events in the sideband (left) and signal region (right). The yellow distributions show the multijet model before applying the FvT corrections. The average of the mixed models (red) provides a high-statistics proxy of the 4b background (black) that allows the extrapolation of the background model to be tested with precision. |
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Figure 9-a:
Signal probabilities for ZZ (upper row) and ZH (lower row) events in the sideband (left) and signal region (right). The yellow distributions show the multijet model before applying the FvT corrections. The average of the mixed models (red) provides a high-statistics proxy of the 4b background (black) that allows the extrapolation of the background model to be tested with precision. |
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Figure 9-b:
Signal probabilities for ZZ (upper row) and ZH (lower row) events in the sideband (left) and signal region (right). The yellow distributions show the multijet model before applying the FvT corrections. The average of the mixed models (red) provides a high-statistics proxy of the 4b background (black) that allows the extrapolation of the background model to be tested with precision. |
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Figure 9-c:
Signal probabilities for ZZ (upper row) and ZH (lower row) events in the sideband (left) and signal region (right). The yellow distributions show the multijet model before applying the FvT corrections. The average of the mixed models (red) provides a high-statistics proxy of the 4b background (black) that allows the extrapolation of the background model to be tested with precision. |
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Figure 9-d:
Signal probabilities for ZZ (upper row) and ZH (lower row) events in the sideband (left) and signal region (right). The yellow distributions show the multijet model before applying the FvT corrections. The average of the mixed models (red) provides a high-statistics proxy of the 4b background (black) that allows the extrapolation of the background model to be tested with precision. |
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Figure 10:
The ZZ (left) and ZH (right) background models fit to the observed signal-region yield in the mixed models. The black data points show the average of the mixed models, the yellow distributions the average of the multijet models. The red distribution shows the post-fit background model. The lower panels show the pre- and post-fit pulls. The ZZ distribution is fit with all five basis coefficients constrained. The ZH distribution is fit with two of the four basis parameters unconstrained. |
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Figure 10-a:
The ZZ (left) and ZH (right) background models fit to the observed signal-region yield in the mixed models. The black data points show the average of the mixed models, the yellow distributions the average of the multijet models. The red distribution shows the post-fit background model. The lower panels show the pre- and post-fit pulls. The ZZ distribution is fit with all five basis coefficients constrained. The ZH distribution is fit with two of the four basis parameters unconstrained. |
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Figure 10-b:
The ZZ (left) and ZH (right) background models fit to the observed signal-region yield in the mixed models. The black data points show the average of the mixed models, the yellow distributions the average of the multijet models. The red distribution shows the post-fit background model. The lower panels show the pre- and post-fit pulls. The ZZ distribution is fit with all five basis coefficients constrained. The ZH distribution is fit with two of the four basis parameters unconstrained. |
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Figure 11:
Post-fit background-only SvB signal probability distributions in the ZZ region (left) and the ZH region (right). |
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Figure 11-a:
Post-fit background-only SvB signal probability distributions in the ZZ region (left) and the ZH region (right). |
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Figure 11-b:
Post-fit background-only SvB signal probability distributions in the ZZ region (left) and the ZH region (right). |
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Figure 12:
Projected ZZ, ZH, and $ \mathrm{H}\mathrm{H} \rightarrow 4\mathrm{b} $ 95% CL exclusion limits up to 3000 fb$ ^{-1} $. The HH projections are shown with four different background uncertainty scaling assumptions. |
Tables | |
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Table 1:
Summary of relative uncertainties on the measured signal strength, expressed in percentage of total uncertainty. The total uncertainties include the effect of correlations. |
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Table 2:
Expected and observed significance and 95% CL limit for ZZ, and ZH signal strengths. The limits are computed under the hypothesis of no $ \mathrm{Z}\mathrm{Z}\rightarrow 4\mathrm{b} $ or $ \mathrm{Z}\mathrm{H}\rightarrow 4\mathrm{b} $ signal. |
Summary |
Sensitivity to di-Higgs boson (HH) production is driven by combining the $ \mathrm{b}\overline{\mathrm{b}}\gamma\gamma $, $ \mathrm{b}\overline{\mathrm{b}}\tau\tau $, and $ \mathrm{b}\overline{\mathrm{b}}\mathrm{b}\overline{\mathrm{b}} $ (4b) decay modes. Extracting all available information from the 4b decay mode will require the development and validation of a high-dimensional data-driven background model. The $ \mathrm{Z}\mathrm{Z}\rightarrow 4\mathrm{b} $ and $ \mathrm{Z}\mathrm{H} \rightarrow4\mathrm{b} $ processes provide standard candles that can be used to validate the 4b background model in situ. A search for ZZ and ZH production in the 4b final state was presented. The search uses the full 2016--2018 data set of proton-proton collisions at a center-of-mass energy of 13 TeV recorded with the CMS detector at the LHC, corresponding to an integrated luminosity of 133 fb$ ^{-1} $. This analysis benefits from a multi-class multivariate classifier, which uses convolutions to solve the combinatoric jet-pairing problem, and has been designed with an architecture customized to the 4b final state. The classifier is used both for signal-versus-background discrimination and for the derivation and validation of the background model. A novel technique for assessing background modeling uncertainties, using a synthetic data set derived from hemisphere mixing, allows both the extrapolation uncertainty and variance of the background model to be measured with a precision better than the statistical uncertainties of the four-tag data. The observed (expected) 95% CL upper limits on the $ \mathrm{Z}\mathrm{Z} \rightarrow 4\mathrm{b} $ and $ \mathrm{Z}\mathrm{H} \rightarrow 4\mathrm{b} $ production cross sections correspond to 3.8 (3.8) and 5.0 (2.9) times the standard model prediction, respectively. The results of this analysis, in addition to the expected HH sensitivity, have been extrapolated to 3000 fb$ ^{-1} $. These projections indicate that, using the current analysis strategy, the $ \mathrm{Z}\mathrm{Z} \rightarrow 4\mathrm{b} $ and $ \mathrm{Z}\mathrm{H} \rightarrow 4\mathrm{b} $ signals are within reach of the HL-LHC and that the sensitivity to $ \mathrm{H}\mathrm{H} \rightarrow 4\mathrm{b} $ would be close to the cross section predicted in the standard model. These projections are conservative as they do not consider improvements from the upgraded HL-LHC detectors or from analysis strategies developed with the much larger data sets. |
Additional Figures | |
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Additional Figure 1:
Event selection acceptance times efficiency as a function of the generated four body mass $ m_{4\mathrm{b}} $-gen for the ZZ (left) and ZH (right) signal. The plots show the relative efficiency of each selection requirement. |
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Additional Figure 1-a:
Event selection acceptance times efficiency as a function of the generated four body mass $ m_{4\mathrm{b}} $-gen for the ZZ (left) and ZH (right) signal. The plots show the relative efficiency of each selection requirement. |
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Additional Figure 1-b:
Event selection acceptance times efficiency as a function of the generated four body mass $ m_{4\mathrm{b}} $-gen for the ZZ (left) and ZH (right) signal. The plots show the relative efficiency of each selection requirement. |
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Additional Figure 2:
Comparison of the performance of the nominal HCR SvB to a simpler likelihood-based classifier using the two dijet invariant masses. A comparison is also made to a version of the HCR SvB with out multijet attention block including the kinematics from the additional non-boson-candidate jets. ZZ is shown on the left and ZH on the right. |
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Additional Figure 2-a:
Comparison of the performance of the nominal HCR SvB to a simpler likelihood-based classifier using the two dijet invariant masses. A comparison is also made to a version of the HCR SvB with out multijet attention block including the kinematics from the additional non-boson-candidate jets. ZZ is shown on the left and ZH on the right. |
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Additional Figure 2-b:
Comparison of the performance of the nominal HCR SvB to a simpler likelihood-based classifier using the two dijet invariant masses. A comparison is also made to a version of the HCR SvB with out multijet attention block including the kinematics from the additional non-boson-candidate jets. ZZ is shown on the left and ZH on the right. |
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Additional Figure 3:
The FvT classifier reweight distribution before (left) and after (right) applying the FvT corrections. |
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Additional Figure 3-a:
The FvT classifier reweight distribution before (left) and after (right) applying the FvT corrections. |
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Additional Figure 3-b:
The FvT classifier reweight distribution before (left) and after (right) applying the FvT corrections. |
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Additional Figure 4:
The ZZ SvB distribution in the signal region for one of the mixed data sub-samples (left) and after averaging over all fifteen of the mixed sub-samples (right). |
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Additional Figure 4-a:
The ZZ SvB distribution in the signal region for one of the mixed data sub-samples (left) and after averaging over all fifteen of the mixed sub-samples (right). |
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Additional Figure 4-b:
The ZZ SvB distribution in the signal region for one of the mixed data sub-samples (left) and after averaging over all fifteen of the mixed sub-samples (right). |
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Additional Figure 5:
The ZH SvB distribution in the signal region for one of the mixed data sub-samples (left) and after averaging over all fifteen of the mixed sub-samples (right). |
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Additional Figure 5-a:
The ZH SvB distribution in the signal region for one of the mixed data sub-samples (left) and after averaging over all fifteen of the mixed sub-samples (right). |
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Additional Figure 5-b:
The ZH SvB distribution in the signal region for one of the mixed data sub-samples (left) and after averaging over all fifteen of the mixed sub-samples (right). |
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Additional Figure 6:
The predicted multijet ZZ SvB distributions in the mixed data signal regions fit to their average. The plots show the results of fitting with an increasing number of basis functions, from one in the upper left figure, to five in the lower row. The predictions from each mixed model are shown separately in yellow by adding the mixed data set index to offset the signal SvB distribution. The black data points show the average of the predictions. The predictions, including the fitted basis corrections, are shown in blue. The lower panels show the pulls before and after adding the basis corrections. The pull of adjacent bins are tested for statistical correlation. The correlation coefficient (r) is reported in the legend along with the p-value used to test for lack of correlation. Basis functions are added until the p-value is above 5%. |
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Additional Figure 6-a:
The predicted multijet ZZ SvB distributions in the mixed data signal regions fit to their average. The plots show the results of fitting with an increasing number of basis functions, from one in the upper left figure, to five in the lower row. The predictions from each mixed model are shown separately in yellow by adding the mixed data set index to offset the signal SvB distribution. The black data points show the average of the predictions. The predictions, including the fitted basis corrections, are shown in blue. The lower panels show the pulls before and after adding the basis corrections. The pull of adjacent bins are tested for statistical correlation. The correlation coefficient (r) is reported in the legend along with the p-value used to test for lack of correlation. Basis functions are added until the p-value is above 5%. |
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Additional Figure 6-b:
The predicted multijet ZZ SvB distributions in the mixed data signal regions fit to their average. The plots show the results of fitting with an increasing number of basis functions, from one in the upper left figure, to five in the lower row. The predictions from each mixed model are shown separately in yellow by adding the mixed data set index to offset the signal SvB distribution. The black data points show the average of the predictions. The predictions, including the fitted basis corrections, are shown in blue. The lower panels show the pulls before and after adding the basis corrections. The pull of adjacent bins are tested for statistical correlation. The correlation coefficient (r) is reported in the legend along with the p-value used to test for lack of correlation. Basis functions are added until the p-value is above 5%. |
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Additional Figure 6-c:
The predicted multijet ZZ SvB distributions in the mixed data signal regions fit to their average. The plots show the results of fitting with an increasing number of basis functions, from one in the upper left figure, to five in the lower row. The predictions from each mixed model are shown separately in yellow by adding the mixed data set index to offset the signal SvB distribution. The black data points show the average of the predictions. The predictions, including the fitted basis corrections, are shown in blue. The lower panels show the pulls before and after adding the basis corrections. The pull of adjacent bins are tested for statistical correlation. The correlation coefficient (r) is reported in the legend along with the p-value used to test for lack of correlation. Basis functions are added until the p-value is above 5%. |
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Additional Figure 6-d:
The predicted multijet ZZ SvB distributions in the mixed data signal regions fit to their average. The plots show the results of fitting with an increasing number of basis functions, from one in the upper left figure, to five in the lower row. The predictions from each mixed model are shown separately in yellow by adding the mixed data set index to offset the signal SvB distribution. The black data points show the average of the predictions. The predictions, including the fitted basis corrections, are shown in blue. The lower panels show the pulls before and after adding the basis corrections. The pull of adjacent bins are tested for statistical correlation. The correlation coefficient (r) is reported in the legend along with the p-value used to test for lack of correlation. Basis functions are added until the p-value is above 5%. |
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Additional Figure 6-e:
The predicted multijet ZZ SvB distributions in the mixed data signal regions fit to their average. The plots show the results of fitting with an increasing number of basis functions, from one in the upper left figure, to five in the lower row. The predictions from each mixed model are shown separately in yellow by adding the mixed data set index to offset the signal SvB distribution. The black data points show the average of the predictions. The predictions, including the fitted basis corrections, are shown in blue. The lower panels show the pulls before and after adding the basis corrections. The pull of adjacent bins are tested for statistical correlation. The correlation coefficient (r) is reported in the legend along with the p-value used to test for lack of correlation. Basis functions are added until the p-value is above 5%. |
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Additional Figure 7:
The predicted multijet ZH SvB distributions in the mixed data signal regions fit to their average. The plots show the results of fitting with an increasing number of basis functions, from one in the upper left figure, to four in the lower right. The predictions from each mixed model are shown separately in yellow by adding the mixed data set index to offset the signal SvB distribution. The black data points show the average of the predictions. The predictions, including the fitted basis corrections, are shown in blue. The lower panels show the pulls before and after adding the basis corrections. The pull of adjacent bins are tested for statistical correlation. The correlation coefficient (r) is reported in the legend along with the p-value used to test for lack of correlation. Basis functions are added until the p-value is above 5%. |
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Additional Figure 7-a:
The predicted multijet ZH SvB distributions in the mixed data signal regions fit to their average. The plots show the results of fitting with an increasing number of basis functions, from one in the upper left figure, to four in the lower right. The predictions from each mixed model are shown separately in yellow by adding the mixed data set index to offset the signal SvB distribution. The black data points show the average of the predictions. The predictions, including the fitted basis corrections, are shown in blue. The lower panels show the pulls before and after adding the basis corrections. The pull of adjacent bins are tested for statistical correlation. The correlation coefficient (r) is reported in the legend along with the p-value used to test for lack of correlation. Basis functions are added until the p-value is above 5%. |
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Additional Figure 7-b:
The predicted multijet ZH SvB distributions in the mixed data signal regions fit to their average. The plots show the results of fitting with an increasing number of basis functions, from one in the upper left figure, to four in the lower right. The predictions from each mixed model are shown separately in yellow by adding the mixed data set index to offset the signal SvB distribution. The black data points show the average of the predictions. The predictions, including the fitted basis corrections, are shown in blue. The lower panels show the pulls before and after adding the basis corrections. The pull of adjacent bins are tested for statistical correlation. The correlation coefficient (r) is reported in the legend along with the p-value used to test for lack of correlation. Basis functions are added until the p-value is above 5%. |
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Additional Figure 7-c:
The predicted multijet ZH SvB distributions in the mixed data signal regions fit to their average. The plots show the results of fitting with an increasing number of basis functions, from one in the upper left figure, to four in the lower right. The predictions from each mixed model are shown separately in yellow by adding the mixed data set index to offset the signal SvB distribution. The black data points show the average of the predictions. The predictions, including the fitted basis corrections, are shown in blue. The lower panels show the pulls before and after adding the basis corrections. The pull of adjacent bins are tested for statistical correlation. The correlation coefficient (r) is reported in the legend along with the p-value used to test for lack of correlation. Basis functions are added until the p-value is above 5%. |
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Additional Figure 7-d:
The predicted multijet ZH SvB distributions in the mixed data signal regions fit to their average. The plots show the results of fitting with an increasing number of basis functions, from one in the upper left figure, to four in the lower right. The predictions from each mixed model are shown separately in yellow by adding the mixed data set index to offset the signal SvB distribution. The black data points show the average of the predictions. The predictions, including the fitted basis corrections, are shown in blue. The lower panels show the pulls before and after adding the basis corrections. The pull of adjacent bins are tested for statistical correlation. The correlation coefficient (r) is reported in the legend along with the p-value used to test for lack of correlation. Basis functions are added until the p-value is above 5%. |
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Additional Figure 8:
Fits of the ZH background model to the observed signal-region yield in the mixed models. The black data points show the average of the mixed models, the yellow distributions the average of the multijet models. The lower panels show the pre- and post-fit pulls. The figure on the left (right) shows the fit with zero (one) unconstrained basis parameter(s). |
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Additional Figure 8-a:
Fits of the ZH background model to the observed signal-region yield in the mixed models. The black data points show the average of the mixed models, the yellow distributions the average of the multijet models. The lower panels show the pre- and post-fit pulls. The figure on the left (right) shows the fit with zero (one) unconstrained basis parameter(s). |
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Additional Figure 8-b:
Fits of the ZH background model to the observed signal-region yield in the mixed models. The black data points show the average of the mixed models, the yellow distributions the average of the multijet models. The lower panels show the pre- and post-fit pulls. The figure on the left (right) shows the fit with zero (one) unconstrained basis parameter(s). |
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Additional Figure 9:
The pre-fit signal SvB signal probability distributions in the ZZ region (left) and the ZH region (right). |
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Additional Figure 9-a:
The pre-fit signal SvB signal probability distributions in the ZZ region (left) and the ZH region (right). |
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Additional Figure 9-b:
The pre-fit signal SvB signal probability distributions in the ZZ region (left) and the ZH region (right). |
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Additional Figure 10:
The post-fit signal plus background SvB signal probability distributions in the ZZ region (left) and the ZH region (right). |
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Additional Figure 10-a:
The post-fit signal plus background SvB signal probability distributions in the ZZ region (left) and the ZH region (right). |
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Additional Figure 10-b:
The post-fit signal plus background SvB signal probability distributions in the ZZ region (left) and the ZH region (right). |
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