CMS-PAS-FTR-21-002

CMS-PAS-FTR-21-002
Prospects for the measurement of $\mathrm{t\bar{t}}$H production in the opposite-sign dilepton channel at $\sqrt{s}$ = 14 TeV at the High-Luminosity LHC
CMS Collaboration
March 2022

Abstract: Prospects for the measurement of top quark-antiquark associated Higgs boson ($\mathrm{t\bar{t}}$H) production in the HL-LHC era are presented. The measurement is performed in the opposite sign dilepton channel where $e^{+}e^{-}$, $\mu^{+}\mu^{-}$ and $e^{\pm}\mu^{\pm}$ are considered, while focusing on the H $ \to \mathrm{b\bar{b}}$ decay. A novel approach with respect to previous analyses and the projection study for the HL-LHC is explored. The analysis strategy is based on the reconstruction of the Higgs boson invariant mass through the analytical solution of the kinematic equations of the $\mathrm{t\bar{t}}$H system, employs a neural network for the selection of data and makes use of a data-based method to estimate the main backgrounds. The results are extrapolated to the full integrated luminosity expected at the High-Luminosity LHC at $\sqrt{s}=$ 14 TeV, with the signal strength being expected to be extracted with $^{+0.13}_{-0.12}$ uncertainty.
Links: CDS record (PDF) ; CADI line (restricted) ;

Figures & Tables	Summary	References	CMS Publications

Figures
png pdf	Figure 1: Tree-level Feynman diagrams for the production of a Higgs boson in association with a top quark-antiquark pair ($\mathrm{t\bar{t}}$H) in the t-channel (left) and in the s-channel (right). In both cases the Higgs boson subsequently decays to a pair of b and anti-b quarks (H $ \to \mathrm{b\bar{b}}$).
png pdf	Figure 1-a: Tree-level Feynman diagrams for the production of a Higgs boson in association with a top quark-antiquark pair ($\mathrm{t\bar{t}}$H) in the t-channel (left) and in the s-channel (right). In both cases the Higgs boson subsequently decays to a pair of b and anti-b quarks (H $ \to \mathrm{b\bar{b}}$).
png	Figure 1-b: Tree-level Feynman diagrams for the production of a Higgs boson in association with a top quark-antiquark pair ($\mathrm{t\bar{t}}$H) in the t-channel (left) and in the s-channel (right). In both cases the Higgs boson subsequently decays to a pair of b and anti-b quarks (H $ \to \mathrm{b\bar{b}}$).
png pdf	Figure 2: Reconstructed invariant mass ${m_{\mathrm{b\bar{b}}}}$ distribution in the signal region after the fit to Asimov data. The expected signal+background distribution is shown with a red line and is constructed using simulated events in the signal region at $\sqrt {s} = $14 TeV scaled to an integrated luminosity of 3000 fb$^{-1}$. The background prediction has been extrapolated to the signal region using events in the control region by applying event probability weights. The post-fit background prediction distribution is shown with a filled purple histogram. The pre-fit $\mathrm{t\bar{t}}$H signal distribution, multiplied by a factor 5, is overlayed with a blue line. The bottom panel shows the pull distribution defined as the per-bin ratio of the expected number of data minus the expected number of background events over the per-bin statistical uncertainty of the expected data.
png pdf	Figure 3: B-tagging probability map as a function of ${p_{\text {T}}}$ and $\eta $.
png pdf	Figure 4: Left: The ${m_{\mathrm{b\bar{b}}}}$ distribution is shown for $\mathrm{t\bar{t}} $+jets events in the 3b VR when applying the b-tagging selection (black) and when applying TRF weights to events in the 2b CR to predict the distribution in the 3b VR (blue) while the bottom panel shows the ratio of these two distributions (black points). The ratio has been fitted with a polynomial function of 0$^{th}$ degree (blue line) and of 1$^{st}$ degree (magenta line). The fitted parameter values of the 0$^{th}$ (1$^{st}$) degree polynomials are shown in the blue (magenta) box. The goodness-of-fit is assessed by a chi-square test, the probability of which is also denoted. Right: The ${m_{\mathrm{b\bar{b}}}}$ distribution is shown for $\mathrm{t\bar{t}} $+jets events in the SR when applying the b-tagging selection (black) and when applying TRF weights to events in the 2b CR to predict the distribution in the SR (blue) while the bottom panel shows the ratio of these two distributions (black points). The ratio has been fitted with a polynomial function of 0$^{th}$ degree (blue line) and of 1$^{st}$ degree (magenta line). The fitted parameter values of the 0$^{th}$ (1$^{st}$) degree polynomials are shown in the blue (magenta) box. The goodness-of-fit is assessed by a chi-square test, the probability of which is also denoted.
png	Figure 4-a: Left: The ${m_{\mathrm{b\bar{b}}}}$ distribution is shown for $\mathrm{t\bar{t}} $+jets events in the 3b VR when applying the b-tagging selection (black) and when applying TRF weights to events in the 2b CR to predict the distribution in the 3b VR (blue) while the bottom panel shows the ratio of these two distributions (black points). The ratio has been fitted with a polynomial function of 0$^{th}$ degree (blue line) and of 1$^{st}$ degree (magenta line). The fitted parameter values of the 0$^{th}$ (1$^{st}$) degree polynomials are shown in the blue (magenta) box. The goodness-of-fit is assessed by a chi-square test, the probability of which is also denoted. Right: The ${m_{\mathrm{b\bar{b}}}}$ distribution is shown for $\mathrm{t\bar{t}} $+jets events in the SR when applying the b-tagging selection (black) and when applying TRF weights to events in the 2b CR to predict the distribution in the SR (blue) while the bottom panel shows the ratio of these two distributions (black points). The ratio has been fitted with a polynomial function of 0$^{th}$ degree (blue line) and of 1$^{st}$ degree (magenta line). The fitted parameter values of the 0$^{th}$ (1$^{st}$) degree polynomials are shown in the blue (magenta) box. The goodness-of-fit is assessed by a chi-square test, the probability of which is also denoted.
png	Figure 4-b: Left: The ${m_{\mathrm{b\bar{b}}}}$ distribution is shown for $\mathrm{t\bar{t}} $+jets events in the 3b VR when applying the b-tagging selection (black) and when applying TRF weights to events in the 2b CR to predict the distribution in the 3b VR (blue) while the bottom panel shows the ratio of these two distributions (black points). The ratio has been fitted with a polynomial function of 0$^{th}$ degree (blue line) and of 1$^{st}$ degree (magenta line). The fitted parameter values of the 0$^{th}$ (1$^{st}$) degree polynomials are shown in the blue (magenta) box. The goodness-of-fit is assessed by a chi-square test, the probability of which is also denoted. Right: The ${m_{\mathrm{b\bar{b}}}}$ distribution is shown for $\mathrm{t\bar{t}} $+jets events in the SR when applying the b-tagging selection (black) and when applying TRF weights to events in the 2b CR to predict the distribution in the SR (blue) while the bottom panel shows the ratio of these two distributions (black points). The ratio has been fitted with a polynomial function of 0$^{th}$ degree (blue line) and of 1$^{st}$ degree (magenta line). The fitted parameter values of the 0$^{th}$ (1$^{st}$) degree polynomials are shown in the blue (magenta) box. The goodness-of-fit is assessed by a chi-square test, the probability of which is also denoted.
png pdf	Figure 5: The 68% confidence level uncertainty bands of the 1$^{st}$ degree polynomial used to correct the background prediction is shown as a function of the reconstructed $m_{\mathrm{b\bar{b}}}$ invariant mass. The number of events in simulation is assumed to be 10 times larger than the one expected in data. The progression of the uncertainty bands is shown for 140 fb$^{-1}$ x 10 (pink), 300 fb$^{-1}$ x 10 (light blue), 1000 fb$^{-1}$ x 10 (green) and 3000 fb$^{-1}$ x 10 (brown) of simulated events.
png pdf	Figure 6: Left: Projected background rate uncertainty originating from the data to simulation compatibility of the TRF prediction normalization in the 3b VR as a function of the integrated luminosity. Right: Projection of the uncertainty originating from the background prediction correction factor. The graph shows the average per- ${m_{\mathrm{b\bar{b}}}}$ -bin value of the 68% confidence interval of the 1$^{st}$ degree polynomial which is used to correct the background prediction as a function of the simulated luminosity.
png	Figure 6-a: Left: Projected background rate uncertainty originating from the data to simulation compatibility of the TRF prediction normalization in the 3b VR as a function of the integrated luminosity. Right: Projection of the uncertainty originating from the background prediction correction factor. The graph shows the average per- ${m_{\mathrm{b\bar{b}}}}$ -bin value of the 68% confidence interval of the 1$^{st}$ degree polynomial which is used to correct the background prediction as a function of the simulated luminosity.
png	Figure 6-b: Left: Projected background rate uncertainty originating from the data to simulation compatibility of the TRF prediction normalization in the 3b VR as a function of the integrated luminosity. Right: Projection of the uncertainty originating from the background prediction correction factor. The graph shows the average per- ${m_{\mathrm{b\bar{b}}}}$ -bin value of the 68% confidence interval of the 1$^{st}$ degree polynomial which is used to correct the background prediction as a function of the simulated luminosity.
png pdf	Figure 7: Projected estimated significance shown as a function of the integrated luminosity for the statistical uncertainties only scenario (black), the Run 2 uncertainty scenario (magenta), the conservative scenario (light blue) and the realistic scenario (green).
png pdf	Figure 8: Best fit values of the signal strength modifiers $\mu $ obtained in the latest CMS published result [4] (top) and for the fit of the Asimov dataset constructed for 3000 fb$^{-1}$ for the different uncertainty scenarios. Also shown are the 68% expected confidence intervals (blue error bar) which are additionally split into their statistical (red error bar) and systematic (black error bar) components.

Tables
png pdf	Table 1: Baseline event selection criteria.
png pdf	Table 2: Expected significance and total (statistical and systematic) uncertainty on the signal strength modifier ($\delta \mu $) calculated for different values of integrated luminosity for the conservative and the most realistic uncertainty extrapolation scenarios.

Summary

The discovery of the Higgs boson has played a key role in understanding the SM. With increased integrated luminosity acquired, the LHC has opened a new era of precision measurements allowing us to study the properties of this important particle, thoroughly testing the consistency of the foundations of the SM. The HL-LHC will provide a unique opportunity to extensively test the Higgs boson properties with increased statistical precision. For the prospective studies presented in this summary, a novel alternative approach with respect to the one presented in Ref. [4] was considered. The novel approach introduced is able to overcome the hurdle of being systematic-uncertainty-limited since the main background, arising from $\mathrm{t\bar{t}} $+jets events, is predicted with a data-based method. It also provides a measurement utilizing the mass of the Higgs boson as an observable, hence being orthogonal and complementary to the approaches which utilize the output of a ML estimator. Different uncertainty extrapolation scenarios were studied to take into account the different running conditions and expected increase in luminosity. With this analysis approach, one should be able to reach discovery with the dilepton channel alone accompanied by a significantly improved precision with as much as 1/3 of the HL-LHC integrated luminosity. A measurement that will utilize the full HL-LHC integrated luminosity can result to approximately 13% total uncertainty on the signal cross section. This translates to a similar level of precision in measuring $y_{t}$ and will allow us to probe possible deviations from the SM expectation.

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