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The first part of this chapter is devoted to the description of the effort to identify a discriminating observable that, being maximally sensible to the presence of the investigated signal, can be used as input to a statistical method. The second part of this chapter will then cover the results obtained within the Standard Model (SM) framework as well as their interpretation in various Beyond the SM (BSM) scenarios, with special regard for Effective Field Theories (EFT). The \({\text {b}}{\text {b}}{\uptau } {\uptau } \) analysis, as any other analysis in CMS, starts from the information deposited in each subdetector of the CMS experiment, which is optimally exploited and combined by Level-1 (L1) trigger and High-Level Trigger (HLT) to select the most interesting events to be recorded for storage (Sect. 2.3 ) based on a trigger menu targeting multiple compelling signatures. The journey continues with the offline analysis of the data, whose first step is the reconstruction of physics objects via the Particle Flow (PF) algorithm (Sect. 2.4 ). In the \({\text {b}}{\text {b}}{\uptau } {\uptau } \) analysis, all PF objects are exploited to reconstruct events compatible with the \({\text {H}}{\text {H}} \) signal: muons, electrons, and hadronically decaying \({\uptau } \) leptons ( \({\uptau } _\textrm{h} \) ) are used to identify the \({\text {H}}\rightarrow {\uptau } {\uptau } \) candidate, while hadronic jets identified with \({\text {b}}\) -tagging algorithms are used to select the \({\text {H}}\rightarrow {\text {b}}{\text {b}}\) candidate (Sect. 5.2 ). Finally, a careful definition of the signal region and the categorization of the events guarantees the ability to further and highly increase the sensitivity of the analysis, exploiting state-of-the-art machine learning techniques for signal extraction (Sect. 5.3 ). All along this process, the construction of Monte Carlo (MC) simulations that carefully reproduce the behaviour of data is performed, and the systematic uncertainties related to it are evaluated (Sects. 5.4 and 5.5 ). The results of the exploration of the Higgs boson pair ( \({\text {H}}{\text {H}} \) ) production in the \({\text {b}}{\text {b}}{\uptau } {\uptau } \) decay channel are presented in this chapter alongside the statistical framework employed for the purpose.

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The Results on  \({\text {H}}{\text {H}} \rightarrow {\text {b}}{\overline{{\text {b}}}}{{\uptau } {^{+}}} {{\uptau } {^{-}}} \)

  • 章节

  • 发表日期：

    2025年

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  • Jona Motta

摘要

The first part of this chapter is devoted to the description of the effort to identify a discriminating observable that, being maximally sensible to the presence of the investigated signal, can be used as input to a statistical method. The second part of this chapter will then cover the results obtained within the Standard Model (SM) framework as well as their interpretation in various Beyond the SM (BSM) scenarios, with special regard for Effective Field Theories (EFT). The \({\text {b}}{\text {b}}{\uptau } {\uptau } \) analysis, as any other analysis in CMS, starts from the information deposited in each subdetector of the CMS experiment, which is optimally exploited and combined by Level-1 (L1) trigger and High-Level Trigger (HLT) to select the most interesting events to be recorded for storage (Sect. 2.3 ) based on a trigger menu targeting multiple compelling signatures. The journey continues with the offline analysis of the data, whose first step is the reconstruction of physics objects via the Particle Flow (PF) algorithm (Sect. 2.4 ). In the \({\text {b}}{\text {b}}{\uptau } {\uptau } \) analysis, all PF objects are exploited to reconstruct events compatible with the \({\text {H}}{\text {H}} \) signal: muons, electrons, and hadronically decaying \({\uptau } \) leptons ( \({\uptau } _\textrm{h} \) ) are used to identify the \({\text {H}}\rightarrow {\uptau } {\uptau } \) candidate, while hadronic jets identified with \({\text {b}}\) -tagging algorithms are used to select the \({\text {H}}\rightarrow {\text {b}}{\text {b}}\) candidate (Sect. 5.2 ). Finally, a careful definition of the signal region and the categorization of the events guarantees the ability to further and highly increase the sensitivity of the analysis, exploiting state-of-the-art machine learning techniques for signal extraction (Sect. 5.3 ). All along this process, the construction of Monte Carlo (MC) simulations that carefully reproduce the behaviour of data is performed, and the systematic uncertainties related to it are evaluated (Sects. 5.4 and 5.5 ). The results of the exploration of the Higgs boson pair ( \({\text {H}}{\text {H}} \) ) production in the \({\text {b}}{\text {b}}{\uptau } {\uptau } \) decay channel are presented in this chapter alongside the statistical framework employed for the purpose.