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تسجيل مستخدم جديدA machine learning global analysis approach to enhance the precision of the Higgs decay branching fractions in electron-positron colliders
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Several high energy $e^{+}e^{-}$ colliders are proposed as Higgs factories by the international high energy physics community. One of the most important goals of these projects is to study the Higgs properties, such as its couplings, mass, width, and production rate, with unprecedented precision. Precision studies of the Higgs boson should be the priority and drive the design and optimization of detectors. A global analysis approach based on the multinomial distribution and Machine Learning techniques is proposed to realize an ``end-to-end analysis and to enhance the precision of all accessible decay branching fractions of the Higgs significantly. A proof-of-principle Monte Carlo simulation study is performed to show the feasibility. This approach shows that the statistical uncertainties of all branching fractions are proportional to a single parameter, which can be used as a metric to optimize the detector design, reconstruction algorithms, and data analyses. In the Higgs factories, the global analysis approach is valuable both to the Higgs measurements and detector R & D, because it has the potential for superior precision and makes detector optimization single-purpose.
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