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The consecutive steps of cascade decay initiated by H→ττ can be useful for the measurement of Higgs couplings and in particular of the Higgs boson parity. In the previous papers we have found that multidimensional signatures of the τ±→π±π0ν and τ±→3π±ν decays can be used to distinguish between scalar and pseudoscalar Higgs state. The machine learning techniques (ML) of binary classification, offered break-through opportunities to manage such complex multidimensional signatures. The classification between two possible CP states: scalar and pseudoscalar, is now extended to the measurement of the hypothetical mixing angle of Higgs boson parity states. The functional dependence of H→ττ matrix element on the mixing angle is predicted by theory. The potential to determine preferred mixing angle of the Higgs boson events sample including τ-decays is studied using deep neural network. The problem is addressed as classification or regression with the aim to determine the per-event: (a) probability distribution (spin weight) of the mixing angle; (b) parameters of the functional form of the spin weight; (c) the most preferred mixing angle. Performance of proposed methods is evaluated and compared. [ABSTRACT FROM AUTHOR]

Machine learning (ML) techniques are rapidly finding a place among the methods of high-energy physics data analysis. Different approaches are explored concerning how much effort should be put into building high-level variables based on physics insight into the problem, and when it is enough to rely on low-level ones, allowing ML methods to find patterns without an explicit physics model. In this paper we continue the discussion of previous publications on the CP state of the Higgs boson measurement of the H→ττ decay channel with the consecutive τ±→ρ±ν; ρ±→π±π0 and τ±→a1±ν; a1±→ρ0π±→3π± cascade decays. The discrimination of the Higgs boson CP state is studied as a binary classification problem between CP even (scalar) and CP odd (pseudoscalar) states using a deep neural network (DNN). Improvements on the classification from the constraints on directly nonmeasurable outgoing neutrinos are discussed. We find that, once added, they enhance the sensitivity sizably, even if only imperfect information is provided. In addition to DNNs we also evaluate and compare other ML methods: boosted trees, random forests, and support vector machines. [ABSTRACT FROM AUTHOR]

In this paper we study the use of machine learning techniques to exploit kinematic information in V H, the production of a Higgs in association with a massive vector boson. We parametrize the effect of new physics in terms of the standard model effective field theory (SMEFT) framework. We find that the use of a shallow neural network allows us to dramatically increase the sensitivity to deviations in V H respect to previous estimates. We also discuss the relation between the usual measures of performance in machine learning, such as area under the curve or accuracy, with the more adept measure of Asimov significance. This relation is particularly relevant when parametrizing systematic uncertainties. Our results show the potential of incorporating machine learning techniques to the SMEFT studies using the current datasets. [ABSTRACT FROM AUTHOR]