Abstract
The consecutive steps of cascade decay initiated by H to tau tau 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 multi-dimensional signatures of the tau^pm to pi^pm pi^0 nu and tau^pm to 3pi^pm nu 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 to tau tau matrix element on the mixing angle is predicted by theory. The potential to determine preferred mixing angle of the Higgs boson events sample including $\tau$-decays is studied using Deep Neural Network. The problem is adressed 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 methods are evaluated and compared. Numerical results are collected.
Highlights
Machine learning (ML) techniques find increasing number of applications in high energy physics (HEP) phenomenology
In the previous papers we have found that multidimensional signatures of the τÆ → πÆπ0ν and τÆ → 3πÆν decays can be used to distinguish between scalar and pseudoscalar Higgs state
In the scope of our interest was the kinematics of outgoing decay products of the τ leptons and geometry of decay vertices. With these concerns in mind, in the following we extend our previous work on the physics of the Higgs CP parity scalar/pseudoscalar classification, to a measurement of scalar-pseudoscalar mixing angle φCP of the Hττ coupling
Summary
Machine learning (ML) techniques find increasing number of applications in high energy physics (HEP) phenomenology. In this paper we present how ML techniques can be helpful to exploit substructure of the hadronically decaying τ leptons in the measurement of the Higgs boson CP-state mixing angle in H → ττ decay. Identification of neutrinos orientation angles is helpful to develop intuition on the observable structure, as it is the most difficult to grab, to discuss systematic in contributions from τ → 2π and τ → 3π decay modes Such studies are of a value to cross check ML results. In the scope of our interest was the kinematics of outgoing decay products of the τ leptons and geometry of decay vertices With these concerns in mind, in the following we extend our previous work on the physics of the Higgs CP parity scalar/pseudoscalar classification, to a measurement of scalar-pseudoscalar mixing angle φCP of the Hττ coupling.
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