Deep Neural Network application: Higgs boson CP state mixing angle in H to tau tau decay and at LHC

TL;DR

This paper explores using deep neural networks to measure the Higgs boson parity mixing angle in tau decay channels at the LHC, enhancing the precision of Higgs property determination.

Contribution

It extends machine learning methods to not only classify Higgs CP states but also to estimate the mixing angle from complex tau decay signatures.

Findings

Deep neural networks can effectively determine the Higgs mixing angle.

The approach improves sensitivity over traditional methods.

Numerical results demonstrate the method's potential for LHC analyses.

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.