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Deep neural network application: Higgs boson CP state mixing angle in H→ττ decay and at the LHC.
- Source :
-
Physical Review D: Particles, Fields, Gravitation & Cosmology . Feb2021, Vol. 103 Issue 3, p1-1. 1p. - Publication Year :
- 2021
-
Abstract
- 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]
- Subjects :
- *HIGGS bosons
*CP violation
*DISTRIBUTION (Probability theory)
*MACHINE learning
Subjects
Details
- Language :
- English
- ISSN :
- 24700010
- Volume :
- 103
- Issue :
- 3
- Database :
- Academic Search Index
- Journal :
- Physical Review D: Particles, Fields, Gravitation & Cosmology
- Publication Type :
- Periodical
- Accession number :
- 149082474
- Full Text :
- https://doi.org/10.1103/PhysRevD.103.036003