https://doi.org/10.1140/epjc/s10052-020-08713-1
Regular Article - Experimental Physics
Comparing traditional and deep-learning techniques of kinematic reconstruction for polarization discrimination in vector boson scattering
1
Universitá di Pavia, Dipartimento di Fisica and INFN, Sezione di Pavia, Via A. Bassi 6, 27100, Pavia, Italy
2
IBM Italia s.p.a. Circonvallazione Idroscalo, 20090, Segrate, MI, Italy
3
Faculty of Mathematics and Physics, University of Ljubljana, Jadranska cesta 19, 1000, Ljubljana, Slovenia
4
Jožef Stefan Institute, Jamova cesta 39, 1000, Ljubljana, Slovenia
c
borut.kersevan@fmf.uni-lj.si
Received:
12
August
2020
Accepted:
26
November
2020
Published online:
11
December
2020
Measuring longitudinally polarized vector boson scattering in 
channel is a promising way to investigate unitarity restoration with the Higgs mechanism and to search for possible physics beyond the Standard Model. In order to perform such a measurement, it is crucial to develop an efficient reconstruction of the full 
boson kinematics in leptonic decays with the focus on polarization measurements. We investigated several approaches, from traditional ones up to advanced deep neural network structures, and we compared their abilities in reconstructing the boson reference frame and in consequently measuring the longitudinal fraction 
in both semi-leptonic and fully-leptonic decay channels.
© The Author(s) 2020
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Funded by SCOAP3
