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Comparing Traditional and Deep-Learning Techniques of Kinematic Reconstruction for polarisation Discrimination in Vector Boson Scattering by Grossi, M.; Novak, J.; Kersevan, B.; Rebuzzi, D. is a scholarly article available to read on EtoBox.

What is Comparing Traditional and Deep-Learning Techniques of Kinematic Reconstruction for polarisation Discrimination in Vector Boson Scattering about?

Measuring longitudinally polarised vector boson scattering in WW 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 W boson kinematics in leptonic decays with the focus on polarisation measurements. We investigated several approaches, from traditional ones up to advanced deep neural network structures, and we compared their ability to reconstruct the W boson reference frame and to consequently measure the longitudinal fraction W_L in both semi-leptonic and di-leptonic WW decay channels.

Author

Grossi, M.; Novak, J.; Kersevan, B.; Rebuzzi, D.

Published

2020

Language

EN