General recipe to form input space for deep learning analysis of HEP scattering processes
High Energy Physics - Phenomenology
2020-08-26 v2 Data Analysis, Statistics and Probability
Abstract
Deep learning neural network technique (DNN) is one of the most efficient and general approach of multivariate data analysis of the collider experiments. The important step of the analysis is the optimization of the input space for multivariate technique. In the article we propose the general recipe how to form the set of low-level observables sensitive for the differences in hard scattering processes at the colliders. It is shown in the paper that without any sophisticated analysis of the kinematic properties one can achieve close to optimal performance of DNN with the proposed general set of low-level observables.
Cite
@article{arxiv.2002.09350,
title = {General recipe to form input space for deep learning analysis of HEP scattering processes},
author = {Andrei Chernoded and Lev Dudko and Georgi Vorotnikov and Petr Volkov and Dmitri Ovchinnikov and Maxim Perfilov and Artem Shporin},
journal= {arXiv preprint arXiv:2002.09350},
year = {2020}
}