Application of Deep Learning Technique to an Analysis of Hard Scattering Processes at Colliders
Data Analysis, Statistics and Probability
2021-09-20 v1 High Energy Physics - Experiment
High Energy Physics - Phenomenology
Abstract
Deep neural networks have rightfully won the place of one of the most accurate analysis tools in high energy physics. In this paper we will cover several methods of improving the performance of a deep neural network in a classification task in an instance of top quark analysis. The approaches and recommendations will cover hyperparameter tuning, boosting on errors and AutoML algorithms applied to collider physics.
Cite
@article{arxiv.2109.08520,
title = {Application of Deep Learning Technique to an Analysis of Hard Scattering Processes at Colliders},
author = {Lev Dudko and Petr Volkov and Georgii Vorotnikov and Andrei Zaborenko},
journal= {arXiv preprint arXiv:2109.08520},
year = {2021}
}
Comments
Proceeding of the DLCP'21 conference. https://theory.sinp.msu.ru/doku.php/dlcp21/about