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arXiv · 2002.09350

General recipe to form input space for deep learning analysis of HEP scattering processes

Andrei Chernoded·Lev Dudko·Georgi Vorotnikov·Petr Volkov·Dmitri Ovchinnikov·Maxim Perfilov·Artem Shporin
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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.

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Andrei Chernoded, Lev Dudko, Georgi Vorotnikov, Petr Volkov, Dmitri Ovchinnikov, Maxim Perfilov, Artem Shporin. 2020-02-21. General recipe to form input space for deep learning analysis of HEP scattering processes. https://doi.org/10.1142/s0217751x20501195

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