https://doi.org/10.1051/epjconf/201922202016
Optimization of the input space for deep learning data analysis in HEP.
1
Lomonosov Moscow State University, Skobeltsyn Institute of Nuclear Physics (SINP MSU),
1(2), Leninskie gory, GSP-1,
Moscow
119991,
Russian Federation
2
Faculty of Physics, Lomonosov Moscow State University,
Leninskie gory,
Moscow
119991,
Russian Federation
* e-mail: andrei.chernoded@cern.ch
** e-mail: lev.dudko@cern.ch
*** e-mail: georgii.vorotnikov@cern.ch
**** e-mail: petr.volkov@cern.ch
† e-mail: ovchinnikov.dm16@physics.msu.ru
‡ e-mail: maksim.perfilov@cern.ch
§ e-mail: shporin.artem@list.ru
Published online: 19 November 2019
Deep learning neural network technique is one of the most efficient and general approach of multivariate data analysis of the collider experiments. The important step of such analysis is the optimization of the input space for multivariate technique. In the article we propose the general recipe how to find the general set of low-level observables sensitive for the differences in the collider hard processes.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
