| Issue |
EPJ Web Conf.
Volume 222, 2019
The XXIV International Workshop “High Energy Physics and Quantum Field Theory” (QFTHEP 2019)
|
|
|---|---|---|
| Article Number | 02016 | |
| Number of page(s) | 6 | |
| Section | Experiment / Detectors / Data analysis | |
| DOI | https://doi.org/10.1051/epjconf/201922202016 | |
| Published online | 19 November 2019 | |
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
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