2-5 October 2017
Hotel Intourist Kolomenskoye 4*
Europe/Moscow timezone

Usage of machine learning for the separation of electroweak and strong $Z\gamma$ production

2 Oct 2017, 15:10
2h 50m
Petrovsky hall (Hotel Intourist Kolomenskoye 4*)

Petrovsky hall

Hotel Intourist Kolomenskoye 4*

Kashyrskoye shosse, 39B, Moscow, Russia, 115409
Poster High energy physics Poster session and coffee&reception

Speakers

Mr. Alexander Petukhov (NRNU MEPhI) Evgeny Soldatov (MEPhI)

Description

Separation of electroweak from strong $Z\gamma$ production is a very challenging task due to identical final states of such processes. The only difference is the origin of two leading jets. Rectangular cuts on jet kinematical variables from ATLAS Run1 $Z\gamma$ analysis were improved using machine learning techniques. New selection variables were also tested. The reached expected significance of separation for ATLAS Run2 conditions and 36 $fb^{-1}$ amount of data is $6\sigma$.

Primary author

Co-authors

Mr. Alexander Petukhov (NRNU MEPhI)

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