Aug 21 – 25, 2017
University of Washington, Seattle
US/Pacific timezone

Machine-Learning techniques for electro-magnetic showers identification in OPERA datasets

Aug 24, 2017, 4:00 PM
The Commons (Alder Hall)

The Commons

Alder Hall

Poster Track 2: Data Analysis - Algorithms and Tools Poster Session


Andrey Ustyuzhanin (Yandex School of Data Analysis (RU))


We investigate different approaches to the recognition of electromagnetic showers in the data which was collected by the international collaboration OPERA. The experiment initially was designed to detect neutrino oscillations, but the data collected can also be used for the development of the machine learning techniques for electromagnetic shower detection in photo emulsion films. Such showers may be used as signals of Dark Matter interaction. Due to the design of the detector and exposure time, emulsion films contain few million of traces of cosmic rays and around 1000 signal tracks attributed to single shower. We propose three different algorithms for the shower identification. All the algorithms achieve higher performance than baseline and can completely clean the detector volume from the background tracks saving about a half of the signal tracks.

Primary authors

Andrey Ustyuzhanin (Yandex School of Data Analysis (RU)) Sergey Shirobokov (Yandex School of Data Analysis (RU)) Mr Vladislav Belavin (CERN) Mr Artem Filatov (YSDA)

Presentation materials

Peer reviewing