10-15 March 2019
Steinmatte conference center
Europe/Zurich timezone

Deep learning for Directional Dark Matter search

Not scheduled
20m
Steinmatte conference center

Steinmatte conference center

Hotel Allalin, Saas Fee, Switzerland https://allalin.ch/conference/
Poster Track 2: Data Analysis - Algorithms and Tools Poster Session

Speaker

Artem Golovatiuk (University of Naples (IT))

Description

The NEWSdm (Nuclear Emulsions for WIMP Search directional measure) is an underground Direct detection Dark Matter (DM) search experiment. The usage of recent developments in the nuclear emulsions allows probing new regions in the WIMP parameter space. The prominent feature of this experiment is a potential of recording the signal direction, which gives a chance of overcoming the "neutrino floor".

State of the art techniques lower the background contamination significantly, however, background rejection remains crucial for DM sensitivity. Deep Neural Networks were used for separation between potential DM signal and various classes of background.

In this work, we present the usage of deep 3D Convolutional Neural Networks in order to take into account the physical peculiarities of the data and achieve strong background rejection.

Primary authors

Artem Golovatiuk (University of Naples (IT)) Giovanni De Lellis (Universita e sezione INFN di Napoli (IT)) Andrey Ustyuzhanin (Yandex School of Data Analysis (RU))

Presentation materials

Peer reviewing

Paper