Deep learning for Directional Dark Matter search
May 26, 2020
5 pages
Published in:
- J.Phys.Conf.Ser. 1525 (2020) 1, 012108
Contribution to:
- Published: Jul 8, 2020
e-Print:
- 2005.13042 [astro-ph.IM]
Experiments:
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Abstract: (IOP)
We provide an algorithm for detection of possible dark matter particle interactions recorded within NEWSdm detector. The NEWSdm (Nuclear Emulsions for WIMP Search directional measure) is an underground Direct detection Dark Matter search experiment. The usage of recent developments in the nuclear emulsions allows probing new regions in the WIMP parameter space. The directional approach, which is the key feature of the NEWSdm experiment, gives the unique chance of overcoming the neutrino floor. Deep Neural Networks were used for separation between potential DM signal and various classes of background. In this paper, we present the usage of deep 3D Convolutional Neural Networks to take into account the physical peculiarities of the datasets and report the achievement of the required 104 background rejection power.Note:
- 5 pages, 6 figures. This is a proceedings paper from the ACAT2019 conference: https://indico.cern.ch/event/708041
- dark matter: direct detection
- dark matter: potential
- particle: interaction
- nuclear emulsion
- neural network
- background
- neutrino
- dimension: 3
- WIMP
References(8)
Figures(6)