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Deep learning for Directional Dark Matter search
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 em...
Autores principales: | , , |
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Lenguaje: | eng |
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2020
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Materias: | |
Acceso en línea: | https://dx.doi.org/10.1088/1742-6596/1525/1/012108 http://cds.cern.ch/record/2722576 |
_version_ | 1780965905693409280 |
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author | Golovatiuk, Artem De Lellis, Giovanni Ustyuzhanin, Andrey |
author_facet | Golovatiuk, Artem De Lellis, Giovanni Ustyuzhanin, Andrey |
author_sort | Golovatiuk, Artem |
collection | CERN |
description | 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. |
id | cern-2722576 |
institution | Organización Europea para la Investigación Nuclear |
language | eng |
publishDate | 2020 |
record_format | invenio |
spelling | cern-27225762023-05-19T04:10:28Zdoi:10.1088/1742-6596/1525/1/012108http://cds.cern.ch/record/2722576engGolovatiuk, ArtemDe Lellis, GiovanniUstyuzhanin, AndreyDeep learning for Directional Dark Matter searchastro-ph.IMAstrophysics and AstronomyWe 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.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 $10^4$ background rejection power.arXiv:2005.13042oai:cds.cern.ch:27225762020-05-26 |
spellingShingle | astro-ph.IM Astrophysics and Astronomy Golovatiuk, Artem De Lellis, Giovanni Ustyuzhanin, Andrey Deep learning for Directional Dark Matter search |
title | Deep learning for Directional Dark Matter search |
title_full | Deep learning for Directional Dark Matter search |
title_fullStr | Deep learning for Directional Dark Matter search |
title_full_unstemmed | Deep learning for Directional Dark Matter search |
title_short | Deep learning for Directional Dark Matter search |
title_sort | deep learning for directional dark matter search |
topic | astro-ph.IM Astrophysics and Astronomy |
url | https://dx.doi.org/10.1088/1742-6596/1525/1/012108 http://cds.cern.ch/record/2722576 |
work_keys_str_mv | AT golovatiukartem deeplearningfordirectionaldarkmattersearch AT delellisgiovanni deeplearningfordirectionaldarkmattersearch AT ustyuzhaninandrey deeplearningfordirectionaldarkmattersearch |