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arXiv · 2106.11995

Deep Learning for direct Dark Matter search with nuclear emulsions

Artem Golovatiuk·Andrey Ustyuzhanin·Andrey Alexandrov·Giovanni De Lellis
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Abstract

We propose a new method for the discrimination of sub-micron nuclear recoil tracks from an instrumental background in fine-grain nuclear emulsions used in the directional dark matter search. The proposed method uses a 3D Convolutional Neural Network, whose parameters are optimised by Bayesian search. Unlike previous studies focused on extracting the directional information, we focus on the signal/background separation exploiting the polarisation dependence of the Localised Surface Plasmon Resonance phenomenon. Comparing the proposed method with the conventional cut-based approach shows a significant boost in the reduction factor for given signal efficiency.

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Artem Golovatiuk, Andrey Ustyuzhanin, Andrey Alexandrov, Giovanni De Lellis. 2021-06-22. Deep Learning for direct Dark Matter search with nuclear emulsions. https://doi.org/10.1016/j.cpc.2022.108312

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