Deep Learning for direct Dark Matter search with nuclear emulsions

Artem Golovatiuk, Andrey Ustyuzhanin, Andrey Alexandrov, Giovanni De Lellis

Published 2021-06-22Version 1

We propose a new method for discriminating sub-micron nuclear recoil tracks from an instrumental background in fine-grain nuclear emulsions used in the directional dark matter search. The proposed method is based on the Deep Learning approach and uses a 3D Convolutional Neural Network architecture with parameters 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 rejection power while keeping the signal efficiency at the same level.