  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.
