Accelerating dark matter search in emulsion SHiP detector by deep learning

Abstract

Abstract We introduce a novel approach for the reconstruction of particle properties for the SHiP detector. The SHiP experiment significantly focuses on finding effects of dark matter particle interaction. A characteristic trace of such an interaction is an electromagnetic shower. Our algorithm aims to reconstruct the energy and origin of such showers using online Target Tracker subdetectors that do not suffer from pile-up. Thus, the online observation of the excess of events with proper energy can be a signal for a dark matter. Two different approaches were applied: classical, using Gaussian Mixtures and machine learning based on a convolutional neural network. We’ve refined the output of the previous step by clusterization techniques to improve transverse coordinate estimation. The obtained results are 25% for energy resolution, 0.8 cm for position resolution in the longitudinal direction and 1 mm in the transverse direction, without any usage of the emulsion.