For the last 10 years, the search for dark matter (DM) was carried out taking into account the fact that the DM particles are WIMPs (Weakly Interacted Massive Particles) which were introduced in supersymmetric extensions of the Standard Model. Many experiments such as XENON1T, DarkSide, CRESST, etc. set the constraints on the WIMP-nucleon elastic interaction cross sections for different assumed WIMP masses. Methods for detecting WIMPs could play a special role, allowing one to determine the directions of the tracks of recoil nuclei and, therefore, to determine the preferred direction of the WIMP flux. In this work, we analyze the capabilities of such direct detection experiments through analyzing the lengths and directions of the tracks of recoil nuclei. Taking into account the existing experimental constraints, we conclude that the optimal target would be a lower density target containing nuclei of the CNO group, for example, liquid propane.

This work presents the complete modeling scheme of production and detection of two types of light dark matter (LDM)—Dirac fermionic and scalar particles—in a fixed target experiment using SHiP experiment as an example. The Drell-Yan process was chosen as a channel of LDM production; the deep inelastic scattering on lead nuclei was simulated and analyzed in the detector; the production of secondary particles was modeled with the aid of PYTHIA6 toolkit. Obtained observable parameters of secondary particles produced in events associated with LDM were compared with the background neutrino events that were simulated using GENIE toolkit. The yield of LDM events was calculated with various model parameter values. Using machine learning methods, a classifier that is able to distinguish LDM events from neutrino background events based on the observed parameters with high precision has been developed.