Possibilities of Detecting Light Dark Matter Produced via Drell-Yan Channel in a Fixed Target Experiment
Eduard Ursov,
Anna Anokhina,
Emil Khalikov,
Ivan Vidulin,
Tatiana Roganova
Affiliations
Eduard Ursov
Department of Physics, Federal State Budget Educational Institution of Higher Education, M.V. Lomonosov Moscow State University, 1(2), Leninskie gory, GSP-1, 119991 Moscow, Russia
Anna Anokhina
Department of Physics, Federal State Budget Educational Institution of Higher Education, M.V. Lomonosov Moscow State University, 1(2), Leninskie gory, GSP-1, 119991 Moscow, Russia
Emil Khalikov
Skobeltsyn Institute of Nuclear Physics (SINP MSU), Federal State Budget Educational Institution of Higher Education, M.V. Lomonosov Moscow State University,1(2), Leninskie gory, GSP-1, 119991 Moscow, Russia
Ivan Vidulin
Department of Physics, Federal State Budget Educational Institution of Higher Education, M.V. Lomonosov Moscow State University, 1(2), Leninskie gory, GSP-1, 119991 Moscow, Russia
Tatiana Roganova
Skobeltsyn Institute of Nuclear Physics (SINP MSU), Federal State Budget Educational Institution of Higher Education, M.V. Lomonosov Moscow State University,1(2), Leninskie gory, GSP-1, 119991 Moscow, Russia
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.
