Decoding the nature of Dark Matter at current and future experiments

Abstract

Decoding the nature of Dark Matter (DM) as a crucial part of Beyond-the-Standard-Model (BSM) theory is one of the most important problems of modern particle physics. DM potentially provides unique signatures at collider and non-collider experiments. These signatures are quite generic, however their details could allow us to delineate various BSM models and the properties of DM. While there are many comprehensive studies of the phenomenology of various appealing BSM models, exhibiting “top-bottom” approach, there is no clear strategy for the reverse task of identifying the underlying theory from the new signatures. To solve this problem one should consider the comprehensive set of signatures, database of models and use modern methods, including machine learning and artificial intelligence, to decode the underlying theory from potential signals of new physics we are expecting from the coming experimental data. One of the tools which could be helpful to solve the problem is High Energy Physics Model Database (HEPMDB) which was created to make a step forward towards solving this problem. It is aimed to facilitate connection between HEP theory and experiment, to store, validate and explore BSM models and to collect their signatures. DM decoding is based on the unique complementarity of Large Hadron Collider (LHC) potential as well as on the potential DM direct and indirect detection experiments to probe DM. The combination of our knowledge on this complementarity, modern analysis methods, comprehensive database of BSM models and their signatures is the key point of decoding the nature of DM and the whole underlying theory of Nature.