The Standard Model of elementary particles does not contain a proper Dark Matter candidate. One of the most tantalizing theoretical developments is the so-called Hidden Valley models: a mirror-like copy of the Standard Model, with dark particles that communicate with standard ones via a very feeble interaction. These models predict the existence of dark hadrons – composite particles that are bound similarly to ordinary hadrons in the Standard Model. Such dark hadrons can be abundantly produced in high-energy proton-proton collisions, making the LHC a unique place to search for them. Some dark hadrons are stable like a proton, which makes them excellent Dark Matter candidates, while others decay to ordinary particles after flying a certain distance in the collider experiment. The LHCb detector has a unique capability to identify such decays, particularly if the new particles have a mass below ten times the proton mass. 

This project assumes a unique search for light dark hadrons that covers a mass range not accessible to other experiments. It assumes an interesting program on data analysis (python-based) with non-trivial machine learning solutions and phenomenology research using fast simulation framework. Depending on the interest, there is quite a bit of flexibility in the precise focus of the project.


Andrii Usachov

Masters Thesis Projects