Searching for New Physics with the CMS experiment at the LHC

The CMS experiment at the Large Hadron Collider has already made ground-breaking discoveries since it started operating in 2010. However, the long awaited Run 3 of the LHC has now started, and is set to double the entire dataset currently collected by CMS. This will lead to an unprecedented amount of data which will need to be carefully scrutinised to make sure we leave no stone unturned in our search for new physics. In particular, the search for signatures which exhibit large amounts of missing transverse momentum - a characteristic of particles escaping undetected - are especially interesting as they cast a wide net for new physics, including dark matter candidates and more exotic models such as split-susy. In this project we will develop machine learning (ML) algorithms to significantly enhance the discovery prospects from our newly collected data; a variety of different ML techniques will be studied to sift through the hundreds of millions of collision events looking for signs of new physics. These algorithms can be applied at all stages of analysis from trigger and reconstruction to event selection and background estimation.