Fully Harnessing the Potential of Machine Learning to Expand the Discovery Potential of the LHC

The Large Hadron Collider (LHC) has continued to push its search for new physics to higher mass ranges. However, it has so far failed to find any signs of new physics beyond the Standard Model (SM). This may mean that signs of new physics, assuming it exists in the mass range probed by the LHC, must be in more extreme regions of phase space or will require significantly more data to be discovered. The LHC has so far only probed ~1% of the data we expect to collect over the next ~20 years and there are vast regions of phase space that are not accessible either due to algorithmic constraints or insufficient data being collected so far. To ensure the data is fully exploited in the search for new physics, and that all the possible regions of phase space are explored, a paradigm shift must occur in the use of machine learning (ML) at the LHC to ensure that optimal use is made of all available data. This project will use lower level detector information coupled with cutting-edge ML techniques to boost the performance of the reconstruction algorithms, specifically those which identify b-quarks (b-tagging). As b-tagging is used in the majority of the results produced by ATLAS, improving the b-tagging algorithms will have a big impact on the vast majority of the physics programmes of the LHC, helping to significantly boost the discovery potential of the LHC.