Axion searches at the LHC is a novel and largely unexplored possibility. Theoretical studies have shown that data collected by the LHC are largely complementary to other experiments and probe regions of the available parameter space far from their reach.
The study has also to take into account the possibility of long-lived axion, which further complicates the study. The full exploitation of the physics potential of LHC has also to take advantage of advanced data-analysis techniques, including deep neural networks for pattern regognition. The project will aim to develop such techniques to achieve the best possible physics result out of the ATLAS data.
Student: Adam Ruby
Supervisor: Nikolaos Rompotis
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