Speaker
Description
The Standard Model (SM) of particle physics is an extremely successful theory, and in many areas, it agrees to extremely high precision with experimental measurements; however, several shortcomings—such as the lack of a feasible dark matter candidate or the existence of non-zero neutrino masses—fuel belief that it is not the final theory of Nature. Despite the decades-long experimental and theoretical effort, the nature of this final theory remains elusive. One of the challenges to this effort is the breadth of signatures that new physics might produce, which would require many searches. In the contribution, I will present a machine-learning-based model-agnostic search for rare events in data from the IceCube Neutrino Observatory. This search looks for events that deviate from the expected background distribution without specifying the underlying physics model.
