📝 SummarXiv

Abstract

Many theories of physics beyond the Standard Model predict particles with non-helical trajectories in a uniform magnetic field, but standard tracking algorithms assume helical paths and so are incapable of discovering non-helical tracks. While alternative algorithms have been developed for specific trajectories, unforeseen physics could lead to unanticipated behavior, and such unexpected tracks are largely invisible to current algorithms, despite being potentially striking to the naked eye. A model-agnostic tracking algorithm is presented, capable of reconstructing a broad class of smooth non-helical tracks without requiring explicit specification of particle trajectories, instead defining the target trajectories implicitly in the training sample. The network exhibits strong performance, even outside of the trajectories defined by the training sample. This proof-of-principle study takes the first step towards searches for unexpected tracks which may await discovery in current data.