Classifier Surrogates: Sharing AI-based Searches with the World

Understand

In recent years, neural network-based classification has been used to improve data analysis at collider experiments.

• While this strategy proves to be hugely successful, the underlying models are not commonly shared with the public and rely on experiment-internal data as well as full detector simulations.
• We show a concrete implementation of a newly proposed strategy, so-called Classifier Surrogates, to be trained inside the experiments, that only utilise publicly accessible features and truth information.
• These surrogates approximate the original classifier distribution, and can be shared with the public.