Speaker
Sophia Vent
Description
Established Machine learning taggers are a perfect challenge for explainability concepts in a fundamental physics context. For the theoretically challenging quark-gluon tagging, we first identify a small set of learned latent features that correlate strongly with physics observables. Then we use symbolic regression to derive compact analytic expressions to approximate the tagger in terms of these observables.
