Retrieval of Boost Invariant Symbolic Observables via Feature Importance
Jose M Munoz Ilyes Batatia Christoph Ortner Francesco Romeo
Deep learning approaches for jet tagging in high-energy physics are
characterized as black boxes that process a large amount of information from
which it is difficult to extract key distinctive observables. In this
proceeding, we present an alternative to deep learning approaches, Boost
Invariant Polynomials, which enables direct analysis of simple analytic
expressions representing the most important features in a given task. Further,
we show how this approach provides an extremely low dimensional classifier with
a minimum set of features representing %effective discriminating physically
relevant observables and how it consequently speeds up the algorithm execution,
with relatively close performance to the algorithm using the full information.