Retrieval of Boost Invariant Symbolic Observables via Feature Importance
This addresses the need for interpretable and efficient feature extraction in high-energy physics for researchers analyzing jet data, though it is incremental as it builds on existing methods.
The paper tackles the problem of extracting key observables from black-box deep learning methods in jet tagging by introducing Boost Invariant Polynomials, which provide simple analytic expressions for important features, resulting in a low-dimensional classifier with relatively close performance to full-information methods and faster execution.
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.