Retrieval of Boost Invariant Symbolic Observables via Feature Importance

TL;DR

This paper introduces Boost Invariant Polynomials as an interpretable, low-dimensional alternative to deep learning for jet tagging in high-energy physics, focusing on extracting key physical observables efficiently.

Contribution

It presents a novel method that identifies important features analytically, reducing complexity and maintaining performance compared to traditional deep learning models.

Findings

Low-dimensional classifiers with minimal features perform nearly as well as full models.

The approach speeds up computation significantly.

Provides physically meaningful observables for jet tagging.

Abstract

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.