Retrieval of Boost Invariant Symbolic Observables via Feature Importance

• URL: http://arxiv.org/abs/2306.13496v1
• Date: Fri, 23 Jun 2023 13:41:06 GMT
• Title: Retrieval of Boost Invariant Symbolic Observables via Feature Importance
• Authors: Jose M Munoz and Ilyes Batatia and Christoph Ortner and Francesco Romeo
• Abstract summary: 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. 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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