Interpretable deep learning models for the inference and classification of LHC data

Ngairangbam, Vishal S.; Spannowsky, Michael

Abstract

The Shower Deconstruction methodology is pivotal in distinguishing signal and background jets, leveraging the detailed information from perturbative parton showers. Rooted in the Neyman-Pearson lemma, this method is theoretically designed to differentiate between signal and background processes optimally in high-energy physics experiments. A key challenge, however, arises from the combinatorial growth associated with increasing jet constituents, which hampers its computational feasibility. We address this by demonstrating that the likelihood derived from comparing the most probable signal and background shower histories is equally effective for discrimination as the conventional approach of summing over all potential histories in top quark versus Quantum Chromodynamics (QCD) scenarios. We propose a novel approach by conceptualising the identification of the most probable shower history as a Markov Decision Process (MDP). Utilising a sophisticated modular point-transformer architecture, our method efficiently learns the optimal policy for this task. The developed neural agent excels in constructing the most likely shower history and demonstrates robust generalisation capabilities on unencountered test data. Remarkably, our approach mitigates the complexity inherent in the inference process, achieving a linear scaling relationship with the number of jet constituents. This offers a computationally viable and theoretically sound method for signal-background differentiation, paving the way for more effective data analysis in particle physics.

Citation

Ngairangbam, V. S., & Spannowsky, M. (2024). Interpretable deep learning models for the inference and classification of LHC data. Journal of High Energy Physics, 2024(5), Article 4. https://doi.org/10.1007/jhep05%282024%29004

• Journal Article Type: Article
• Acceptance Date: Apr 3, 2024
• Online Publication Date: May 2, 2024
• Publication Date: 2024-05
• Deposit Date: May 17, 2024
• Publicly Available Date: May 17, 2024
• Journal: Journal of High Energy Physics
• Print ISSN: 1126-6708
• Electronic ISSN: 1029-8479
• Publisher: Scuola Internazionale Superiore di Studi Avanzati (SISSA)
• Peer Reviewed: Peer Reviewed
• Volume: 2024
• Issue: 5
• Article Number: 4
• DOI: https://doi.org/10.1007/jhep05%282024%29004
• Keywords: Jets and Jet Substructure, Parton Shower
• Public URL: https://durham-repository.worktribe.com/output/2437323