Interpretable deep learning models for the inference and classification of LHC data
(2024)
Journal Article
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
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 differen... Read More about Interpretable deep learning models for the inference and classification of LHC data.