Exotic and physics-informed support vector machines for high energy physics

Autor:	Ramirez-Morales, A., Gutiérrez-Rodríguez, A., Cisneros-Pérez, T., Garcia-Tecocoatzi, H., Dávila-Rivera, A.
Rok vydání:	2024
Předmět:	High Energy Physics - Experiment Physics - Data Analysis Statistics and Probability
Druh dokumentu:	Working Paper
Popis:	In this article, we explore machine learning techniques using support vector machines with two novel approaches: exotic and physics-informed support vector machines. Exotic support vector machines employ unconventional techniques such as genetic algorithms and boosting. Physics-informed support vector machines integrate the physics dynamics of a given high-energy physics process in a straightforward manner. The goal is to efficiently distinguish signal and background events in high-energy physics collision data. To test our algorithms, we perform computational experiments with simulated Drell-Yan events in proton-proton collisions. Our results highlight the superiority of the physics-informed support vector machines, emphasizing their potential in high-energy physics and promoting the inclusion of physics information in machine learning algorithms for future research. Comment: 9 pages, 2 figures, 3 tables
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2407.03538 Zobrazit plný text záznamu View this record from Arxiv