2024/07/03 by A. Ramirez-Morales, A. Gutiérrez-Rodríguez, Ramirez-Morales, A. +7
Computer Science · Physics and Astronomy · #Computational Physics and Python Applications #Data Analysis #FOS: Physical sciences #High Energy Physics - Experiment (hep-ex) #Particle Detector Development and Performance #Particle physics theoretical and experimental studies #Statistics and Probability (physics.data-an)
paper · pdf · doi:10.48550/arxiv.2407.03538
openalex publication_date 2024/07/03 · openalex created_date 2024/07/09 · openalex updated_date 2026/07/28
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.