Exotic and physics-informed support vector machines for high energy physics
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866911944168964096 |
|---|---|
| author | Ramirez-Morales, A. Gutiérrez-Rodríguez, A. Cisneros-Pérez, T. Garcia-Tecocoatzi, H. Dávila-Rivera, A. |
| author_facet | Ramirez-Morales, A. Gutiérrez-Rodríguez, A. Cisneros-Pérez, T. Garcia-Tecocoatzi, H. Dávila-Rivera, A. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_03538 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Exotic and physics-informed support vector machines for high energy physics Ramirez-Morales, A. Gutiérrez-Rodríguez, A. Cisneros-Pérez, T. Garcia-Tecocoatzi, H. Dávila-Rivera, A. High Energy Physics - Experiment Data Analysis, Statistics and Probability 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. |
| title | Exotic and physics-informed support vector machines for high energy physics |
| topic | High Energy Physics - Experiment Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2407.03538 |