Jet Image Tagging Using Deep Learning: An Ensemble Model
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866915445889564672 |
|---|---|
| author | Bassa, Juvenal Manian, Vidya Malik, Sudhir Chattopadhyay, Arghya |
| author_facet | Bassa, Juvenal Manian, Vidya Malik, Sudhir Chattopadhyay, Arghya |
| contents | Jet classification in high-energy particle physics is important for understanding fundamental interactions and probing phenomena beyond the Standard Model. Jets originate from the fragmentation and hadronization of quarks and gluons, and pose a challenge for identification due to their complex, multidimensional structure. Traditional classification methods often fall short in capturing these intricacies, necessitating advanced machine learning approaches. In this paper, we employ two neural networks simultaneously as an ensemble to tag various jet types. We convert the jet data to two-dimensional histograms instead of representing them as points in a higher-dimensional space. Specifically, this ensemble approach, hereafter referred to as Ensemble Model, is used to tag jets into classes from the JetNet dataset, corresponding to: Top Quarks, Light Quarks (up or down), and W and Z bosons. For the jet classes mentioned above, we show that the Ensemble Model can be used for both binary and multi-categorical classification. This ensemble approach learns jet features by leveraging the strengths of each constituent network achieving superior performance compared to either individual network. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_10034 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Jet Image Tagging Using Deep Learning: An Ensemble Model Bassa, Juvenal Manian, Vidya Malik, Sudhir Chattopadhyay, Arghya Data Analysis, Statistics and Probability Artificial Intelligence Machine Learning High Energy Physics - Experiment High Energy Physics - Phenomenology Jet classification in high-energy particle physics is important for understanding fundamental interactions and probing phenomena beyond the Standard Model. Jets originate from the fragmentation and hadronization of quarks and gluons, and pose a challenge for identification due to their complex, multidimensional structure. Traditional classification methods often fall short in capturing these intricacies, necessitating advanced machine learning approaches. In this paper, we employ two neural networks simultaneously as an ensemble to tag various jet types. We convert the jet data to two-dimensional histograms instead of representing them as points in a higher-dimensional space. Specifically, this ensemble approach, hereafter referred to as Ensemble Model, is used to tag jets into classes from the JetNet dataset, corresponding to: Top Quarks, Light Quarks (up or down), and W and Z bosons. For the jet classes mentioned above, we show that the Ensemble Model can be used for both binary and multi-categorical classification. This ensemble approach learns jet features by leveraging the strengths of each constituent network achieving superior performance compared to either individual network. |
| title | Jet Image Tagging Using Deep Learning: An Ensemble Model |
| topic | Data Analysis, Statistics and Probability Artificial Intelligence Machine Learning High Energy Physics - Experiment High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2508.10034 |