تقرير
A multicategory jet image classification framework using deep neural network
| العنوان: | A multicategory jet image classification framework using deep neural network |
|---|---|
| المؤلفون: | Sandoval, Jairo Orozco, Manian, Vidya, Malik, Sudhir |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science High Energy Physics - Phenomenology |
| مصطلحات موضوعية: | High Energy Physics - Phenomenology, Computer Science - Machine Learning |
| الوصف: | Jet point cloud images are high dimensional data structures that needs to be transformed to a separable feature space for machine learning algorithms to distinguish them with simple decision boundaries. In this article, the authors focus on jet category separability by particle and jet feature extraction, resulting in more efficient training of a simple deep neural network, resulting in a computational efficient interpretable model for jet classification. The methodology is tested with three to five categories of jets from the JetNet benchmark jet tagging dataset, resulting in comparable performance to particle flow network. This work demonstrates that high dimensional datasets represented in separable latent spaces lead to simpler architectures for jet classification. Comment: 9 pages, y figures |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2407.03524 |
| رقم الأكسشن: | edsarx.2407.03524 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |