تقرير
Classification of Fermi Gamma-Ray Bursts Based on Machine Learning
| العنوان: | Classification of Fermi Gamma-Ray Bursts Based on Machine Learning |
|---|---|
| المؤلفون: | Zhu, Si-Yuan, Sun, Wan-Peng, Ma, Da-Ling, Zhang, Fu-Wen |
| المصدر: | MNRAS, 2024, 532, 1434-1443 |
| سنة النشر: | 2024 |
| المجموعة: | Astrophysics |
| مصطلحات موضوعية: | Astrophysics - High Energy Astrophysical Phenomena |
| الوصف: | Gamma-ray bursts (GRBs) are typically classified into long and short GRBs based on their durations. However, there is a significant overlapping in the duration distributions of these two categories. In this paper, we apply the unsupervised dimensionality reduction algorithm called t-SNE and UMAP to classify 2061 Fermi GRBs based on four observed quantities: duration, peak energy, fluence, and peak flux. The map results of t-SNE and UMAP show a clear division of these GRBs into two clusters. We mark the two clusters as GRBs-I and GRBs-II, and find that all GRBs associated with supernovae are classified as GRBs-II. It includes the peculiar short GRB 200826A, which was confirmed to originate from the death of a massive star. Furthermore, except for two extreme events GRB 211211A and GRB 230307A, all GRBs associated with kilonovae fall into GRBs-I population. By comparing to the traditional classification of short and long GRBs, the distribution of durations for GRBs-I and GRBs-II do not have a fixed boundary. We find that more than 10% of GRBs-I have a duration greater than 2 seconds, while approximately 1% of GRBs-II have a duration shorter than 2 seconds. Comment: 11 pages, 5 figures, revised version submitted to MNRAS |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2406.05357 |
| رقم الأكسشن: | edsarx.2406.05357 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |