Prompt GRB recognition through waterfalls and deep learning
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929725331472384 |
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| author | Negro, Michela Cibrario, Nicoló Burns, Eric Wood, Joshua Goldstein, Adam Canton, Tito Dal |
| author_facet | Negro, Michela Cibrario, Nicoló Burns, Eric Wood, Joshua Goldstein, Adam Canton, Tito Dal |
| contents | Gamma-ray Bursts (GRBs) are one of the most energetic phenomena in the cosmos, whose study probes physics extremes beyond the reach of laboratories on Earth. Our quest to unravel the origin of these events and understand their underlying physics is far from complete. Central to this pursuit is the rapid classification of GRBs to guide follow-up observations and analysis across the electromagnetic spectrum and beyond. Here, we introduce a compelling approach that can set milestone towards a new and robust GRB prompt classification method. Leveraging self-supervised deep learning, we pioneer a previously unexplored data product to approach this task: the GRB waterfalls. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_03643 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Prompt GRB recognition through waterfalls and deep learning Negro, Michela Cibrario, Nicoló Burns, Eric Wood, Joshua Goldstein, Adam Canton, Tito Dal High Energy Astrophysical Phenomena Gamma-ray Bursts (GRBs) are one of the most energetic phenomena in the cosmos, whose study probes physics extremes beyond the reach of laboratories on Earth. Our quest to unravel the origin of these events and understand their underlying physics is far from complete. Central to this pursuit is the rapid classification of GRBs to guide follow-up observations and analysis across the electromagnetic spectrum and beyond. Here, we introduce a compelling approach that can set milestone towards a new and robust GRB prompt classification method. Leveraging self-supervised deep learning, we pioneer a previously unexplored data product to approach this task: the GRB waterfalls. |
| title | Prompt GRB recognition through waterfalls and deep learning |
| topic | High Energy Astrophysical Phenomena |
| url | https://arxiv.org/abs/2406.03643 |