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arXiv 2406.03643astro-ph.HE
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通过瀑布图与深度学习实现GRB的Prompt阶段识别

Prompt GRB recognition through waterfalls and deep learning

Michela Negro, Nicoló Cibrario, Eric Burns, Joshua Wood, Adam Goldstein, Tito Dal Canton

AI总结:

针对伽马射线暴快速分类以支撑后续多波段观测的需求,该研究提出利用自监督深度学习结合全新的GRB瀑布图数据产品,构建鲁棒的GRB prompt阶段分类方法,为相关研究树立新里程碑。

摘要:

伽马射线暴(GRBs)是宇宙中能量最高的现象之一,对其的研究可探究地球实验室无法企及的极端物理环境。我们揭示这类事件起源、理解其底层物理机制的探索远未完成。这一研究的核心是对GRB进行快速分类,以指导电磁波段及其他领域的后续观测与分析。本文提出了一种极具价值的方法,可为构建全新且鲁棒的GRB prompt阶段分类方法树立里程碑。我们利用自监督深度学习,首次采用一种此前未被探索的数据产品——GRB瀑布图(GRB waterfalls)来开展这项任务。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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