arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2005.06406astro-ph.HEastro-ph.IM
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多信使瞬态的数据驱动检测

Data-driven detection of multi-messenger transients

Iftach Sadeh

摘要:

爆炸性天体物理瞬变研究的主要挑战是使用多个信使对其进行检测和表征。为此,我们开发了一个新的基于深度学习的数据驱动发现框架。我们展示了它的使用搜索涉及中微子,光学超新星,伽马射线。我们表明,我们可以匹配或大幅提高最先进技术的性能,同时显着减少对建模和仪器表征的依赖。特别是,我们的方法是为了近实时分析,这是必不可少的有效后续检测。我们的算法旨在结合一系列的工具和类型的输入数据,代表不同的信使,物理制度和时间尺度。该方法针对意外现象的不可知论搜索进行了优化,并有可能大幅提高其发现前景。

英文摘要:

The primary challenge in the study of explosive astrophysical transients is their detection and characterisation using multiple messengers. For this purpose, we have developed a new data-driven discovery framework, based on deep learning. We demonstrate its use for searches involving neutrinos, optical supernovae, and gamma rays. We show that we can match or substantially improve upon the performance of state-of-the-art techniques, while significantly minimising the dependence on modelling and on instrument characterisation. Particularly, our approach is intended for near- and real-time analyses, which are essential for effective follow-up of detections. Our algorithm is designed to combine a range of instruments and types of input data, representing different messengers, physical regimes, and temporal scales. The methodology is optimised for agnostic searches of unexpected phenomena, and has the potential to substantially enhance their discovery prospects.

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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