Deep Learning for Multi-Messenger Astrophysics: A Gateway for Discovery in the Big Data Era
Deep Learning for Multi-Messenger Astrophysics: A Gateway for Discovery in the Big Data Era
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发表时间：
2019-02
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通讯作者：
Gabrielle Allen;I. Andreoni;E. Bachelet;G. Berriman;F. Bianco;R. Biswas;M. Kind;K. Chard;
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作者：
Gabrielle Allen;I. Andreoni;E. Bachelet;G. Berriman;F. Bianco;R. Biswas;M. Kind;K. Chard;
本报告概述了最近的工作，这些工作利用大数据革命和大规模计算来解决多信使天体物理学中的巨大计算挑战，特别强调实时发现活动。鉴于多信使天体物理学的跨学科性质，本文件由物理学、天文学、计算机科学、数据科学、软件和网络基础设施社区的成员编写，他们参加了由 NSF、DOE 和 NVIDIA 资助的“多信使天体物理学的深度学习：大规模实时发现”研讨会，该研讨会于 2018 年 10 月 17 日至 19 日在国家超级计算应用中心举办。一致认为，加速新型信号处理算法的开发和部署至关重要，这些算法利用人工智能 (AI) 和高性能计算之间的协同作用，最大限度地发挥多信使天体物理学的科学发现潜力。我们讨论了实现这一努力的关键方面，即（i）为多信使天体物理学设计和开发可扩展且计算高效的人工智能算法； (ii) 对天体物理源进行数值模拟以及处理和解释多信使天体物理数据的网络基础设施要求； (iii) 管理引力波探测和触发，以实现电磁和天体粒子后续行动； (iv) 利用机器和深度学习以及网络基础设施资源的未来发展来应对大数据时代的发现规模的愿景； (v) 需要建立一个社区，将领域专家与数据科学家平等地聚集在一起，以最大限度地提高和加速多信使天体物理学这一新兴领域的发现。
This report provides an overview of recent work that harnesses the Big Data Revolution and Large Scale Computing to address grand computational challenges in Multi-Messenger Astrophysics, with a particular emphasis on real-time discovery campaigns. Acknowledging the transdisciplinary nature of Multi-Messenger Astrophysics, this document has been prepared by members of the physics, astronomy, computer science, data science, software and cyberinfrastructure communities who attended the NSF-, DOE- and NVIDIA-funded "Deep Learning for Multi-Messenger Astrophysics: Real-time Discovery at Scale" workshop, hosted at the National Center for Supercomputing Applications, October 17-19, 2018. Highlights of this report include unanimous agreement that it is critical to accelerate the development and deployment of novel, signal-processing algorithms that use the synergy between artificial intelligence (AI) and high performance computing to maximize the potential for scientific discovery with Multi-Messenger Astrophysics. We discuss key aspects to realize this endeavor, namely (i) the design and exploitation of scalable and computationally efficient AI algorithms for Multi-Messenger Astrophysics; (ii) cyberinfrastructure requirements to numerically simulate astrophysical sources, and to process and interpret Multi-Messenger Astrophysics data; (iii) management of gravitational wave detections and triggers to enable electromagnetic and astro-particle follow-ups; (iv) a vision to harness future developments of machine and deep learning and cyberinfrastructure resources to cope with the scale of discovery in the Big Data Era; (v) and the need to build a community that brings domain experts together with data scientists on equal footing to maximize and accelerate discovery in the nascent field of Multi-Messenger Astrophysics.