Collaborative Research: WoU-MMA: Optimal Follow-up for Multimessenger Astronomy

合作研究：WoU-MMA：多信使天文学的最佳后续研究

• 批准号: 2307373
• 负责人: Matthew Graham
• 金额: $ 16.05万
• 依托单位: California Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2307373&HistoricalAwards=false
• 关键词: Collaborative; Research; WoU; MMA; Optimal

Mergers of black holes and neutron stars emit gravitational waves that can be detected on Earth by advanced detectors that sense minuscule disturbances in the fabric of the universe. In some cases, such events are accompanied by a burst of electromagnetic radiation that can also be seen by telescopes. These light signals combined with gravitational waves provide us with unprecedented insights into some of the most extreme objects in the Universe. However, the detection of such emission is challenging as it usually brief and faint. This project aims to develop cutting-edge artificial intelligence (AI) systems that will optimize the search for such sources among the hundreds of cosmic explosions that light up the night sky. Such systems will represent some of the first that make real-time scientific decisions in astronomy, determining the most efficient use of limited telescope resources to streamline the discovery process. This allows astronomers to focus on the scientific interpretation of results. The project will also train students in the advanced techniques required to design similar systems in other domains, such as robotics and finance.The investigators will develop a system that automates the follow-up decision-making step of the kilonova discovery infrastructure. Specifically, given an alert of gravitational wave mergers and gamma-ray bursts in the form of survey light curves and image stamps, and any value-added information, like galaxy redshift, the system will direct a series of resource assignments within a finite horizon that maximize a designated objective. The novel approach involves an AI agent that adaptively learns to make the best sequence of decisions given incomplete information and stochasticity concerning future survey and supplemental follow-up data from other sources. It will use the framework of reinforcement learning to rehearse gravitational wave trigger scenarios and learn how taking a certain action influences benefits achieved downstream, to solve for the optimal set of decisions that maximizes benefits given an unseen scenario. The AI agent will be trained to handle both photometric and spectroscopic follow-up and simultaneously maximize both kilonova discovery and inference objectives.This project addresses/advances the goals of the Windows on the Universe Big Idea.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

黑洞和中子星的合并会发出引力波，地球上的先进探测器可以探测到这些引力波，这些探测器可以感知宇宙结构中的微小扰动。在某些情况下，此类事件伴随着电磁辐射爆发，望远镜也可以看到这种辐射。这些光信号与引力波相结合，为我们提供了对宇宙中一些最极端物体的前所未有的见解。然而，这种发射的检测具有挑战性，因为它通常短暂且微弱。该项目旨在开发尖端的人工智能（AI）系统，以优化在照亮夜空的数百次宇宙爆炸中寻找此类来源的过程。此类系统将成为天文学领域首批做出实时科学决策的系统，确定最有效地利用有限的望远镜资源来简化发现过程。这使得天文学家能够专注于结果的科学解释。该项目还将培训学生在机器人和金融等其他领域设计类似系统所需的先进技术。研究人员将开发一个系统，自动执行千新星发现基础设施的后续决策步骤。具体来说，以巡天光变曲线和图像标记的形式给出引力波合并和伽马射线爆发的警报，以及任何增值信息，如星系红移，系统将在有限的范围内指导一系列资源分配，最大化指定目标。这种新颖的方法涉及一个人工智能代理，该代理在考虑到未来调查和来自其他来源的补充后续数据的不完整信息和随机性的情况下，自适应地学习做出最佳决策序列。它将使用强化学习的框架来演练引力波触发场景，并了解采取某种行动如何影响下游实现的效益，以解决在未见过的场景下最大化效益的最佳决策集。人工智能代理将接受培训，以处理光度和光谱后续工作，并同时最大限度地提高千新星发现和推理目标。该项目解决/推进了“宇宙之窗”大创意的目标。该奖项反映了 NSF 的法定使命，并已通过使用基金会的智力优点和更广泛的影响审查标准进行评估，认为值得支持。