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: 2307374
• 负责人: Niharika Sravan
• 金额: $ 34.12万
• 依托单位: Drexel University
• 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=2307374&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发出的引力波可以通过高级探测器在地球上检测到的引力波，从而感觉到宇宙织物中的微小干扰。在某些情况下，此类事件伴随着电磁辐射的爆发，望远镜也可以看到。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。但是，这种排放的检测通常是挑战性的，因为它通常简短而微弱。该项目旨在开发尖端的人工智能（AI）系统，以优化数百种宇宙爆炸中的来源的搜索，这些宇宙爆炸点燃了夜空。这样的系统将代表一些在天文学中做出实时科学决策的第一个系统，这决定了有限望远镜资源来简化发现过程的最有效利用。这使天文学家可以专注于对结果的科学解释。该项目还将培训学生在其他领域（例如机器人技术和金融）中设计类似系统所需的高级技术。研究人员将开发一种自动化Kilonova Discovery基础架构的后续决策步骤的系统。具体而言，鉴于引力波合并和伽马射线的警报以调查光曲线和图像邮票的形式爆发，以及任何增值信息，例如Galaxy Redshift，该系统将在有限的地平线内引导一系列资源分配，以最大化指定目标。这种新颖的方法涉及一种AI代理，它可以自适应地学习以做出最佳的决策顺序，并且有关未来调查以及来自其他来源的补充随访数据的不完整信息和随机性。它将使用强化学习的框架来排练引力波触发场景，并了解采取某种动作的影响下游所带来的福利，以解决最佳的决策集，这些决策在情况下最大程度地提高了福利。 AI代理将接受培训，以处理光度和光谱的后续措施，并同时最大化Kilonova的发现和推理目标。该项目解决/促进宇宙窗户的目标。该奖项反映了NSF的法定任务，并通过使用基础优点进行评估，以评估智力和宽阔的影响。