Detecting and studying light echoes in the era of Rubin and Artificial Intelligence

鲁宾和人工智能时代检测和研究光回波

• 批准号: 2108841
• 负责人: Federica Bianco
• 金额: $ 59.61万
• 依托单位: University of Delaware
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2108841&HistoricalAwards=false
• 关键词: Detecting; studying; light; echoes; era

This award supports building an artificial intelligence (AI)-based pipeline for the all-sky-scale automated detection and study of light echoes. Light echoes (LEs) are caused by stellar explosions lighting up cosmic dust, and they are faint, rare, diffuse, and hard to detect. The detection of all sky samples of LEs can enable the discovery of previously unknown Galactic supernovae, and permit the study of Galactic dust and the history of stellar explosions and eruptions in the Galaxy. These new AI methods will robustly re-discover old, and discover new, light echoes in both existing and future survey datasets. The project will develop essential data science skills in students at the University of Delaware and at Delaware State University, a minority- and rural population-serving HBCU. AI model architectures for efficient and reliable detection of low-signal-to-noise diffuse features can be used throughout astronomy, for medical imaging, and in ecology and urban metabolism studies.Presently, LEs are detected by visual inspection, which is limiting and does not scale to all-sky surveys. The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will observe the southern sky at frequent intervals, making it an ideal LE survey. Unfortunately, the LSST alert pipeline is optimized for point sources and will entirely miss LEs. This project will produce the first pipeline for the automated all-sky detection and study of LEs by leveraging cutting-edge AI, and support deployment of the pipeline on the LSST science platform. The team will also be providing hands-on data- and computer-science training to students from groups historically underrepresented in the STEM fields, using an immersive learning program that includes Data Science boot camps, hackathons, and mentored research opportunities. This project capitalizes on the two NSF Big Ideas of “Harnessing the Data Revolution” and “Growing Convergence Research”.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.

该奖项支持建立一个基于人工智能（AI）的管道，用于全天空范围的自动检测和光回波研究。 光回波（LE）是由恒星爆炸照亮宇宙尘埃引起的，它们微弱，罕见，弥漫，难以检测。 对所有LEs天空样本的探测可以发现以前未知的银河系超新星，并允许研究银河系尘埃和银河系恒星爆炸和爆发的历史。 这些新的人工智能方法将在现有和未来的调查数据集中重新发现旧的和新的光回波。 该项目将培养特拉华州大学和特拉华州州立大学学生的基本数据科学技能，这是一所为少数民族和农村人口服务的HBCU。 人工智能模型架构可用于高效可靠地检测低信噪比漫射特征，可用于整个天文学、医学成像以及生态学和城市代谢研究。目前，LEs通过目视检查进行检测，这具有局限性，无法扩展到全天调查。 维拉C。鲁宾天文台的时空遗产巡天（LSST）将频繁地观测南部天空，使其成为理想的LE巡天。 不幸的是，LSST警报管道针对点源进行了优化，并且将完全错过LE。 该项目将通过利用尖端的人工智能，为LEs的自动化全天检测和研究提供第一个管道，并支持在LSST科学平台上部署管道。 该团队还将为来自STEM领域历史上代表性不足的群体的学生提供实践数据和计算机科学培训，使用沉浸式学习计划，包括数据科学靴子营地，黑客马拉松和指导研究机会。 该项目充分利用了“驾驭数据革命”和“不断增长的融合研究”这两个NSF大理念。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Toward the Automated Detection of Light Echoes in Synoptic Surveys: Considerations on the Application of Deep Convolutional Neural Networks

• DOI: 10.3847/1538-3881/ac9409
• 发表时间: 2022-08
• 期刊: The Astronomical Journal
• 作者: Xiaolong Li;F. Bianco;G. Dobler;Roee Partoush;A. Rest;Tatiana Acero-Cuellar;Riley Clarke;W. Fortino;S. Khakpash;Ming Lian