Revealing the Physical Drivers of Morphological Evolution with AI/Machine Learning and Rubin Observatory

未来十年天文调查的标志将是数据量和复杂性的大幅增加。未来几年的天文发现将依赖于社区快速处理、分析和理解大量信息的能力。在这个项目中，加州大学圣克鲁兹分校的科学家们将应用一种名为Morpheus的人工智能/机器学习（AI/ML）模型来分析和分类大规模公共天文成像调查中的天文物体。通过将AI/ML方法应用于天文数据，有可能实现在计算上难以处理的分析。通过发布Morpheus作为开放框架，其他科学家可以将其应用于自己的数据集，这项研究将使概率形态学成为广泛的河外成像调查的可行测量，从而大大增加对星系形成的了解。该项目的目标包括：1)进一步了解控制星系形成的形态学和物理学之间的联系；2)降低将强大的AI/ ML方法应用于天文数据集的标准。作为该项目的一部分，该团队还将为研究生和博士后建立一个每年一次的免费研讨会，以开发高质量、可转移和可扩展的专业网站。这些活动将提高天文学和天体物理学领域年轻研究人员的知名度，同时为他们提供一个持久的在线足迹，以展示他们的专业活动。本研究将应用Morpheus深度学习框架进行天文数据分析，对大规模公共调查数据进行语义分割、源提取和星系形态分类。Morpheus框架利用AI/ML技术对图像进行逐像素分类，检测物体，生成相应的分割图，然后量化每个像素属于一类天文物体的模型概率。该团队将把Morpheus整合到鲁宾科学平台中，结合鲁宾和天基数据对其进行验证，并将其应用于最初的LSST数据发布。研究小组还将利用得到的Morpheus形态来研究形态与其他星系特性之间的相关性。该项目还支持加州大学圣克鲁斯分校鲁宾天文台的科学验证活动。该奖项反映了美国国家科学基金会的法定使命，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。

The hallmark of astronomical surveys over the next decade will be their vastly increased data volumes and complexity. Astronomical discoveries in coming years will rely on the ability of the community to rapidly process, analyze, and understand enormous amounts of information. In this project, scientists at the University of California, Santa Cruz, will apply an Artificial Intelligence/Machine Learning (AI/ML) model called Morpheus to analyze and classify astronomical objects in large-scale, public astronomical imaging surveys. Through the application of AI/ML methods to astronomical data it is possible to enable analyses that are otherwise computationally intractable. By releasing Morpheus as an open framework for other scientists to apply on their own datasets, this research will substantially augment the knowledge of galaxy formation by making probabilistic morphology a feasible measurement for a wide range of extragalactic imaging surveys. Goals of the project include 1) furthering the understanding of the connection between morphology and the physics that govern galaxy formation and 2) lowering the bar for the application of powerful AI//ML methodologies to astronomical datasets. As part of this project, the team will also establish a yearly free workshop for graduate students and postdocs to develop high-quality, transferrable, and extendable professional websites. These activities will increase the visibility of young researchers in astronomy and astrophysics, while providing them with a durable on-line footprint for featuring their professional activities.The proposed research will apply the Morpheus deep learning framework for astronomical data analysis to perform semantic segmentation, source extraction, and morphological classification of galaxies in large scale public survey data. The Morpheus framework leverages AI/ML technology to provide pixel-by-pixel classifications of images, detecting objects, producing corresponding segmentation maps, and then quantifying the model probability that each pixel belongs a class of astronomical object. The team will incorporate Morpheus into the Rubin Science Platform, validate it with a combination of Rubin and space-based data, and apply it to the initial LSST data releases. The team will also use the resulting Morpheus morphologies to investigate the correlations between morphology and other galaxy properties. This project also supports Rubin Observatory science verification activities at UC Santa Cruz.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.