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

University Of California-Santa Cruz, Santa Cruz CA

Investigators

Abstract

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.