Note: This article by Maggie Knutte was first published by the College of Liberal Arts & Sciences at the University of Urbana-Champaign.
CAII is happy to share this article from our Center Affiliate Professor Xin Liu. Professor Xin Liu leads the research group Astro-AI referenced in this article.
About five years ago, graduate students at the University of Illinois embarked on a project for their ASTRO 596 class. That project has since evolved into a sophisticated artificial intelligence (AI) machine learning software that can identify objects from telescope images, such as stars and galaxies.
Two images of the same portion of space taken by the Hyper Suprime-Cam on the Subaru Telescope in Japan. On the left is the untouched image and on the right is the image displaying the objects detected by DeepDISC. Green boxes indicate that DeepDISC thinks the object is a galaxy, and red boxes indicate that DeepDISC thinks the object is a star.
(Image courtesy of Grant Merz and Xin Liu)
The project, known as Detection, Instance Segmentation, and Classification with Deep Learning, or DeepDISC, leverages machine learning—a branch of AI intertwined with computer science. Instead of relying on explicit coding, machine learning allows AI to learn from data and algorithms, much like humans do. The AI then writes its own code and continues to improve and adapt. More specifically, DeepDISC uses deep learning, a subfield of machine learning that uses neural networks akin to those in the human brain. These networks process data, analyze it, and generate outputs.
CAII Affiliate and Astronomy professor Xin Liu leads the research group Astro-AI, which is at the helm of DeepDISC’s development. Grant Merz, a graduate student, plays a pivotal role in the project as part of his thesis work, contributing to both the development of the codebase and the scientific analysis.
The group collaborates with the Legacy Survey of Space and Time (LSST) Discovery Alliance, a joint initiative between the National Science Foundation and the U.S. Department of Energy, aimed at maximizing the scientific and societal impact of Rubin Observatory’s LSST. Their efforts include integrating DeepDISC with the software used by LSST.
DeepDISC receives funding from various agencies, including the LINCC Frameworks Incubator program, supported by Schmidt Sciences through the LSST Discovery Alliance. Additional support comes from NSF, NASA, a National Center for Supercomputing Applications faculty fellowship, and NCSA’s Students Pushing INnovation internship programs.
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