An AI-Assisted Labeling Tool for Cataloging High-Resolution Images of Galaxies

Gustavo Perez, Sean Linden, Timothy McQuaid, Matteo Messa, Daniela Calzetti, Subhransu Maji

Keywords: astronomy image, labeling tool, self-supervised learning, star cluster classification

TL;DR: We propose an ecosystem of AI tools for cataloging star clusters that consist of a web-based annotation tool for high-resolution astronomy data and techniques to reduce the annotation cost by using unsupervised representation learning

Abstract: The Hubble Space Telescope (HST), the recently launched James Web Space Telescope (JWST), and many earth-based observatories collect data allowing astronomers to answer fundamental questions about the Universe. In this work we focus on an ecosystem of AI tools for cataloging bright sources within galaxies, and use them to analyze young star clusters -- groups of stars held together by their gravitational fields. Their ages and masses, among other properties provide insights into the process of star formation and the birth and evolution of galaxies. Significant domain expertise and resources are required to discriminate star clusters among tens of thousands of sources that may be extracted for each galaxy. To accelerate this step we propose: 1) a web-based annotation tool to label and visualize high-resolution astronomy data, encouraging efficient labeling and consensus building; and 2) techniques to reduce the annotation cost by leveraging recent advances in unsupervised representation learning on images. We present case studies where we work with astronomy researchers to validate the annotation tool and find that the proposed tools can reduce the annotation effort by 3$\times$ on existing HST catalogs, while facilitating accelerated analysis of new data.