Deep Space: Deep Learning in Astronomy

Early developments in Statistics were often driven by astronomical data and analyses. It is well known that the motivation for the development of regression analysis by Gauss was the problem of locating the position of minor planet Ceres after emergence from Sun block. In recent times, Astrostatistics has driven new techniques in MCMC and Bayesian methodology. Astronomical data has stead ily increased in both quantity and quality, with space telescopes like SDO, Hubble, Chandra, XMM-Newton, Gaia, TESS, and JWST producing copious data with unprecedented resolutions, and massive ground-based surveys like Rubin LSST and the Square Kilometer Array poised to cause a revolution in both astronomy and data science. Recently, astronomy has also been at the forefront of applications of Machine Learning methods. This session emphasizes the interface between Machine Learning, Statistics, and Astronomy, emphasizing the advances in statistical machine learning driven by astronomical data and analyses. In keeping with the theme of the JSM, this session will focus on the community of AI-aware astronomers bringing the ir unique perspectives to mesh together astronomy and statistical machine learning. This is particularly relevant in the Boston area, which has one of the largest astrostatistical and astroinformatics communities in the world, and has been buzzing with activity in this field. Greater Boston has several institutions that do space weather and astronomy, and concomitantly work in the overlap between these fields in both astrostatistics and AI (notably the CHASC Astrostatistics collaboration and the AstroAI initiative). We will bring in three researchers working at the nexus of statistical machine learning and astronomy to present talks at the session. Additionally, we will have two discussants synthesizing the current state of the art, one from an astronomer's perspective, and one from a statistician's perspective. This session is congruous to the aims of ASA's Astrostatistics Interest Group (AIG), which seeks to highlight and advance astrostatistical learning and collaborations.