Finding Simple Structures in Complex Datasets - Dalya Baron

Explore the application of astrostatistics and machine learning tools to galaxy formation and evolution in this 29-minute conference talk by Dalya Baron from Tel Aviv University. Discover how data-driven approaches can uncover simple structures within complex astronomical datasets. Learn about the vast potential of Integral Field Unit surveys, which produce hundreds of spectra per galaxy across thousands of galaxies, and the wealth of information contained in galaxy morphology imaging data. Understand how statistical and machine learning-powered outlier detection algorithms are identifying anomalous galaxies that challenge current paradigms, and how these discoveries will accelerate with upcoming projects like Rubin, DESI, Roman, Euclid, and the SKA. Gain insights into the crucial role of data science tools in linking observations with theoretical models, including cosmological hydrodynamical simulations and dark matter-only simulations with semi-analytic or empirical models. This talk is part of a conference aimed at maximizing the benefits of astrostatistics, data science, and machine learning for the galaxy formation field, emphasizing the translation of data-driven results to physical understanding.