Prospects for Detecting Gaps in GC Streams with Roman - Christian Aganze

Explore the potential for detecting gaps in globular cluster streams using the Roman Space Telescope in this 28-minute conference talk by Christian Aganze from UCSD. Delve into the application of astrostatistics and machine learning tools in galaxy formation and evolution research. Discover how current and future Integral Field Unit surveys are revolutionizing our understanding of galaxies by producing hundreds of spectra per galaxy across tens of thousands of galaxies. Learn about the wealth of information contained in galaxy morphology imaging data, down to the pixel level and across various wavelengths. Understand how statistical and machine learning-powered outlier detection algorithms are uncovering anomalous galaxies that challenge our current paradigms, and how these discoveries are expected to accelerate with upcoming projects like Rubin, DESI, Roman, Euclid, and the SKA. Gain insights into the crucial role of data science tools in bridging observations with theoretical models, including cosmological hydrodynamical simulations and dark matter-only simulations with semi-analytic or empirical models.