Optimizing Target Selection for Low-Redshift Galaxy Surveys - Elise Darragh Ford

Explore optimized target selection strategies for low-redshift galaxy surveys in this 28-minute conference talk by Elise Darragh Ford from Stanford University. Delve into the application of astrostatistics and machine learning tools in galaxy formation and evolution research. Discover how Integral Field Unit surveys are revolutionizing data collection, providing hundreds of spectra per galaxy across tens of thousands of galaxies. Learn about the wealth of information contained in galaxy morphology through imaging data, and how statistical and machine learning-powered outlier detection algorithms are uncovering anomalous galaxies that challenge current paradigms. Gain insights into the role of data science tools in linking observations with theoretical models, including cosmological hydrodynamical simulations and dark matter-only simulations. Understand the importance of translating data-driven results into physical understanding for advancing the field of galaxy formation.