Galaxy Merger Reconstruction with Generative Graph Neural Networks - Yuan Sen Ting

Explore the cutting-edge application of generative graph neural networks in reconstructing galaxy mergers in this 29-minute conference talk by Yuan Sen Ting from the Australian National University. Delve into the intersection of astrostatistics, machine learning, and galaxy formation physics as part of the Kavli Institute for Theoretical Physics' conference on data-driven tools in galaxy evolution studies. Gain insights into how advanced computational methods are revolutionizing our understanding of galaxy formation and evolution, particularly in the context of large-scale surveys and multi-wavelength data. Discover how these innovative techniques can bridge the gap between observational data and theoretical models, potentially uncovering new paradigms in astrophysics. Learn about the broader implications of this research for upcoming astronomical missions and the future of galactic studies in the era of big data.