ABSTRACT
Galaxies are complex systems influenced by numerous factors. Despite extensive research, we cannot yet explain the diverse properties of nearby galaxies. Gas accretion, environments, galaxy merging, and supermassive black hole (SMBH) feedback play pivotal roles in galaxy evolution. In this talk, I will present an overview of my recent and current research. My work entails a comprehensive comparative analysis of fundamental properties between observed and simulated galaxies, encompassing stellar mass, gas contents, star formation rate, multiscale environments, and SMBH properties. Highlighting a significant finding, I will demonstrate a major inconsistencies between the properties of approximately 40,000 nearby active galactic nuclei (AGNs: Seyfert and quasars) identified in the Sloan Digital Sky Survey and simulated AGNs in three cutting-edge cosmological simulations. While both simulations and observations qualitatively suggest a prevalence of strongly accreting SMBHs in gas-rich, star-forming host galaxies in low-density environments, substantial quantitative discrepancies exist in the properties of these host galaxies. Furthermore, the simulations fail to accurately reproduce the star formation rates or quenched fractions of galaxies hosting inactive SMBHs across different environments, casting doubts on the efficacy of cumulative SMBH feedback in the simulations. I ague that the halo-scale environment, or related processes, significantly influence the availability of cold gas, thereby impacting both short-term activity and long-term quiescence in galaxies. A combination of recent gas accretion (both diffuse and merger-related) in low-density environments, leading to episodic starbursts and black hole activity, alongside ancient bulge buildup, offers a plausible explanation observed properties in nearby galaxies.
BIO
He is an associate professor at Shanghai Astronomical Observatory, CAS. He was a Kavli IPMU-KIAA postdoctoral fellow from 2018-2023. He earned his PhD in Astrophysics and Statistics from University of California Santa Cruz in 2016 and worked in UC Santa Cruz until 2018. He received his Bachelor degree in Astrophysical Sciences from Princeton University in 2010.
His research interests are galaxy formation & evolution (star formation, AGN feedback, gas in galaxies, post-starburst galaxies, and etc), and applications of Statistics and Machine Learning to astrophysical data. He integrates these methods into his research to simplify the complexities of galaxy evolution, offering insights into how these techniques enhance our understanding of galaxies.
