Discrepancies in Cosmological Simulations: Uncovering the Black Hole, Star Formation, and Dark Matter Connection in Nearby Galaxies
中国科学院云南天文台学术报告通知
2024年第30期
报告题目：Discrepancies in Cosmological Simulations: Uncovering the Black Hole, Star Formation, and Dark Matter Connection in Nearby Galaxies
报告人：Dr. Hassen Mohammed Yesuf,?Associate researcher
报告人单位：Shanghai Astronomical Observatory, CAS
报告语言：English
报告时间：2024年12月23日10:30-11:30
报告地点：1号楼4楼412会议室
报告摘要：
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Environmental factors and supermassive black hole (SMBH) feedback are critical components of galaxy evolution. I will provide an overview of our comprehensive comparative analysis of observed and simulated galaxies. This analysis examines stellar mass, star formation rate, halo mass, environments, and SMBH properties using multiwavelength data from nearby galaxies in the SDSS and GAMA surveys. I will highlight new measurements and findings, emphasizing major inconsistencies between the properties of approximately 40,000 nearby active galactic nuclei (AGNs: Seyferts and quasars), identified in Sloan data, and those of simulated AGNs in the TNG, EAGLE, and SIMBA cosmological simulations. While both simulations and observations qualitatively indicate a prevalence of strongly accreting SMBHs in gas-rich, star-forming host galaxies within low-density environments, substantial quantitative discrepancies exist. These include differences in the distribution of stellar mass, star formation rates in host galaxies, as well as black hole mass and accretion luminosity functions. Moreover, the simulations fail to accurately reproduce the star formation rates or quenched fractions of galaxies with inactive galaxies across various environments. All simulations overpredict the quenched fraction by more than 30% in low-mass galaxies within high density environments, while they offer differing predictions for galaxies with high stellar mass.
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报告人简介：?
Dr.Hassen Mohammed Yesuf is an associate researcher at Shanghai Astronomical Observatory, CAS. He received his Bachelor degree in Astrophysical Sciences from Princeton University in 2010. 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 was a Kavli IPMU-KIAA postdoctoral fellow from 2018-2023.
His research interests are galaxy formation & evolution (star formation, AGN feedback, gas in 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.
云台邀请团组：大样本恒星演化研究团组
邀请方联系人：韩云坤
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