报告摘要：The evolution of massive galaxies is important for understanding key physical processes such as AGN feedback in galaxy formation and evolution. In a cosmological context, statistical properties of this population such as their abundance could also provide an interesting diagnostic test. There is mounting evidence that our knowledge of the galaxy stellar mass function at the high mass end is incomplete, and may even miss the dominant population of the very massive galaxies. To achieve an unbiased census of massive galaxies particularly in the early Universe, it is imperative to account for the dust-obscured star-formation galaxies, which are most easily observed in the far-infrared and sub-millimetre. I will present our efforts and results in tracing this elusive population, their physical properties and triggering mechanisms, by employing a tripod approach of state-of-the-art galaxy surveys, cosmological simulations and artificial intelligence techniques. I will conclude by summarising our main conclusions so far, future outlook and also challenges therein.
主讲人简介：I obtained B.S. in Physics from Zhejiang University in 2005 and Ph.D. in astrophysics from Imperial Collage London in the UK in 2009. I held post-doctoral research fellowships at the University of Sussex and University of Durham in the UK from 2009 to 2015. In 2015, I was appointed as scientist (tenure track and later tenured) at SRON Netherlands Institute for Space Research and assistant professor at the University of Groningen in the Netherlands. I was promoted to senior scientist in 2021 and associate professor in 2024. My research focuses on statistical galaxy evolution studies in diverse environment across cosmic time and how galaxy evolution connects to the underlying cosmological framework. For more than a decade, I have worked extensively on large multi-wavelength photometric and spectroscopic extragalactic galaxy surveys from the local to the early Universe (such as Herschel, GAMA, LOFAR and Euclid), consistent comparisons with state-of-the-art theoretical models and simulations of galaxy formation and evolution, and applications of machine learning (in particular deep learning) methods. In particular, I focus on investigating the two main physical processes driving galaxy evolution, i.e., star formation and accretion onto supermassive black holes and feedback, using multiple observational probes and test galaxy populations (from field galaxies to galaxy clusters). Over the past decade, I have won various individual and team research grants for my work. I have also served on various international panels and committees, in particular I served as a Topical Team member for the European Space Agency (ESA) Voyage 2050 Programme which sets out priorities in the ESA science mission themes for the timeframe 2035 - 2050.
