The low-surface-brightness Universe: a new frontier in the study of galaxy evolution

新的天文观测既深入又广泛-从革命性的仪器，如超级超级凸轮（HSC）和大型综合巡天望远镜（LSST）-准备改变我们对星系演化的理解。以前的工作主要是明亮/大质量的星系，这些星系位于过去调查的表面亮度极限之上（例如SDSS）。然而，“低表面亮度”（LSB）星系，即那些在过去的调查中太微弱而无法检测到的星系，实际上主导了星系的数量密度。此外，关键LSB组件，如星系合并引起的潮汐功能，强烈约束我们的结构形成范式。因此，要完全理解宇宙是如何演化的，就需要对LSB宇宙有详细的了解--没有它，我们对星系演化的理解可能是非常不完整的！该项目将联合收割机结合HSC和LSST的最先进数据，以及内部宇宙学模拟（Horizon-AGN）和先进的机器学习技术（Martin et al. 2019），对LSB宇宙进行首次统计研究。它将通过以前所未有的细节绘制LSB星系的特性，量化星系合并在推动恒星形成，黑洞生长和宇宙时间形态转变中的作用，并研究黑洞反馈的作用，在宇宙的至少一半寿命中，LSB/矮星系中的黑洞反馈的作用。学生将密切合作（通过访问和会议旅行）与同事在巴黎，牛津大学和科学家的全球网络在LSST项目（其中我们的团队有领导作用）。该项目将为学生提供天文观测，理论和机器学习方面的广泛技能，与大数据天文学的新时代保持一致。

New astronomical surveys that are both deep and wide - from revolutionary instruments like the Hyper Suprime-Cam (HSC) and the Large Synoptic Survey Telescope (LSST) - are poised to transform our understanding of galaxy evolution. Previous work has been dominated by bright/massive galaxies that lie above the surface-brightness limit of past surveys (e.g. the SDSS). However, 'low-surface-brightness' (LSB) galaxies, i.e. ones that are too faint to be undetectable in past surveys, actually dominate the galaxy number density. Furthermore, key LSB components, such as tidal features induced by galaxy mergers, strongly constrain our structure-formation paradigm. A complete comprehension of how the Universe evolves therefore demands a detailed understanding of the LSB Universe - without it our understanding of galaxy evolution is likely to be highly incomplete! This project will combine state-of-the-art data from HSC and LSST, with in-house cosmological simulations (Horizon-AGN) and advanced machine-learning techniques (Martin et al. 2019), to perform the first statistical studies of the LSB Universe. It will open up the vast discovery space in this regime by mapping the properties of LSB galaxies in unprecedented detail, quantify the role of galaxy mergers in driving star-formation, black-hole growth and morphological transformation over cosmic time and study the poorly-understood role of black-hole feedback in LSB/dwarf galaxies over at least half the lifetime of the Universe. The student will collaborate closely (through visits and conference trips) with colleagues in Paris, Oxford and a worldwide network of scientists within the LSST project (in which our team has a leadership role). The project will give the student a broad skillset in astronomical observation, theory and machine-learning that is well-aligned with this new era of Big Data astronomy.