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

新的天文调查既深入又广泛——来自像超级超级相机（HSC）和大型综合巡天望远镜（LSST）这样的革命性仪器——准备改变我们对星系演化的理解。以前的工作主要是在过去调查的表面亮度限制之上的明亮/大质量星系（例如SDSS）。然而，“低表面亮度”（LSB）星系，即那些在过去的调查中太暗而无法探测到的星系，实际上在星系数密度中占主导地位。此外，LSB的关键组成部分，如由星系合并引起的潮汐特征，强烈地限制了我们的结构-形成范式。因此，要完全理解宇宙是如何演化的，就需要详细了解LSB宇宙——没有它，我们对星系演化的理解可能是非常不完整的！该项目将结合HSC和LSST的最新数据，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，并研究黑洞反馈在LSB/矮星系中至少在宇宙生命的一半中所起的作用，从而为这一机制开辟广阔的发现空间。该学生将与巴黎、牛津的同事以及LSST项目的全球科学家网络密切合作（通过访问和会议旅行）（我们的团队在该项目中发挥领导作用）。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，这与大数据天文学的新时代非常吻合。

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.