Exploring the evolution of galaxies with the next generation of massive surveys

了解星系如何演化需要了解它们的形态和它们各种形态组成部分的恒星形成历史，以及它们之间的相互依存关系。我们正处于能够探索过去100多亿年星系演化的尖端，通过分析从未来几年将进行的大型天空调查中获得的前所未有的大星系样本。特别地，LSST和EUCLID调查的联合分析将为我们提供数百万个星系的样本，这些星系的特征是从观测到的U波段到近红外波段的全光度测量，以及适合确定星系在回顾时间和红移的大范围内的光学和近红外形态的高分辨率成像-从宇宙中恒星形成的高峰期到今天。为了探索这种演化，计算工具将被开发出来，以同时描述每个星系的形态（隆起和圆盘）以及每个组成部分的详细恒星形成历史，从而确定整个星系群演化的自洽模型——低红移星系群的历史必须与早期宇宙时间所见的星系群的特性相一致。这项工作将涉及从大数据和潜在的人工智能中提取的技术，以应对数量庞大的对象，每个对象都具有大量参数，并可能适合多种不同的进化模型

Understanding how galaxies evolve requires knowledge of their morphologies and the star formation histories of their various morphological components, and their interdependence. We are at the cusp of being able to explore galaxy evolution over the past 10+ billion years through the analysis of unprecedentedly-large samples of galaxies drawn from huge sky surveys that are to take place in the next few years. In particular, the joint analysis of the LSST and EUCLID surveys will provide us with samples of many millions of galaxies which are characterised by full photometry from observed U band though to the near IR and with high resolution imaging suitable to determine the optical and near-IR morphologies of the galaxies over a huge range in lookback time and redshift - from the epoch of peak star formation in the universe to today. In order to explore this evolution computational tools will be developed to simultaneously characterise each galaxy in terms of its morphology (bulge & disk) and detailed star formation history for each component) and thereby determine a self-consistent model of the evolution of the entire population - the history of a lower redshift population must agree with the properties of a population seen at an earlier cosmic time. The work will involve techniques drawn from Big Data and potentially AI in order to cope with the huge number of objects, each characterised by a large number of parameters and potentially fit with a multitude of different evolutionary models