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

要了解星系是如何演化的，需要了解它们的形态以及它们各种形态成分的恒星形成历史，以及它们之间的相互依存关系。我们即将能够通过分析史无前例的大量星系样本来探索过去100多亿年的星系演化，这些样本来自于未来几年将进行的大规模巡天。特别是，对LSST和欧几里德观测的联合分析将为我们提供数百万个星系的样本，这些星系的特点是从观测到的U波段到近红外的全光度测量，以及适合于确定星系在大范围内的光学和近红外形态的高分辨率成像，这些星系的回顾时间和红移-从宇宙中恒星形成的顶峰时期到今天。为了探索这一演化，将开发计算工具，以同时根据每个星系的形态(凸起和圆盘)和每个组件的详细恒星形成历史来描述每个星系，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较早的宇宙时间看到的星系群的性质一致。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，以应对数量巨大的对象，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