From dark to light: The connection between galaxies and their dark matter haloes through cosmic time

从黑暗到光明：宇宙时间中星系与其暗物质晕之间的联系

• 批准号: 274846616
• 负责人: Dr. Benjamin Moster
• 金额: --
• 依托单位: Lehrstuhl für Theoretische und Numerische Astrophysik
• 依托单位国家: 德国
• 项目类别: Independent Junior Research Groups
• 财政年份: 2015
• 资助国家: 德国
• 起止时间: 2014-12-31 至 2021-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/274846616?language=en
• 关键词: dark; light; connection; between; galaxies

The formation and evolution of galaxies ranks among the most exciting and promising areas of physical sciences. How do galaxies form and what determines their properties? What can galaxies teach us about the nature of the Universe and its main components such as dark matter and dark energy? To answer these central questions we need to compare observations of stars and gas with the prediction of cosmological models. Empirical galaxy formation models provide a unique and direct link between galaxies and dark matter haloes, and do not depend on model assumptions on baryonic physics, only on gravity and observed galaxy properties. During the first phase of the Emmy Noether project we have developed the state-of-the-art empirical galaxy formation model EMERGE, which follows the evolution of individual galaxies by tracing the growth histories of their haloes as extracted from numerical simulations, and uses empirical relations to derive galaxy properties such as star formation rate and gas mass. The main difficulty in this approach is finding suitable parameterised relations between galaxy and halo properties. In this project, we will utilise the enormous advances that the field of artificial intelligence has made over the last years, and employ machine learning methods that are able to detect patterns like the connection between galaxy and halo properties automatically. To this end, we will develop a deep neural network that takes the properties of a dark matter halo as input values, and computes the properties of the galaxy in the centre of the halo. The network will first be trained in a supervised manner using the results of EMERGE. In the next step, we will train the neural network directly on observed data, using a reinforcement learning approach. For a set of network weights, we will compute mock observations like stellar mass functions, cosmic star formation rate densities and clustering, and compare them to the observed data with a loss function. We will then use particle swarm optimisation and evolutionary algorithms to minimise the loss function, and choose the weights such that the observed data are reproduced. Once the network has been trained, we will extract suitable parameterisations between galaxy and halo properties and implement them in EMERGE so that the evolution of galaxies can be followed self-consistently. Finally, we will make the trained neural networks available to the community. In this way, the scientific achievements of our Emmy Noether group can be easily accessed and used, providing a legacy.

星系的形成和演化是物理科学中最令人兴奋和最有前途的领域之一。星系是如何形成的，是什么决定了它们的性质？星系能告诉我们什么关于宇宙的性质和它的主要组成部分，如暗物质和暗能量？为了回答这些核心问题，我们需要将恒星和气体的观测结果与宇宙学模型的预测进行比较。经验星系形成模型提供了星系和暗物质晕之间独特而直接的联系，并且不依赖于重子物理学的模型假设，只依赖于引力和观测到的星系性质。在第一阶段的埃米诺特项目，我们已经开发出国家的最先进的经验星系形成模型EMERGE，它遵循个别星系的演化，通过跟踪从数值模拟中提取的晕的生长历史，并使用经验关系，以获得星系的属性，如星星的形成率和气体质量。这种方法的主要困难是找到合适的参数化星系和晕属性之间的关系。在这个项目中，我们将利用人工智能领域在过去几年中取得的巨大进步，并采用机器学习方法，能够自动检测星系和光环属性之间的联系等模式。为此，我们将开发一个深度神经网络，将暗物质晕的属性作为输入值，并计算晕中心星系的属性。该网络将首先使用EMERGE的结果以监督的方式进行训练。在下一步中，我们将使用强化学习方法直接在观测数据上训练神经网络。对于一组网络权重，我们将计算模拟观测，如恒星质量函数，宇宙星星形成率密度和聚类，并将它们与观测数据进行比较。然后，我们将使用粒子群优化和进化算法来最小化损失函数，并选择权重，以便重现观测数据。一旦网络经过训练，我们将在星系和晕属性之间提取合适的参数，并在EMERGE中实现它们，以便能够自洽地跟踪星系的演化。最后，我们将向社区提供经过训练的神经网络。通过这种方式，我们的埃米诺特集团的科学成就可以很容易地访问和使用，提供了一个遗产。