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？关于宇宙的本质及其主要组成部分，如暗物质和暗能量，星系能教给我们什么？为了回答这些核心问题，我们需要将对恒星和气体的观测与宇宙学模型的预测进行比较。经验星系形成模型在星系和暗物质晕之间提供了独特和直接的联系，并且不依赖于重子物理的模型假设，只依赖于重力和观测到的星系属性。在艾美诺特项目的第一阶段，我们开发了最先进的经验星系形成模型Emerge，该模型通过跟踪从数值模拟中提取的光晕的增长历史来跟踪单个星系的演化，并使用经验关系来推导星系的属性，如恒星形成率和气体质量。这种方法的主要困难是在星系和晕属性之间找到合适的参数关系。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，并使用能够自动检测星系和光晕属性之间的联系等模式的机器学习方法。为此，我们将开发一个以暗物质晕的性质为输入值的深度神经网络，并计算位于晕中心的星系的性质。该网络将首先使用Emerge的结果以监督的方式进行训练。下一步，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数据，如恒星质量函数、宇宙恒星形成率密度和星团，并将它们与具有损失函数的观测数据进行比较。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。一旦网络被训练，我们将提取星系和晕属性之间的适当参数，并在Emerge中实现它们，以便可以自我一致地跟踪星系的演化。最后，我们将把训练好的神经网络提供给社区。通过这种方式，我们可以轻松地访问和使用我们艾美奖诺伊特小组的科学成果，提供遗产。