Computational Cosmology and Artificial Intelligence

计算宇宙学和人工智能

• 批准号: CRC-2021-00334
• 负责人: PerreaultLevasseur, Laurence
• 金额: $ 6.92万
• 依托单位: Université de Montréal
• 依托单位国家: 加拿大
• 项目类别: Canada Research Chairs
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=762335
• 关键词: Computational; Cosmology; Artificial; Intelligence

Over the past few decades, the standard model of cosmology has had tremendous success at explaining a vast array of observations spanning an immense range of scales both in space and in time. However, the origin and nature of all 3 key components of this model remain, to this day, unknown. The physical nature of the field(s) responsible for the period of primordial inflation in the early Universe, the source of the apparent accelerated expansion of the Universe (dark energy), and the particle(s) that make up dark matter are the biggest outstanding mysteries of modern cosmology. Their understanding constitutes the main goal of modern cosmology, and will most likely cause a revolution in fundamental physics. In the next decade, a large number of new observatories and experiments will attempt to shed light on these mind-boggling questions. However, while the data cosmologists expect will be of unprecedented quality, the volumes of these new data will pose serious challenges when it comes to their analysis through traditional statistical methods. Artificial intelligence and machine learning will offer alternative analysis methods that promise great increase not only in speed, but also, in some cases, in accuracy.The goal of the proposed Tier 2 Canada Research Chair program is to lead cutting-edge observational and theoretical research on the use of machine learning for the simulation and analysis of cosmological data with the aim of enabling rapid, precise, and accurate analysis of upcoming observations from these large sky surveys, paving new ways for major breakthrough discoveries in the field of astrophysics.More specifically, the research program will focus on two central questions:1. Measuring the Hubble constant with strong gravitational lensing and machine learning, with the hope of solving an emerging crisis in cosmology and potentially uncovering new physics.2. Reconstructing the initial conditions of the Universe with survey data and artificial intelligence, creating a map of the initial 3-dimensional Universe only 400 000 years after its birth. This will open new avenues for answering questions about the nature of inflation and probing fundamental properties of dark energy.

在过去的几十年里，宇宙学的标准模型在解释跨越空间和时间尺度的大量观测方面取得了巨大的成功。然而，这一模式的所有3个关键组成部分的起源和性质至今仍然未知。导致早期宇宙原始膨胀时期的场的物理性质，宇宙明显加速膨胀的来源（暗能量），以及构成暗物质的粒子是现代宇宙学最大的未解之谜。对它们的理解构成了现代宇宙学的主要目标，并很可能引发基础物理学的革命。在接下来的十年里，大量新的观测站和实验将试图阐明这些令人难以置信的问题。然而，尽管宇宙学家预计这些数据将具有前所未有的质量，但这些新数据的数量将对通过传统统计方法进行分析构成严重挑战。人工智能和机器学习将提供替代分析方法，不仅可以大大提高速度，而且在某些情况下还可以提高准确性。拟议的Tier 2 Canada Research Chair计划的目标是领导关于使用机器学习模拟和分析宇宙学数据的前沿观测和理论研究，旨在实现快速，精确，并对这些大型巡天观测结果进行精确分析，为天体物理学领域的重大突破性发现铺平新的道路。利用强引力透镜和机器学习测量哈勃常数，希望解决宇宙学中出现的危机，并可能发现新的物理学。利用调查数据和人工智能重建宇宙的初始条件，创建一个诞生后仅40万年的初始三维宇宙的地图。这将为回答有关暴胀本质的问题和探索暗能量的基本性质开辟新的途径。