Crc In Astrophysical Data Analysis And Machine Learning

天体物理数据分析和机器学习中的 Crc

• 批准号: CRC-2019-00420
• 负责人: Hezaveh, Yashar
• 金额: $ 8.74万
• 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: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=730832
• 关键词: Crc; Astrophysical; Data; Analysis; Machine

The nature of dark matter remains one of the most important outstanding questions in cosmology, with the potential to reveal fundamental physics beyond the Standard Model of particle physics, resulting in giant leaps in our understanding of the natural world. Measuring the clustering of dark matter on the poorly-understood sub-galactic scales is the most promising frontier of research to reveal its particle properties. The goal of this Canada Research Chair is to quantify the abundance of dark matter subhalos in distant galaxies using strong gravitational lensing to measure the temperature of the dark matter particle.

暗物质的性质仍然是宇宙学中最重要的问题之一，有可能揭示超出粒子物理标准模型的基本物理学，从而导致我们对自然世界的理解巨大。测量在不良理解的半乳酸尺度上的暗物质的聚类是揭示其粒子特性的最有前途的研究前沿。这位加拿大研究椅的目的是使用强力透镜来量化遥远星系中暗物质Subhalos的丰度，以测量暗物质颗粒的温度。