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悬而未决的问题之一，有可能揭示粒子物理学标准模型之外的基础物理学，从而使我们对自然世界的理解发生巨大的飞跃。在亚星系尺度上测量暗物质的聚集是揭示其粒子性质的最有前途的研究前沿。这个加拿大研究主席的目标是量化遥远星系中暗物质subhalos的丰度，使用强引力透镜来测量暗物质粒子的温度。