Revealing the Nature of Dark Matter with JWST and Euclid

暗物质的神秘本质是现代天体物理学和宇宙学中最令人困惑的问题之一。对宇宙最大尺度的观测表明，暗物质约占宇宙总物质的85%。暗物质仅通过引力相互作用，因此人眼不可见。剩下的15%是重子物质，这是一种可以通过静电力在地球上看到和感觉到的物质。虽然天文学家知道暗物质粒子存在，但他们不知道它是什么。这与重子物质形成对比，我们对电子和质子等亚原子粒子的理解提供了形成周期表的原子的全面模型。该奖学金将以比以往更高的分辨率观察暗物质-量化星系内暗物质的团块。这使用了引力透镜效应，这是一种从星系发出的光被它在宇宙中穿过的任何质量扭曲的现象。这些扭曲揭示了星系内有多少暗物质，因此测试了标准的“冷暗物质”范式的预测，即星系内有数千个单独的暗物质团块。该项目汇集了研究宇宙的宇宙学家和医疗保健研究人员，旨在改善NHS患者的癌症治疗。这两个看似对立的科学家群体之间的合作是由他们在数据科学的数字时代面临的共同挑战所驱动的;使用强大可靠的统计技术从超大数据集中提取有意义的信息。因此，该项目将进一步加深我们对基础物理学的理解，并帮助拯救地球上的生命。

The mysterious nature of dark matter is one of the most puzzling questions in modern astrophysics and cosmology. Observations on the Universe's largest scales have revealed that dark matter is approximately 85% of the Universe's total matter. Dark matter interacts via only gravitational forces, and is therefore invisible to the human eye. The remaining 15% is Baryonic matter, a type of matter that can be seen and felt on Earth via electrostatic forces. Whilst Astronomers know that a dark matter particle exists, they have no idea what it is. This contrasts baryonic matter, where our understanding of subatomic particles like the electron and proton provide a comprehensive model of atoms that form the periodic table. This fellowship will observe dark matter at higher resolution than ever before - quantifying the clumpiness of dark matter inside galaxies. This uses gravitational lensing, a phenomenon where light emanating from a galaxy is distorted by any mass it passes on its journey through the Universe. These distortions reveal how much dark matter is inside galaxies, therefore testing the standard "cold dark matter" paradigm's prediction that within galaxies there are thousands of individual dark matter clumps. This project brings together cosmologists studying the Universe with healthcare researchers aiming to improve the treatment of cancers for NHS patients. The collaboration between these two seemingly opposite groups of scientists is driven by the common challenges they face in the digital era of data science; extracting meaningful information from extremely large datasets using robust and reliable statistical techniques. This project will therefore both further our understanding of fundamental physics and help save lives on Earth.