From cluster mass calibration to cluster cosmology

从星团质量校准到星团宇宙学

• 批准号: 2131277
• 金额: --
• 依托单位: University of Sussex
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2131277
• 关键词: cluster; mass; calibration; cosmology

Clusters are an essential part of the Cosmologist's tool box. The Dark Energy Survey hasproduced the best ever cluster catalogue for cosmology. This catalogue is finally ready forscientific exploitation. The student will play a key role in that exploitation, by using X-rayand weak lensing techniques to constrain cluster masses (an essential part of thecosmology pipeline). The project is data intensive in every aspect: from the billions ofgalaxies that make up the cluster catalogue (each galaxy having 20+ data parameters), tothe complexities of the analysis of noisy, time varying, X-ray spectra. Machine learning willbe an integral part of the project, because we need to remove as much of the "human" aspossible to ensure we do not prejudice our results. In the later years of the project, thestudent will naturally transition from the interpretation of data from the Dark Energy Surveyinto the simulation of Large Synoptic Survey Telescope (LSST) data and thus into theforecasting of LSST cluster science outcomes.

星系团是宇宙学家工具箱的重要组成部分。暗能量调查为宇宙学提供了有史以来最好的星团目录。这个目录终于可以供科学利用了。该学生将通过使用x射线和弱透镜技术来限制星团质量（宇宙学管道的重要组成部分），在开发中发挥关键作用。这个项目在各个方面都是数据密集型的：从组成星系团目录的数十亿个星系（每个星系有20多个数据参数），到分析嘈杂、时变的x射线光谱的复杂性。机器学习将是该项目不可或缺的一部分，因为我们需要尽可能多地去除“人类”，以确保我们不会影响我们的结果。在项目的后期，学生将自然地从暗能量调查数据的解释过渡到大型天气测量望远镜（LSST）数据的模拟，从而过渡到LSST集群科学成果的预测。