From cluster mass calibration to cluster cosmology

星系团是宇宙学家工具箱中必不可少的一部分。暗能量巡天为宇宙学提供了有史以来最好的星团目录。这个目录终于可以进行科学开发了。学生将发挥关键作用，利用X射线和弱透镜技术，以限制集群质量（一个必不可少的一部分，thecosmology管道）。该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），到分析噪声，时变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删除“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巡天数据的解释过渡到大型综合巡天望远镜（LSST）数据的模拟，从而进入LSST星团科学成果的预测。

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.