The application of deep learning to X ray astronomy

深度学习已经在光学/lR星系图像的分类中发挥了很大的作用(例如arxiv.org/abs/1709.05834; adsabs.harvard.edu/abs/2017IAUS ..325 . .205 c)。到目前为止，这种技术已经超越了银河动物园，使人类分类变得多余。该学生将对来自xmm -牛顿卫星的10,000多张x射线图像应用相同的方法。一旦对现有数据进行改进，该学生将制定分析方案，使即将到来的e-ROSITA和Athena任务的科学产出最大化。除了这个项目的数据科学方面，它还将促进几项天文学研究。由此产生的对星团选择函数的改进知识将提高从星团数中提取的宇宙学参数的精度，以及对星团内介质中起作用的物理的理解。由此产生的非星团XMM源群保真度的提高将有助于研究稀有物体，如孤立的中子星、x射线闪光和遥远的类星体。

Deep learning has been used to great effect in the classification of optical/lR galaxy images (e.g. arxiv.org/abs/1709.05834; adsabs.harvard.edu/abs/2017IAUS .. 325 .. 205C). By now this technique out performs Galaxy Zoo and has made human classifications redundant. The student will apply the same approach to more than 10,000 X-ray images from the XMM-Newton satellite. Once refined on existing data, the student will develop analysis schemes that will maximise the science output of the forthcoming e-ROSITA and Athena missions. In addition to the data science aspects of this project, it will facilitate several astronomy studies. The resulting improved knowledge of the cluster selection function will enhance both the precision of cosmological parameters extracted from cluster number counts, and understanding of the physics at play in the intracluster medium. The resulting improved fidelty of the non-cluster XMM source population will facilitate studies of rare objects, such as Isolated Neutron Stars, X-ray Flashes and distant quasars.