The application of deep learning to X ray astronomy

深度学习在X射线天文学中的应用

• 批准号: 2022929
• 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: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2022929
• 关键词: application; deep; learning; ray; astronomy

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.

深度学习已经在光学/IR星系图像的分类中发挥了巨大作用（例如arxiv.org/abs/1709.05834; adsabs.harvard.edu/abs/2017IAUS）。325... 205 C）。到目前为止，这项技术的性能超过了Galaxy Zoo，并且使人类分类变得多余。学生将应用相同的方法从XMM-牛顿卫星超过10，000 X射线图像。一旦对现有数据进行改进，学生将开发分析方案，最大限度地提高即将到来的e-ROSITA和雅典娜任务的科学产出。除了该项目的数据科学方面，它还将促进几项天文学研究。由此产生的改进知识的集群选择功能将提高从集群数量计数中提取的宇宙学参数的精度，并在集群内介质中发挥的物理学的理解。由此产生的非星团XMM源群的改进的可靠性将促进稀有物体的研究，如孤立中子星，X射线闪光和遥远的类星体。