Machine Learning for High-Energy Astronomy Surveys

高能天文学调查的机器学习

• 批准号: 1808013
• 金额: --
• 依托单位: University of Southampton
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2016
• 资助国家: 英国
• 起止时间: 2016 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-1808013
• 关键词: Machine; Learning; Energy; Astronomy; Surveys

The aim of my research is to radically improve existing source detection algorithms by implementing a system that searches all points on the sky on all timescales for significant emission. This will involve looking at 'smarter' ways to do surveys, and applying new data science and machine learning techniques, especially for transient detection.I use supervised deep learning methods such as convolutional neural networks to detect and eventually classify both persistent and transient X-ray sources. To look for when one of these sources is in outburst I use unsupervised methods such as k-means clustering of time series subsequences which is using anomaly detection on the temporal data.

我的研究目的是通过实现一个系统来从根本上改进现有的源检测算法，该系统可以在所有时间尺度上搜索天空中的所有点，以寻找显著的排放。这将包括寻找更智能的方法进行调查，并应用新的数据科学和机器学习技术，特别是在瞬时探测方面。我使用卷积神经网络等有监督的深度学习方法来探测持久和瞬时X射线源并最终对其进行分类。为了寻找这些源中的一个何时发生突发，我使用了非监督方法，例如对时间序列子序列进行k-均值聚类，这是在时间数据上使用异常检测。