Machine Learning for High-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.