Source detection algorithm for lobster eye telescopes with machine learning algorithms

Abstract

Lobster eye telescopes are a type of innovative telescope design, which could observe celestial objects over a very wide field of view in x-ray band. Thanks to this property, lobster eye telescopes are widely used to detect x-ray transients in time-domain astronomy. However, images obtained by lobster eye telescopes are modified by their unique point spread functions, which would spread photons from point sources to large images with crucify structure. Therefore, it is hard to design an automatic source detection algorithm with high efficiency and fast speed. Manual interventions are always required to modify parameters of contemporary methods to fit data properties of each observed images. In this paper, we will review the classical method and several new methods proposed by our group to detect sources from images obtained by lobster eye telescopes. We have compared the performance of different methods and results show that we would require to integrate different methods to develop a pipeline to process images obtained by lobster eye telescopes.