Target Detection Framework for Lobster Eye X-Ray Telescopes with Machine Learning Algorithms
Peng Jia, Wenbo Liu, Yuan Liu, Haiwu Pan

TL;DR
This paper presents a machine learning-based target detection framework tailored for lobster eye X-ray telescopes, effectively handling their unique image distortions to identify celestial objects with high accuracy.
Contribution
The authors develop a novel multi-step machine learning framework specifically designed for lobster eye telescope data, improving detection accuracy and efficiency.
Findings
Achieves over 94% purity and 90% completeness for bright targets.
Effective detection of low-flux celestial objects with high purity.
Framework applicable to other lobster eye X-ray telescopes.
Abstract
Lobster eye telescopes are ideal monitors to detect X-ray transients, because they could observe celestial objects over a wide field of view in X-ray band. However, images obtained by lobster eye telescopes are modified by their unique point spread functions, making it hard to design a high efficiency target detection algorithm. In this paper, we integrate several machine learning algorithms to build a target detection framework for data obtained by lobster eye telescopes. Our framework would firstly generate two 2D images with different pixel scales according to positions of photons on the detector. Then an algorithm based on morphological operations and two neural networks would be used to detect candidates of celestial objects with different flux from these 2D images. At last, a random forest algorithm will be used to pick up final detection results from candidates obtained by…
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Taxonomy
TopicsStatistical and numerical algorithms · Astronomical Observations and Instrumentation · Gamma-ray bursts and supernovae
