Target Detection Framework for Lobster Eye X-Ray Telescopes with Machine-learning Algorithms

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 the 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 first 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. Finally, a random forest algorithm will be used to pick up final detection results from candidates obtained by previous steps. Tested with simulated data of the Wide-field X-ray Telescope on board the Einstein Probe, our detection framework could achieve over 94% purity and over 90% completeness for targets with flux more than 3 mcrab (9.6 × 10−11 erg cm−2 s−1) and more than 94% purity and moderate completeness for targets with lower flux at acceptable time cost. The framework proposed in this paper could be used as references for data processing methods developed for other lobster eye X-ray telescopes.

CSST Strong-lensing Preparation: A Framework for Detecting Strong Lenses in the Multicolor Imaging Survey by the China Survey Space Telescope (CSST)

“…Generating simulated data, as discussed in our previous papers (Jia et al 2022(Jia et al , 2023a, serves a crucial purpose in the detection of celestial objects, particularly when sufficient training data are lacking. Simulated data provide valuable prior information about the targets we aim to detect.…”

PNet—A Deep Learning Based Photometry and Astrometry Bayesian Framework

“…Moreover, transients, such as supernovae in galaxies, can be further processed using methods based on the image difference or techniques developed based on temporal sequences of images (Kessler et al 2015;Wright et al 2015;Zackay et al 2016;Sánchez et al 2019;Gómez et al 2020;Mong et al 2020;Hu et al 2022;Makhlouf et al 2022). A previous study by Jia et al (2020) has introduced a faster-RCNN-based framework for detecting point-like celestial objects, which was successfully applied to images captured by wide-field optical telescopes and the Lobster-Eye telescope (Jia et al 2023a). However, in real applications, three key challenges need to be addressed:…”