The Deep Neural Network based Photometry and Astrometry Framework for Wide Field Small Aperture Telescopes

Abstract

Wide field small aperture telescopes (WFSATs) are mainly used to obtain scientific information of point-like and streak-like celestial objects. However, qualities of images obtained by WFSATs are seriously affected by the background noise and variable point spread functions. Developing high speed and high efficiency data processing method is of great importance for further scientific research. In recent years, deep neural networks have been proposed for detection and classification of celestial objects and have shown better performance than classical methods. In this paper, we further extend abilities of the deep neural network based astronomical target detection framework to make it suitable for photometry and astrometry. We add new branches into the deep neural network to obtain types, magnitudes and positions of different celestial objects at the same time. Tested with simulated data, we find that our neural network has better performance in photometry than classical methods. Because photometry and astrometry are regression algorithms, which would obtain high accuracy measurements instead of rough classification results, the accuracy of photometry and astrometry results would be affected by different observation conditions. To solve this problem, we further propose to use reference stars to train our deep neural network with transfer learning strategy when observation conditions change. The photometry framework proposed in this paper could be used as an end-to-end quick data processing framework for WFSATs, which can further increase response speed and scientific outputs of WFSATs.

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

“…We set the prediction result being positive for the positive sample as the TP, the prediction result being positive for the negative sample as the FP, the prediction result being negative for the negative sample as the true negative (TN), and the prediction result being negative for the positive sample as the FN. It should be noted that our detection algorithm framework is used to obtain the rough position of celestial objects, and we further obtain positions of celestial objects with high accuracy through regression algorithms (Jia et al 2021).…”

“…Our framework could provide a position accuracy of around 0.5 pixels (1 1). Higher positioning accuracy could be achieved with regression algorithms (Jia et al 2021). To test the performance of our framework in detection of celestial objects from data obtained with different exposure times, we have generated 2D images from data with different exposure times (from 100 to 1200 s, with intervals of 100 s).…”