Detection and Classification of Astronomical Targets with Deep Neural Networks in Wide Field Small Aperture Telescopes
This addresses the need for automated and improved detection/classification of astronomical targets, particularly dim ones, for optical transient observations, though it is incremental as it adapts existing methods to this domain.
The paper tackles astronomical target detection and classification in wide field small aperture telescopes by proposing a deep neural network framework based on Faster R-CNN with ResNet-50 and Feature Pyramid Network, achieving 2 times better detection for dim targets in simulated data and 25% improvement in real observation data.
Wide field small aperture telescopes are widely used for optical transient observations. Detection and classification of astronomical targets in observed images are the most important and basic step. In this paper, we propose an astronomical targets detection and classification framework based on deep neural networks. Our framework adopts the concept of the Faster R-CNN and uses a modified Resnet-50 as backbone network and a Feature Pyramid Network to extract features from images of different astronomical targets. To increase the generalization ability of our framework, we use both simulated and real observation images to train the neural network. After training, the neural network could detect and classify astronomical targets automatically. We test the performance of our framework with simulated data and find that our framework has almost the same detection ability as that of the traditional method for bright and isolated sources and our framework has 2 times better detection ability for dim targets, albeit all celestial objects detected by the traditional method can be classified correctly. We also use our framework to process real observation data and find that our framework can improve 25 % detection ability than that of the traditional method when the threshold of our framework is 0.6. Rapid discovery of transient targets is quite important and we further propose to install our framework in embedded devices such as the Nvidia Jetson Xavier to achieve real-time astronomical targets detection and classification abilities.