An Intelligent Detection Method of Astronomical Transients Based on Lightweight CNN Model

Astronomical Transients carry rich information about the nature and evolution of celestial bodies, and their detection and research have extremely important scientific value. Most of the radiation peaks of astronomical transients are in X-rays or Gamma rays. The observation advantages of space-based telescopes in these high-energy bands are unmatched by ground-based telescopes, and they are more suitable for transients observation, but due to the constraints of the performance of on-board computers, it is difficult to implement complex detection algorithms that rely on the powerful ground computing power. In response to the above problems, a transient detection algorithm is proposed based on the lightweight Convolutional Neural Network (CNN) model, and the model deployment is implemented on the embedded ARM platform. The experimental results show that the model complexity and computational complexity of the lightweight CNN transients detection algorithm proposed are less than 1/4 of the Deep Hits algorithm, while the accuracy rate can reach 96.52%, and it can be applied to a space-borne limited computing power platform to realize real-time detection of space-based transients in the future.