一种基于轻量化CNN的天文暂现源智能识别方法

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

天文暂现源携带了关于天体本质及演化过程的丰富信息,对暂现源进行探测与研究具有极为重要的科学价值。天文暂现源的辐射峰值大多在X射线或伽马射线,天基望远镜对这些高能波段的观测优势是地基望远镜无法比拟的,更适合于暂现源观测。但由于星载计算机的性能约束,很难实现依托于地面强大算力的复杂检测算法。针对以上问题,提出了基于轻量化卷积神经网络(CNN)模型的天基暂现源检测算法,并在嵌入式ARM平台上实现了模型部署。实验结果表明,本文提出的轻量化CNN暂现源检测算法的模型复杂度和计算量不及Deep Hits算法的1/4,准确率达到96.52%,可应用于星载有限算力平台,实现未来的天基暂现源实时检测。

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 spacebased 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 spaceborne limited computing power platform to realize real-time detection of space-based transients in the future.