摘要
The Mini-SiTian(MST)project is a pathfinder for China's next-generation large-scale time-domain survey,SiTian,aimed at discovering variable stars,transients,and explosive events.MST generates hundreds of thousands of transient alerts every night,approximately 99%of which are false alarms,posing a significant challenge to its scientific goals.To mitigate the impact of false positives,we propose a deep learning–based solution and systematically evaluate 13 convolutional neural networks.The results show that ResNet achieves exceptional specificity(99.70%),EfficientNet achieves the highest recall rate(98.68%),and DenseNet provides balanced performance with a recall rate of 94.55%and specificity of 98.66%.Leveraging these complementary strengths,we developed a bagging-based ensemble classifier that integrates ResNet18,DenseNet121,and EfficientNet_B0 using a soft voting strategy.This classifier achieved the best AUC value(0.9961)among all models,with a recall rate of95.37%and specificity of 99.25%.It has now been successfully deployed in the MST real-time data processing pipeline.Validation using 5000 practically processed samples with a classification threshold of 0.798 showed that the classifier achieved 88.31%accuracy,91.89%recall rate,and 99.82%specificity,confirming its effectiveness and robustness under real application conditions.
基金
supported by the National Key Basic R&D Program of China via 2023YFA1608303
the Strategic Priority Research Program of the Chinese Academy of Sciences(XDB0550103)
the National Natural Science Foundation of China under grant Nos.12273076,12133001,12422303 and12261141690。
