The Mini-SiTian Array: real-bogus classification using deep learning
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917974380642304 |
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| author | Shi, Jing-Hang Gu, Hong-Rui Huang, Yang Zhang, Yan-Xia Du, Peng-Liang |
| author_facet | Shi, Jing-Hang Gu, Hong-Rui Huang, Yang Zhang, Yan-Xia Du, Peng-Liang |
| contents | 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 thirteen 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 of 95.37\% and specificity of 99.25\%. It has now been successfully deployed in the MST real-time data processing pipeline. Validation using 5,000 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_01608 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The Mini-SiTian Array: real-bogus classification using deep learning Shi, Jing-Hang Gu, Hong-Rui Huang, Yang Zhang, Yan-Xia Du, Peng-Liang Instrumentation and Methods for Astrophysics 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 thirteen 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 of 95.37\% and specificity of 99.25\%. It has now been successfully deployed in the MST real-time data processing pipeline. Validation using 5,000 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. |
| title | The Mini-SiTian Array: real-bogus classification using deep learning |
| topic | Instrumentation and Methods for Astrophysics |
| url | https://arxiv.org/abs/2504.01608 |