FlareCast: A Solar Flare Forecasting System Utilizing Deep Bayesian Neural Networks and the Concept of Machine Learning Operations
文献类型：期刊论文
| 作者 | Lv, Jiameng4; Jia, Peng4; Shi, Yiwei4; Song, Yu4; Chen, Feng3; Guo, Yang3; Liu, Tie3; Ji KF(季凯帆)2; Jin ZY(金振宇)2; Lin, Jiaben5 |
| 刊名 | ASTROPHYSICAL JOURNAL SUPPLEMENT SERIES
 |
| 出版日期 | 2026-04-01 |
| 卷号 | 283期号:2 |
| ISSN号 | 0067-0049 |
| DOI | 10.3847/1538-4365/ae43dc |
| 产权排序 | 第3完成单位 |
| 文献子类 | Article |
| 英文摘要 | In this paper, we present FlareCast, a novel solar flare forecasting system developed under the framework of machine learning operations (MLOps) and made available as an online service to the community. FlareCast utilizes a deep Bayesian neural network that is specifically designed for forecasting solar flares. This neural network merges a convolutional neural network with long short-term memory to link the radial component of the vector magnetic field with anticipated maximum X-ray flux levels over the next 12-48 hr. Additionally, 24 hr forecasts are available for online access through our website. To address the uncertainties inherent in observational data, the model first predicts the probability distribution of maximal X-ray flux and then classifies these solar flares based on the predicted X-ray flux levels, rather than merely providing direct classification outcomes. Our trained Bayesian neural network achieves a precision of 97.9 for >= X-class flares on the test set, outperforming existing operational models. To assess the model's performance in practical applications, we adapt the MLOps concept. With the concept, we propose using the regression error of the maximal X-ray flux levels as a measurement criterion for the machine learning system. Additionally, we utilize attention maps from the prediction model, along with polarity reversal lines and magnetic field transition regions, to enable human users to monitor the system's performance. FlareCast has been operational since 2024 January 1, consistently providing solar flare forecasting services. |
| 学科主题 | 天文学 ; 太阳与太阳系 |
| URL标识 | 查看原文 |
| 出版地 | No.2 The Distillery, Glassfields, Avon Street, Bristol, ENGLAND |
| WOS关键词 | PREDICTION ; MODEL |
| 资助项目 | MOST divided by National Natural Science Foundation of China (NSFC)[12173027]; MOST divided by National Key Research and Development Program of China (NKPs)[2023YFF0725300]; Young Data Scientist Project of the National Astronomical Data Center[N03] |
| WOS研究方向 | Astronomy & Astrophysics |
| 语种 | 英语 |
| WOS记录号 | WOS:001711743000001 |
| 出版者 | IOP Publishing Ltd |
| 资助机构 | MOST divided by National Natural Science Foundation of China (NSFC)[12173027] ; MOST divided by National Key Research and Development Program of China (NKPs)[2023YFF0725300] ; Young Data Scientist Project of the National Astronomical Data Center[N03] |
| 版本 | 出版稿 |
| 源URL | [http://ir.ynao.ac.cn/handle/114a53/29046]  |
| 专题 | 天文技术实验室 |
| 通讯作者 | Jia, Peng |
| 作者单位 | 1.Peng Cheng Lab, Shenzhen 518066, People’s Republic of China 2.Yunnan Observatory, Chinese Academy of Sciences, Kunming, Yunnan 650011, People’s Republic of China; 3.School of Astronomy & Space Science, Nanjing University, Nanjing 210093, People’s Republic of China; 4.College of Physics and Optoelectronics, Taiyuan University of Technology, Taiyuan 030024, People’s Republic of China; robinmartin20@gmail.com; 5.National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100101, People’s Republic of China; |
| 推荐引用方式 GB/T 7714 | Lv, Jiameng,Jia, Peng,Shi, Yiwei,et al. FlareCast: A Solar Flare Forecasting System Utilizing Deep Bayesian Neural Networks and the Concept of Machine Learning Operations[J]. ASTROPHYSICAL JOURNAL SUPPLEMENT SERIES,2026,283(2). |
| APA | Lv, Jiameng.,Jia, Peng.,Shi, Yiwei.,Song, Yu.,Chen, Feng.,...&Chen, Shupei.(2026).FlareCast: A Solar Flare Forecasting System Utilizing Deep Bayesian Neural Networks and the Concept of Machine Learning Operations.ASTROPHYSICAL JOURNAL SUPPLEMENT SERIES,283(2). |
| MLA | Lv, Jiameng,et al."FlareCast: A Solar Flare Forecasting System Utilizing Deep Bayesian Neural Networks and the Concept of Machine Learning Operations".ASTROPHYSICAL JOURNAL SUPPLEMENT SERIES 283.2(2026). |
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来源：云南天文台
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