Photo voltaic radio bursts and their positive spectral constructions include key bodily data associated to plasma instabilities, high-energy particle acceleration, and different necessary processes. They due to this fact function an necessary observational means for learning photo voltaic exercise and area climate. With the rising quantity of photo voltaic radio spectrogram remark information, deep learning-based recognition and detection of photo voltaic radio bursts have turn into a key analysis route. Nevertheless, most research on meter-wave photo voltaic radio spectrogram recognition and detection depend on proprietary datasets, and publicly accessible datasets stay scarce. To handle this difficulty and facilitate using public datasets for deep studying mannequin validation, thereby selling the event of computerized detection strategies for meter-wave photo voltaic radio spectrogram, we suggest a brand new photo voltaic radio spectrogram dataset. In our research, we additional examine the dataset development course of, its utilization, and the coaching efficiency achieved based mostly on this dataset.
The dataset is publicly accessible at: http://62.234.23.17/SRData/SRSD/.
This research constructs a public dataset based mostly on Learmonth Observatory in Australia and the meter-wavelength observing system of the Chashan Photo voltaic radio Observatory (CSO) of Shandong College. To fulfill the wants of various researchers, we divide the dataset into three components. The primary is the photo voltaic radio spectrogram dataset A (SRSD-A), which is designed for the automated classification and identification of spectrogram remark information into three classes: burst, non-burst, and irregular information. 。The second is the photo voltaic radio burst dataset B (SRBDB-B), which can be utilized for the localization and detection of 5 forms of meter-wave photo voltaic radio bursts, specifically sort II, sort III, sort IIIs, sort IV, and sort V bursts. The third is the photo voltaic radio burst dataset C (SRBD-C), which is meant for the localization and detection of three forms of photo voltaic radio bursts: sort II, sort III, and sort IIIs. Based mostly on the above datasets, a number of deep studying fashions are skilled to confirm their effectiveness. The skilled fashions might be utilized to the popularity and detection of photo voltaic radio spectrograms. Determine 1 exhibits the YOLO11 community mannequin.
Determine 1: Schematic diagram of the YOLO11 mannequin structure.
We validated Dataset A utilizing a number of deep studying architectures, together with ResNet, ConvNeXt, and MobileViT. The classification accuracy exceeded 99%, demonstrating wonderful classification efficiency. Based mostly on the burst detection datasets, we skilled the YOLO11 mannequin on Dataset B and Dataset C. Determine 2 exhibits the coaching course of for Dataset B. As might be seen from the determine, the general coaching course of was comparatively secure. As well as, the skilled mannequin was validated on a extra complicated dataset, attaining a precision of 0.702 and a recall of 0.645, which demonstrates its good generalization functionality. Nevertheless, some misclassified samples nonetheless stay. Noise within the spectrograms and the complexity of burst morphologies are nonetheless challenges that must be addressed. Rising the quantity of knowledge would assist additional enhance the coaching accuracy.
Determine 2: YOLO11 mannequin coaching efficiency in Dataset-B
Conclusion
The outcomes present that the dataset proposed on this research can successfully help the popularity and detection of photo voltaic radio observational information. This supplies a unified dataset for experimental analysis on photo voltaic radio burst detection, thereby supporting research on photo voltaic radio information processing and burst detection, whereas additionally selling the combination of radio astronomy and synthetic intelligence. In future work, we plan to additional optimize and develop the dataset by incorporating further information sources, whereas additionally bettering the objectivity of the annotation course of and enhancing the applicability of the dataset. These datasets are anticipated to enhance the detection functionality for future burst occasions and advance associated analysis.
Based mostly on a lately printed article: Yan Liu , Hongqiang Tune , Fabao Yan and Yanrui Su, Dataset for Recognition and Detection Based mostly on Photo voltaic Radio Spectrogram Knowledge, Analysis in Astronomy and Astrophysics,26:037001 (2026), DOI: https://doi.org/10.1088/1674-4527/ae2b5a
