Automatic detection of CMEs using synthetically-trained Mask R-CNN
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866910014449385472 |
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| author | Iglesias, Francisco A. Lloveras, Diego G. Cisterna, Florencia L. Cremades, Hebe Toledo, Mariano Sanchez López, Fernando M. Machuca, Yasmin Manini, Franco Ramos, Andrés Asensio |
| author_facet | Iglesias, Francisco A. Lloveras, Diego G. Cisterna, Florencia L. Cremades, Hebe Toledo, Mariano Sanchez López, Fernando M. Machuca, Yasmin Manini, Franco Ramos, Andrés Asensio |
| contents | Coronal mass ejections (CMEs) are a major driver of space weather. To assess CME geoeffectiveness, among other scientific goals, it is necessary to reliably identify and characterize their morphology and kinematics in coronagraph images. Current methods of CME identification are either subjected to human biases or perform a poor identification due to deficiencies in the automatic detection. In this approach, we have trained the deep convolutional neural model Mask R-CNN to automatically segment the outer envelope of one or multiple CMEs present in a single difference coronagraph image. The empirical training dataset is composed of 10^5 synthetic coronagraph images with known pixel-level CME segmentation masks. It is obtained by combining quiet coronagraph observations, with synthetic white-light CMEs produced using the GCS geometric model and ray-tracing technique. We found that our model-based trained Mask R-CNN infers segmentation masks that are smooth and topologically connected. While the inferred masks are not representative of the detailed outer envelope of complex CMEs, the neural model can better differentiate a CME from other radially moving background/foreground features, segment multiple simultaneous CMEs that are close to each other, and work with images from different instruments. This is accomplished without relying on kinematic information, i.e. only the included in the single input difference image. We obtain a median IoU=0.98 for 1.6*10^4 synthetic validation images, and IoU=0.77 when compared with two independent manual segmentations of 115 observations acquired by the COR2-A, COR2-B and LASCO C2 coronagraphs. The methodology presented in this work can be used with other CME models to produce more realistic synthetic brightness images while preserving desired morphological features, and obtain more robust and/or tailored segmentations. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2511_04589 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Automatic detection of CMEs using synthetically-trained Mask R-CNN Iglesias, Francisco A. Lloveras, Diego G. Cisterna, Florencia L. Cremades, Hebe Toledo, Mariano Sanchez López, Fernando M. Machuca, Yasmin Manini, Franco Ramos, Andrés Asensio Solar and Stellar Astrophysics Coronal mass ejections (CMEs) are a major driver of space weather. To assess CME geoeffectiveness, among other scientific goals, it is necessary to reliably identify and characterize their morphology and kinematics in coronagraph images. Current methods of CME identification are either subjected to human biases or perform a poor identification due to deficiencies in the automatic detection. In this approach, we have trained the deep convolutional neural model Mask R-CNN to automatically segment the outer envelope of one or multiple CMEs present in a single difference coronagraph image. The empirical training dataset is composed of 10^5 synthetic coronagraph images with known pixel-level CME segmentation masks. It is obtained by combining quiet coronagraph observations, with synthetic white-light CMEs produced using the GCS geometric model and ray-tracing technique. We found that our model-based trained Mask R-CNN infers segmentation masks that are smooth and topologically connected. While the inferred masks are not representative of the detailed outer envelope of complex CMEs, the neural model can better differentiate a CME from other radially moving background/foreground features, segment multiple simultaneous CMEs that are close to each other, and work with images from different instruments. This is accomplished without relying on kinematic information, i.e. only the included in the single input difference image. We obtain a median IoU=0.98 for 1.6*10^4 synthetic validation images, and IoU=0.77 when compared with two independent manual segmentations of 115 observations acquired by the COR2-A, COR2-B and LASCO C2 coronagraphs. The methodology presented in this work can be used with other CME models to produce more realistic synthetic brightness images while preserving desired morphological features, and obtain more robust and/or tailored segmentations. |
| title | Automatic detection of CMEs using synthetically-trained Mask R-CNN |
| topic | Solar and Stellar Astrophysics |
| url | https://arxiv.org/abs/2511.04589 |