Single-Frame Super-Resolution of Solar Magnetograms: Investigating Physics-Based Metrics \&amp; Losses

Jungbluth, Anna; Gitiaux, Xavier; Maloney, Shane A.; Shneider, Carl; Wright, Paul J.; Kalaitzis, Alfredo; Deudon, Michel; Baydin, Atılım Güneş; Gal, Yarin; Muñoz-Jaramillo, Andrés

doi:10.48550/arxiv.1911.01490

Cited by 8 publications

(11 citation statements)

References 18 publications

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“…Magnetogram fusion can be performed in the other direction: super-resolving magnetograms in SMARP to mimic those in SHARP. Such an approach has been recently explored using DNNs (Gitiaux et al 2019;Jungbluth et al 2019). The improved overall image quality of super-resolved SMARP magnetograms could capture higher-resolution magnetic field distributions and hence improve the accuracy of the active region summary parameters in SMARP.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Predicting Solar Flares Using CNN and LSTM on Two Solar Cycles of Active Region Data

Sun

Bobra

Wang

et al. 2022

ApJ

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We consider the flare prediction problem that distinguishes flare-imminent active regions that produce an M- or X-class flare in the succeeding 24 hr, from quiet active regions that do not produce any flares within ±24 hr. Using line-of-sight magnetograms and parameters of active regions in two data products covering Solar Cycles 23 and 24, we train and evaluate two deep learning algorithms—a convolutional neural network (CNN) and a long short-term memory (LSTM)—and their stacking ensembles. The decisions of CNN are explained using visual attribution methods. We have the following three main findings. (1) LSTM trained on data from two solar cycles achieves significantly higher true skill scores (TSSs) than that trained on data from a single solar cycle with a confidence level of at least 0.95. (2) On data from Solar Cycle 23, a stacking ensemble that combines predictions from LSTM and CNN using the TSS criterion achieves a significantly higher TSS than the “select-best” strategy with a confidence level of at least 0.95. (3) A visual attribution method called “integrated gradients” is able to attribute the CNN’s predictions of flares to the emerging magnetic flux in the active region. It also reveals a limitation of CNNs as flare prediction methods using line-of-sight magnetograms: it treats the polarity artifact of line-of-sight magnetograms as positive evidence of flares.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Predicting Solar Flares Using CNN and LSTM on Two Solar Cycles of Active Region Data

Sun

Bobra

Wang

et al. 2022

ApJ

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“…• Magnetogram fusion can be performed in the other direction: super-resolving magnetograms in SMARP to mimic those in SHARP. Such an approach has been recently explored using deep neural networks (Gitiaux et al 2019;Jungbluth et al 2019). The improved overall image quality of super-resolved SMARP magnetograms could capture higher resolution magnetic field distributions and hence improve the accuracy of the active region summary parameters in SMARP.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Predicting Solar Flares Using CNN and LSTM on Two Solar Cycles of Active Region Data

Sun,

Bobra,

Wang

et al. 2022

Preprint

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We consider the flare prediction problem that distinguishes flare-imminent active regions that produce an M-or X-class flare in the future 24 hours, from quiet active regions that do not produce any flare within ±24 hours. Using line-of-sight magnetograms and parameters of active regions in two data products covering Solar Cycle 23 and 24, we train and evaluate two deep learning algorithms-CNN and LSTM-and their stacking ensembles. The decisions of CNN are explained using visual attribution methods. We have the following three main findings. (1) LSTM trained on data from two solar cycles achieves significantly higher True Skill Scores (TSS) than that trained on data from a single solar cycle with a confidence level of at least 0.95. (2) On data from Solar Cycle 23, a stacking ensemble that combines predictions from LSTM and CNN using the TSS criterion achieves significantly higher TSS than the "select-best" strategy with a confidence level of at least 0.95. (3) A visual attribution method called Integrated Gradients is able to attribute the CNN's predictions of flares to the emerging magnetic flux in the active region. It also reveals a limitation of CNN as a flare prediction method using line-of-sight magnetograms: it treats the polarity artifact of line-of-sight magnetograms as positive evidence of flares.

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“…It was trained and tested on the LR HMI magnetograms instead of the MDI magnetograms. Jungbluth et al (2019) superresolved the full-disk MDI magnetograms to the same resolution as HMI magnetograms using the HighRes-Net method (Deudon et al 2020). Dou et al (2022) employed the SPSR-GAN model (Ma et al 2020) for superresolving the MDI magnetograms of the active region (AR).…”

Section: Introductionmentioning

confidence: 99%

A Multibranch Deep Neural Network for the Superresolution of Solar Magnetograms

Dou,

Xu,

Zhao

et al. 2024

ApJS

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The existing superresolution (SR) models for solar magnetograms are mostly borrowed from the SR models for natural images. They are less effective for processing solar magnetograms with a very large dynamic range and very rich image features. In this paper, a multibranch superresolution (MBSR) model is specially designed for solar magnetograms. First, we split a low-resolution magnetogram into a group of overlapping image patches, and classify them into three categories according to magnetic flux intensity, namely simple, medium, and complex. Then, image patches of each category are fed into the corresponding branch of the MBSR network, the lightweight branch for simple image patches and the heavyweight one for complex image patches. The advantage of such a strategy is twofold. On the one hand, active regions are allocated more computational resources to train a heavyweight branch more fully, while quiet regions are allocated fewer computational resources to train a lightweight branch for saving computational resources. On the other hand, a lightweight network with a simple nonlinear function is preferable to simple regions, while a heavyweight one may be underfitting. Additionally, to verify the effectiveness of the proposed model, a magnetic field structure similarity metric is proposed to measure the artifacts of the generated high-resolution (HR) magnetograms. Experimental results show that the proposed MBSR model generates HR magnetograms highly consistent with the HMI ones, and achieves the best performance over five objective metrics, including peak signal-to-noise ratio and structure similarity, etc.

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Single-Frame Super-Resolution of Solar Magnetograms: Investigating Physics-Based Metrics \& Losses

Cited by 8 publications

References 18 publications

Predicting Solar Flares Using CNN and LSTM on Two Solar Cycles of Active Region Data

Predicting Solar Flares Using CNN and LSTM on Two Solar Cycles of Active Region Data

Predicting Solar Flares Using CNN and LSTM on Two Solar Cycles of Active Region Data

A Multibranch Deep Neural Network for the Superresolution of Solar Magnetograms

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