Probabilistic Super-Resolution of Solar Magnetograms: Generating Many Explanations and Measuring Uncertainties

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

doi:10.48550/arxiv.1911.01486

Cited by 4 publications

(5 citation statements)

References 6 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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show abstract

“…• 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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“…This work has been enabled by the Frontier Development Lab Program (FDL), and is based on the works of Gitiaux et al (2019) and Jungbluth et al (2019), which were published in workshops at the 33rd Conference and Workshop on Neural Information Processing Systems (NeurIPS 2019). FDL is a collaboration between the SETI Institute and Trillium Technologies Inc, in partnership with NASA and private sector partners including Google Cloud, Intel, IBM, Lockheed Martin, NVIDIA, and Element AI.…”

Section: Acknowledgmentsmentioning

confidence: 99%

Physically Motivated Deep Learning to Superresolve and Cross Calibrate Solar Magnetograms

Muñoz-Jaramillo,

Jungbluth,

Gitiaux

et al. 2024

ApJS

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Superresolution (SR) aims to increase the resolution of images by recovering detail. Compared to standard interpolation, deep learning-based approaches learn features and their relationships to leverage prior knowledge of what low-resolution patterns look like in higher resolution. Deep neural networks can also perform image cross-calibration by learning the systematic properties of the target images. While SR for natural images aims to create perceptually convincing results, SR of scientific data requires careful quantitative evaluation. In this work, we demonstrate that deep learning can increase the resolution and calibrate solar imagers belonging to different instrumental generations. We convert solar magnetic field images taken by the Michelson Doppler Imager (resolution ∼2″ pixel−1; space based) and the Global Oscillation Network Group (resolution ∼2.″5 pixel−1; ground based) to the characteristics of the Helioseismic and Magnetic Imager (resolution ∼0.″5 pixel−1; space based). We also establish a set of performance measurements to benchmark deep-learning-based SR and calibration for scientific applications.

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Probabilistic Super-Resolution of Solar Magnetograms: Generating Many Explanations and Measuring Uncertainties

Cited by 4 publications

References 6 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

Physically Motivated Deep Learning to Superresolve and Cross Calibrate Solar Magnetograms

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