Artificial intelligence generated solar farside magnetogram using conditional generative adversarial network

Dani, Tiar; Muhamad, Johan; Nurzaman, Muhamad Zamzam; Kesumaningrum, Rasdewita; Sulistiani, Santi; Pangestu, Ayu Dyah

doi:10.1088/1742-6596/2214/1/012016

Cited by 2 publications

(2 citation statements)

References 13 publications

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“…Ugarte-Urra et al (2015) showed that integrated 304 Å light curves can be used as a proxy for the TUMF of the AR. Based on these results, several studies tried to generate solar magnetograms from the EUV 304 Å images using deep learning (Alshehhi 2020;Dani et al 2022). J20 generated more realistic magnetograms using the EUV 304, 193, and 171 Å images.…”

Section: Generation Of Solar Farside Magnetogramsmentioning

confidence: 99%

Improved AI-generated Solar Farside Magnetograms by STEREO and SDO Data Sets and Their Release

Jeong

Moon

Park

et al. 2022

ApJS

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Here we greatly improve artificial intelligence (AI)–generated solar farside magnetograms using data sets from the Solar Terrestrial Relations Observatory (STEREO) and Solar Dynamics Observatory (SDO). We modify our previous deep-learning model and configuration of input data sets to generate more realistic magnetograms than before. First, our model, which is called Pix2PixCC, uses updated objective functions, which include correlation coefficients (CCs) between the real and generated data. Second, we construct input data sets of our model: solar farside STEREO extreme-ultraviolet (EUV) observations together with nearest frontside SDO data pairs of EUV observations and magnetograms. We expect that the frontside data pairs provide historic information on magnetic field polarity distributions. We demonstrate that magnetic field distributions generated by our model are more consistent with the real ones than previously, in consideration of several metrics. The averaged pixel-to-pixel CC for full disk, active regions, and quiet regions between real and AI-generated magnetograms with 8 × 8 binning are 0.88, 0.91, and 0.70, respectively. Total unsigned magnetic flux and net magnetic flux of the AI-generated magnetograms are consistent with those of real ones for the test data sets. It is interesting to note that our farside magnetograms produce polar field strengths and magnetic field polarities consistent with those of nearby frontside magnetograms for solar cycles 24 and 25. Now we can monitor the temporal evolution of active regions using solar farside magnetograms by the model together with the frontside ones. Our AI-generated solar farside magnetograms are now publicly available at the Korean Data Center for SDO (http://sdo.kasi.re.kr).

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Section: Generation Of Solar Farside Magnetogramsmentioning

confidence: 99%

Improved AI-generated Solar Farside Magnetograms by STEREO and SDO Data Sets and Their Release

Jeong

Moon

Park

et al. 2022

ApJS

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“…The general idea of generating synthetic images is not new and has been improving dramatically over the years, including for tackling class imbalance in various domains such as medical applications (Frid-Adar et al 2018 (Xu et al 2019;Li et al 2021). So far, previous work on space weather image synthesis has focused on converting SDO/AIA imagery to SDO/HMI imagery (Dani et al 2022;Sun et al 2022), or vice versa (Dash et al 2022), and mapping SDO/AIA images to corresponding images with different extreme-ultraviolet channels (Salvatelli et al 2022). Such image-to-image translation is a class of computer vision tasks that typically involves learning a mapping function between the two domains, such that the output image preserves the content and key features of the input image.…”

Section: Introductionmentioning

confidence: 99%

Image Synthesis for Solar Flare Prediction

Amar,

Ben-Shahar

2024

ApJS

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Solar flare prediction is a topic of interest to many researchers owing to the potential of solar flares to affect various technological systems, both terrestrial and in orbit. In recent years, the forecasting task has become progressively more reliant on data-driven computations and machine-learning algorithms. Although these efforts have improved solar flare predictions, they still falter in doing so for large solar flares, in particular under operational conditions, since large-flare data are very scarce and labeled data are heavily imbalanced. In this work, we seek to address this fundamental issue and present a scheme for generating synthetic magnetograms to reduce the imbalance in the data. Our method consists of (1) synthetic oversampling of line-of-sight magnetograms using Gaussian mixture model representation, followed by (2) a global optimization technique to ensure consistency of both physical features and flare precursors, and (3) the mapping of the generated representations to realistic magnetogram images using deep generative models. We show that these synthetically generated data indeed improve the capacity of solar flare prediction models and that, when tested on such a state-of-the-art model, it significantly enhances its forecasting performance, achieving an F1-score as high as 0.43 ± 0.08 and a true skill statistic of 0.64 ± 0.10 for X-class flares in the 24 hr operational solar flare data split.

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Artificial intelligence generated solar farside magnetogram using conditional generative adversarial network

Cited by 2 publications

References 13 publications

Improved AI-generated Solar Farside Magnetograms by STEREO and SDO Data Sets and Their Release

Improved AI-generated Solar Farside Magnetograms by STEREO and SDO Data Sets and Their Release

Image Synthesis for Solar Flare Prediction

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