Generating a radioheliograph image from SDO/AIA data with the machine learning method

Zhang, Peijin; Wang, Chuanbing; Pu, Guanshan

doi:10.1088/1674-4527/20/12/204

Cited by 4 publications

(2 citation statements)

References 22 publications

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“…Concretely, the proposed model receptively improves 4.0980 dB, 0.4769 dB and 0.6175 dB in PSRN, and 0.0999, 0.0160 and 0.0195 in SSIM, compared with Mask-Pix2Pix, PCGAN and MCNet (w/o VMC). Following Zhang et al (2020), we also analyze the linear fitting result between the ground-truth pixels and recovered pixels in saturated regions. The linear fitting lines for the last example in Figure 5 are shown in Figure 6.…”

Section: Comparisons With State-of-the-artmentioning

confidence: 99%

Image Desaturation for SDO/AIA Using Mixed Convolution Network

Yu¹,

Xu²,

Ren³

et al. 2022

Res. Astron. Astrophys.

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The Atmospheric Imaging Assembly (AIA) onboard the Solar Dynamics Observatory (SDO) provides full-disk solar images with high temporal cadence and spatial resolution over seven extreme ultraviolet (EUV) wavebands. However, as violent solar flares happen, images captured in EUV wavebands may have saturation in active regions, resulting in information loss. In this paper, we propose a deep learning model to restore the lost signal in saturated regions by referring to both unsaturated/normal regions within a solar image and statistical probability model of massive normal solar images. The proposed model, namely mixed convolution network (MCNet), is established over conditional generative adversarial network (GAN) and the combination of partial convolution (PC) and validness migratable convolution (VMC). These two convolutions were originally proposed for image inpainting. In addition, they are implemented only on unsaturated/valid pixels, followed by certain compensation to compensate the deviation of PC/VMC relative to normal convolution. Experimental results demonstrate that the proposed MCNet achieves favorable desaturated results for solar images and outperforms the state-of-the-art methods both quantitatively and qualitatively.

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Section: Comparisons With State-of-the-artmentioning

confidence: 99%

Image Desaturation for SDO/AIA Using Mixed Convolution Network

Yu¹,

Xu²,

Ren³

et al. 2022

Res. Astron. Astrophys.

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“…Machine learning methods were utilized in different research by various researchers [2][3][4][5] to forecast the habitability of exoplanets based on their atmospheric makeup. The researchers developed a machine learning model utilizing decision trees, random forests, and support vector machines using a dataset of known exoplanets with determined atmospheric compositions.…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Methodology for Hunting Exoplanets in Space Using Machine Learning

Singh,

Agarwal,

Yadav

2024

EAI Endorsed Trans IoT

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INTRODUCTION: Exoplanet exploration outside of our solar system has recently attracted attention among astronomers worldwide. The accuracy of the currently used detection techniques, such as the transit and radial velocity approaches is constrained. Researchers have suggested utilizing machine learning techniques to create a prediction model to increase the identification of exoplanets beyond our milky way galaxy. OBJECTIVES: The novel method proposed in this research paper builds a prediction model using a dataset of known exoplanets and their characteristics, such as size, distance from the parent star, and orbital period. The model is then trained using this data based on machine learning methods that Support Vector Machines and Random Forests. METHODS: A different dataset of recognized exoplanets is used to assess the model’s accuracy, and the findings are compared with in comparison to accuracy rates of the transit and radial velocity approaches. RESULTS: The prediction model created in this work successfully predicts the presence of exoplanets in the test data-set with an accuracy rate of over 90 percent. CONCLUSION: This discovery shows the promise and confidence of machine learning techniques for exoplanet detection.

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Mapping Solar X-Ray Images from SDO/AIA EUV Images by Deep Learning

Hong

Liu

et al. 2021

ApJ

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The full-Sun corona is now imaged every 12 s in extreme ultraviolet (EUV) passbands by Solar Dynamics Observatory/Atmospheric Imaging Assembly (AIA), whereas it is only observed several times a day at X-ray wavelengths by Hinode/X-Ray Telescope (XRT). In this paper, we apply a deep-learning method, i.e., the convolution neural network (CNN), to establish data-driven models to generate full-Sun X-ray images in XRT filters from AIA EUV images. The CNN models are trained using a number of data pairs of AIA six-passband (171, 193, 211, 335, 131, and 94 Å) images and the corresponding XRT images in three filters: “Al_mesh,” “Ti_poly,” and “Be_thin.” It is found that the CNN models predict X-ray images in good consistency with the corresponding well-observed XRT data. In addition, the purely data-driven CNN models are better than the conventional analysis method of the coronal differential emission measure (DEM) in predicting XRT-like observations from AIA data. Therefore, under conditions where AIA provides coronal EUV data well, the CNN models can be applied to fill the gap in limited full-Sun coronal X-ray observations and improve pool-observed XRT data. It is also found that DEM inversions using AIA data and our deep-learning-predicted X-ray data jointly are better than those using AIA data alone. This work indicates that deep-learning methods provide the opportunity to study the Sun based on virtual solar observation in future.

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Generating a radioheliograph image from SDO/AIA data with the machine learning method

Cited by 4 publications

References 22 publications

Image Desaturation for SDO/AIA Using Mixed Convolution Network

Image Desaturation for SDO/AIA Using Mixed Convolution Network

A Novel Methodology for Hunting Exoplanets in Space Using Machine Learning

Mapping Solar X-Ray Images from SDO/AIA EUV Images by Deep Learning

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