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Context.High-resolution magnetograms are crucial for studying solar flare dynamics because they enable the precise tracking of magnetic structures and rapid field changes. The Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory (SDO/HMI) has been an essential provider of vector magnetograms. However, the spatial resolution of the HMI magnetograms is limited and hence is not able to capture the fine structures that are essential for understanding flare precursors. The Near InfraRed Imaging Spectropolarimeter on the 1.6 m Goode Solar Telescope (GST/NIRIS) at Big Bear Solar Observatory (BBSO) provides a better spatial resolution and is therefore more suitable to track the fine magnetic features and their connection to flare precursors. Aims.We propose DeepHMI, a machine-learning method for solar image super-resolution, to enhance the transverse and line-of-sight magnetograms of solar active regions (ARs) collected by SDO/HMI to better capture the fine-scale magnetic structures that are crucial for understanding solar flare dynamics. The enhanced HMI magnetograms can also be used to study spicules, sunspot light bridges and magnetic outbreaks, for which high-resolution data are essential. Methods.DeepHMI employs a conditional diffusion model that is trained using ground-truth images obtained by an inversion analysis of Stokes measurements collected by GST/NIRIS. Results.Our experiments show that DeepHMI performs better than the commonly used bicubic interpolation method in terms of four evaluation metrics. In addition, we demonstrate the ability of DeepHMI through a case study of the enhancement of SDO/HMI transverse and line-of-sight magnetograms of AR 12371 to GST/NIRIS data.
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Improving spatial resolution of sunspot HMI images using conditional generative adversarial networks
Solar Dynamics Observatory (SDO) spacecraft as a space-based project is able to conduct continuous monitoring of the Sun. The Helioseismic and Magnetic Imager (HMI) instrument on SDO, in particular, provides continuum images and magnetograms with a cadence of under 1 minute. SDO/HMI's spatial resolution is only about 1'', which makes it impossible to perform a good analysis on the subarcsecond scale. On the other hand, larger aperture ground-based telescopes such as the Goode Solar Telescope (GST) at the Big Bear Solar Observatory are able to achieve a better resolution (16 times better than SDO/HMI). However, ground-based telescopes like GST have limitations in terms of observation time, which can only make observations during the day in clearsky condition. The purpose of this study is to make attempts in improving the spatial resolution of images captured by HMI beyond the diffraction limit of the telescope by employing the Conditional Generative Adversarial Networks algorithm (cGAN). The cGAN model was trained using 1800 pairs of HMI and GST sunspot images. This method successfully reconstruct HMI images with a spatial resolution close to GST images, this is supported by \raisebox{-0.5ex}\textasciitilde62\% increase in the peak signal-to-noise ratio (PSNR) value and \raisebox{-0.5ex}\textasciitilde90\% decrease in the mean squared error (MSE) value. The higher resolution sunspot images produced by this model can be useful for further Solar Physics studies.
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- Award ID(s):
- 2300341
- PAR ID:
- 10496132
- Publisher / Repository:
- American Institute of Physics Conference Series
- Date Published:
- Journal Name:
- American Institute of Physics Conference Series
- Page Range / eLocation ID:
- 040009
- Format(s):
- Medium: X
- Location:
- Ranchi, India
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1063/5.0181507
