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Abstract Quiet-Sun regions cover most of the Sun's surface; their magnetic fields contribute significantly to solar chromospheric and coronal heating. However, characterizing the magnetic fields of the quiet Sun is challenging due to their weak polarization signal. The 4 m Daniel K. Inouye Solar Telescope (DKIST) is expected to improve our understanding of quiet-Sun magnetism. In this paper, we assess the diagnostic capability of the Diffraction Limited Near Infrared Spectropolarimeter (DL-NIRSP) instrument on DKIST for the energy transport processes in the quiet-Sun photosphere. To this end, we synthesize high-resolution, high-cadence Stokes profiles of the Fei630 nm lines using a realistic magnetohydrodynamic simulation, degrade them to emulate the DKIST/DL-NIRSP observations, and subsequently infer the vector magnetic and velocity fields. For the assessment, we first verify that a widely used flow tracking algorithm, the Differential Affine Velocity Estimator for Vector Magnetograms, works well for estimating the large-scale (>200 km) photospheric velocity fields with these high-resolution data. We then examine how the accuracy of the inferred velocity depends on the temporal resolution. Finally, we investigate the reliability of the Poynting flux estimate and its dependence on the model assumptions. The results suggest that the unsigned Poynting flux, estimated with existing schemes, can account for about 71.4% and 52.6% of the reference ground truth at and . However, the net Poynting flux tends to be significantly underestimated. The error mainly arises from the underestimated contribution of the horizontal motion. We discuss the implications for DKIST observations.
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Spectropolarimetric Inversion in Four Dimensions with Deep Learning (SPIn4D). I. Overview, Magnetohydrodynamic Modeling, and Stokes Profile Synthesis
Abstract The National Science Foundation’s Daniel K. Inouye Solar Telescope (DKIST) will provide high-resolution, multiline spectropolarimetric observations that are poised to revolutionize our understanding of the Sun. Given the massive data volume, novel inference techniques are required to unlock its full potential. Here, we provide an overview of our “SPIn4D” project, which aims to develop deep convolutional neural networks (CNNs) for estimating the physical properties of the solar photosphere from DKIST spectropolarimetric observations. We describe the magnetohydrodynamic (MHD) modeling and the Stokes profile synthesis pipeline that produce the simulated output and input data, respectively. These data will be used to train a set of CNNs that can rapidly infer the four-dimensional MHD state vectors by exploiting the spatiotemporally coherent patterns in the Stokes profile time series. Specifically, our radiative MHD model simulates the small-scale dynamo actions that are prevalent in quiet-Sun and plage regions. Six cases with different mean magnetic fields have been explored; each case covers six solar-hours, totaling 109 TB in data volume. The simulation domain covers at least 25 × 25 × 8 Mm, with 16 × 16 × 12 km spatial resolution, extending from the upper convection zone up to the temperature minimum region. The outputs are stored at a 40 s cadence. We forward model the Stokes profile of two sets of Feilines at 630 and 1565 nm, which will be simultaneously observed by DKIST and can better constrain the parameter variations along the line of sight. The MHD model output and the synthetic Stokes profiles are publicly available, with 13.7 TB in the initial release.
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- PAR ID:
- 10556722
- Publisher / Repository:
- DOI PREFIX: 10.3847
- Date Published:
- Journal Name:
- The Astrophysical Journal
- Volume:
- 976
- Issue:
- 2
- ISSN:
- 0004-637X
- Format(s):
- Medium: X Size: Article No. 204
- Size(s):
- Article No. 204
- Sponsoring Org:
- National Science Foundation
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