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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 $\log \tau =0.0$and $\log \tau =-1$. 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. 

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. 

Abstract Delta (δ) sunspots sometimes host fast photospheric flows along the central magnetic polarity inversion line (PIL). Here we study the strong Doppler shift signature in the central penumbral light bridge of solar active region NOAA 12673. Observations from the Helioseismic and Magnetic Imager (HMI) indicate highly sheared and strong magnetic fields. Large Doppler shifts up to 3.2 km s⁻¹appeared during the formation of the light bridge and persisted for about 16 hr. A new velocity estimator, called DAVE4VMwDV, reveals fast converging and shearing motion along the PIL from HMI vector magnetograms, and recovers the observed Doppler signal much better than an old version of the algorithm. The inferred velocity vectors are largely (anti-)parallel to the inclined magnetic fields, suggesting that the observed Doppler shift contains a significant contribution from the projected field-aligned flows. High-resolution observations from the Hinode/Spectro-Polarimeter further exhibit a clear correlation between the Doppler velocity and the cosine of the magnetic inclination, which is in agreement with HMI results and consistent with a field-aligned flow of about 9.6 km s⁻¹. The complex Stokes profiles suggest significant gradients of physical variables along the line of sight. We discuss the implications on theδ-spot magnetic structure and the flow-driving mechanism.