Search for: All records

Abstract Resolving fine structures in the Sun’s corona may provide key insights into rapid eruptions and the heating of the corona. Adaptive optics systems have been used for over two decades to reach the diffraction limit of large telescopes, thereby compensating for atmospheric image blur. Current systems, however, are still limited to observations of the solar disk and fail with coronal objects, leaving fundamental coronal dynamics hidden in that blur. Here we present observations with coronal adaptive optics reaching the diffraction limit of a 1.6-m telescope to reveal very fine coronal details. Furthermore, we discovered a short-lived, fast-moving, finely twisted feature occurring during the decay phase of a flare that quickly destabilized. Coronal adaptive optics increased the spatial resolution by an order of magnitude at visible wavelengths. We report here a large portion of off-limb coronal rain material with observed scales below 100 km. This new adaptive optics scheme opens opportunities for observational discoveries at small scales beyond the solar limb in the highly dynamic corona by exploiting the diffraction limit of large ground-based telescopes. 

The presence and nature of low-frequency (0.1–10 mHz) Alfvénic waves in the corona have been established over the past decade, with many of these results coming from coronagraphic observations of the infrared Fexiiiline. The Cryo-NIRSP instrument situated at DKIST has recently begun acquiring science-quality data of the same Fexiiiline, with at least a factor of 9 improvement in spatial resolution, a factor of 30 increase in temporal resolution, and an increase in signal-to-noise ratio, when compared to the majority of previously available data. Here we present an analysis of 1 s cadence sit-and-stare data from Cryo-NIRSP, examining the Doppler velocity fluctuations associated with the Fexiii1074 nm coronal line. We are able to confirm previous results of Alfvénic waves in the corona and explore a new frequency regime. The data reveal that the power-law behavior of the Doppler velocity power spectrum extends to higher frequencies. This result appears to challenge some models of photospheric-driven Alfvénic waves that predict a lack of high-frequency wave power in the corona owing to strong chromospheric damping. Moreover, the high-frequency waves do not transport as much energy as their low-frequency counterparts, with less time-averaged energy per frequency interval. We are also able to confirm the incompressible nature of the fluctuations with little coherence between the line amplitude and Doppler velocity time series. 

Abstract Inferring the three-dimensional (3D) solar atmospheric structures from observations is a critical task for advancing our understanding of the magnetic-fields and electric currents that drive solar activity. In this work, we introduce a novel, physics-informed machine learning method to reconstruct the 3D structure of the lower solar atmosphere based on the output of optical-depth-sampled spectropolarimetric inversions, wherein both the fully disambiguated vector magnetic fields and the geometric height associated with each optical depth are returned simultaneously. Traditional techniques typically resolve the 180° azimuthal ambiguity assuming a single layer, either ignoring the intrinsic nonplanar physical geometry of constant optical-depth surfaces (e.g., the Wilson depression in sunspots) or correcting the effect as a postprocessing step. In contrast, our approach simultaneously maps the optical depths to physical heights, and enforces the divergence-free condition for magnetic fields fully in 3D. Tests on magnetohydrodynamic simulations of quiet Sun, plage, and a sunspot demonstrate that our method reliably recovers the horizontal magnetic-field orientation in locations with appreciable magnetic field strength. By coupling the resolutions of the azimuthal ambiguity and the geometric height problems, we achieve a self-consistent reconstruction of the 3D vector magnetic fields and, by extension, the electric current density and Lorentz force. This physics-constrained, label-free training paradigm is a generalizable, physics-anchored framework that extends across solar magnetic environments while improving the understanding of various solar puzzles. 

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. 

This white paper is on the HMCS Firefly mission concept study. Firefly focuses on the global structure and dynamics of the Sun's interior, the generation of solar magnetic fields, the deciphering of the solar cycle, the conditions leading to the explosive activity, and the structure and dynamics of the corona as it drives the heliosphere.