arXiv subjects

S. Curt Dodds

Publications and source records attributed to S. Curt Dodds.

3 recordsLinked to original sources

arXiv2026

3DSTokesFlow: simulation-based inference for 3D Stokes profiles using flow matching

The standard interpretation of observed Stokes profiles to infer the physical conditions of the solar atmosphere is inherently an ill-defined problem due to observational noise and mathematical degeneracies. Traditional pixel-by-pixel (1D) inversion codes provide point estimates with unreliable uncertainties, at the expense of significant computational time. Recent machine-learning-based Bayesian frameworks are restricted to 1D spatial configurations, ignoring crucial spatial correlations between neighboring pixels. We aim to develop a novel multidimensional inversion framework capable of performing fast and scalable Bayesian inference across an entire 2D field-of-view (FoV). This approach seeks to provide accurate height-dependent atmospheric parameters with reliable posterior distributions while exploiting spatial correlations. We introduce a new generative modeling strategy based on conditional flow matching. The model utilizes multi-scale spatial features extracted from observed Stokes profiles in the Fe I line pair at 630 nm, which then conditions a flow matching generative model to sample from the complex posterior distribution of the atmospheric parameters. The framework is trained using realistic 3D quiet Sun magnetohydrodynamic simulations. Validation on independent synthetic datasets demonstrates that the model accurately captures the true 3D stratification of all thermodynamic and magnetic parameters. Because the code additionally provides a geometrical height scale, it allows for the computation of 3D electric current density maps, Lorentz forces, and Ohmic and ambipolar dissipation maps in the solar photosphere. Application to real Hinode/SP quiet Sun observations yields highly localized electric currents at magnetic boundaries. We also leverage the 3D geometrical information to trace the emergence of small-scale emerging magnetic loops across the solar atmosphere.

astro-ph.SR↗

arXiv2025

Spectropolarimetric Inversion in Four Dimensions with Deep Learning (SPIn4D): II. A Physics-Informed Machine Learning Method for 3D Solar Photosphere Reconstruction

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-degree azimuthal ambiguity assuming a single layer, either ignoring the intrinsic non-planar physical geometry of constant optical-depth surfaces (e.g., the Wilson depression in sunspots), or correcting the effect as a post-processing 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 heights 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.

astro-ph.SR↗

arXiv2025

Spectropolarimetric Inversion in Four Dimensions with Deep Learning (SPIn4D): I. Overview, Magnetohydrodynamic Modeling, and Stokes Profile Synthesis

The National Science Foundation's Daniel K. Inouye Solar Telescope (DKIST) will provide high-resolution, multi-line 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 conducted; each case covers six solar-hours, totaling 109 TB in data volume. The simulation domain covers at least $25\times25\times8$ Mm with $16\times16\times12$ 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 Fe I lines 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.

astro-ph.SR↗