arXiv subjects

A. Vicente Arévalo

Publications and source records attributed to A. Vicente Arévalo.

3 recordsLinked to original sources

arXiv2026

Synthesizing Sun-as-a-star flare spectra from high-resolution solar observations

Spatially resolved observations of the Sun and the astronomical sample size of stellar bodies are the respective key strengths of solar and stellar observations. However, the large difference in object brightness between the Sun and other stars has led to distinctly different instrumentation and methodologies between the two fields. We produce and analyze synthetic full-disk spectra derived from 19 small area field-of-view optical observations of solar flares acquired by the Swedish 1-m Solar Telescope (SST) between 2011 and 2024. These are used to investigate what can and cannot be inferred about physical processes on the Sun from Sun-as-a-star observations. The recently released Numerical Empirical Sun-as-a-Star Integrator (NESSI) code provides synthetic full-disk integrated spectral line emission based on smaller field-of-view input, accounting for center-to-limb variations and differential rotation. We use this code to generate pseudo-Sun-as-a-star spectra from the SST observations. ...

astro-ph.SR↗

arXiv2026

Accelerating 3D Non-LTE Synthesis with Graph Neural Networks

Spectropolarimetric interpretation of chromospheric lines requires solving the radiative transfer problem under non-local thermodynamic equilibrium (non-LTE) conditions. This means computing atomic-level populations self-consistently with the radiation field. While traditional inversion codes employ 1.5D approximations, they neglect horizontal radiative transfer, which can be significant near magnetic structures and in the chromosphere. We present a method to solve 3D atomic-level populations using Graph Neural Networks (GNNs), extending prior 1.5D work to the full 3D domain. By discretizing the solar atmosphere as a directed graph, in which nodes encode physical properties and edges encode spatial distances, an Encode-Process-Decode GNN propagates information to efficiently capture radiative coupling. The network is trained on a Bifrost simulation using Ca II populations from Multi3D as ground truth. The trained GNN accurately predicts populations of the five-level Ca II atom plus continuum. Correlations exceed 0.99 in the photosphere and mid-chromosphere; errors in the upper chromosphere remain unbiased. Inference is $\sim 10^6$ times faster than traditional iterative solvers. Spectral synthesis of the Ca II 8542 Å line yields intensity profiles with $< 2 \%$ mean residuals relative to the full 3D solution. This framework bypasses the computational bottleneck of iterative solvers while preserving essential non-LTE physics, including horizontal transfer, paving the way toward routine 3D non-LTE inversions.

astro-ph.SR↗

arXiv2022

Accelerating non-LTE synthesis and inversions with graph networks

The computational cost of fast non-LTE synthesis is one of the challenges that limits the development of 2D and 3D inversion codes. It also makes the interpretation of observations of lines formed in the chromosphere and transition region a slow and computationally costly process, which limits the inference of the physical properties on rather small fields of view. Having access to a fast way of computing the deviation from the LTE regime through the departure coefficients could largely alleviate this problem. We propose to build and train a graph network that quickly predicts the atomic level populations without solving the non-LTE problem. We find an optimal architecture for the graph network for predicting the departure coefficients of the levels of an atom from the physical conditions of a model atmosphere. A suitable dataset with a representative sample of potential model atmospheres is used for training. This dataset has been computed using existing non-LTE synthesis codes. The graph network has been integrated into existing synthesis and inversion codes for the particular case of \caii. We demonstrate orders of magnitude gain in computing speed. We analyze the generalization capabilities of the graph network and demonstrate that it produces good predicted departure coefficients for unseen models. We implement this approach in \hazel\ and show how the inversions nicely compare with those obtained with standard non-LTE inversion codes. Our approximate method opens up the possibility of extracting physical information from the chromosphere on large fields-of-view with time evolution.

astro-ph.SR↗