High-spectral Resolution, Multi-wavelength Center-to-limb Observations of the Sun
Meetu Verma, Carsten Denker, Alexander G. M. Pietrow, Robert Kamlah, Dominique J. M. Petit dit de la Roche
TL;DR
The study addresses how center-to-limb variation (CLV) in photospheric and chromospheric lines encodes vertical solar-atmosphere structure, requiring high-resolution, multi-wavelength data. It employs the Vacuum Tower Telescope (VTT) with the FaMuLUS spectrograph to perform drift-scan spectroscopy across four spectral regions simultaneously, achieving a high resolving power of $R \approx 5.9\times 10^5$ at Hα and collecting around $8\times 10^5$ spectra per scan to generate spectroheliograms and line-parameter maps. Early Hα results confirm CLV trends, with seven-position spectroheliograms illustrating height-dependent morphology and quiet-Sun CLV contrasted against a reference atlas. The multi-wavelength, high-resolution dataset enables cross-line CLV analyses across formation heights and paves the way for spectral inversions and denoising, with implications for stellar activity modeling and exoplanet atmosphere studies.
Abstract
The center-to-limb variations (CLVs) of photospheric and chromospheric spectral lines were obtained in 2025 July and August using drift scans from the echelle spectrograph of the 0.7 m Vacuum Tower Telescope at the Observatorio del Teide (ODT) in Tenerife, Spain. This instrument can observe four spectral regions simultaneously, enabling multi-line spectroscopy with high spectral resolution of various activity features and the quiet Sun in the lower solar atmosphere. The initial results of Halpha observations demonstrate the diagnostic potential of drift scans obtained with a ground-based, high-resolution telescope. Data products include spectroheliograms and maps of physical parameters such as line-of-sight velocity, line width, and line-core intensity. The combination of the CLV from photospheric and chromospheric lines, as well as the wide range of formation heights of the selected lines, renders this dataset ideal for characterizing stellar and exoplanet atmospheres.
