Accurately constraining velocity information from spectral imaging observations using machine learning techniques

Determining accurate plasma Doppler (line-of-sight) velocities from spectroscopic measurements is a challenging endeavour, especially when weak chromospheric absorption lines are often rapidly evolving and, hence, contain multiple spectral components in their constituent line profiles. Here, we present a novel method that employs machine learning techniques to identify the underlying components present within observed spectral lines, before subsequently constraining the constituent profiles through single or multiple Voigt fits. Our method allows active and quiescent components present in spectra to be identified and isolated for subsequent study. Lastly, we employ a Ca ?? 8542 angstrom spectral imaging dataset as a proof-of-concept study to benchmark the suitability of our code for extracting two-component atmospheric profiles that are commonly present in sunspot chromospheres. Minimization tests are employed to validate the reliability of the results, achieving median reduced chi (2)-values equal to 1.03 between the observed and synthesized umbral line profiles. This article is part of the Theo Murphy meeting issue 'High-resolution wave dynamics in the lower solar atmosphere'.

Automated summary

A novel method using machine learning techniques is presented to identify and isolate the underlying components present in observed spectral lines, constraining their profiles through Voigt fits. The study validates the code's suitability for extracting two-component atmospheric profiles in sunspot chromospheres, achieving reliable results with median reduced chi (2) values equal to 1.03.