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

TL;DR

This paper introduces a machine learning-based method to accurately identify and constrain multiple spectral components in chromospheric spectral lines, improving velocity measurements in solar observations.

Contribution

The novel approach combines machine learning with spectral fitting to isolate and analyze multiple spectral components in chromospheric data, enhancing velocity diagnostics.

Findings

Successfully identified multiple spectral components in sunspot chromospheres.

Achieved median reduced χ² value of 1.03 in spectral profile fitting.

Demonstrated the method's effectiveness on Ca II 8542 Å spectral data.

Abstract

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 II 8542 {\AA} 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. Minimisation tests are employed to validate the reliability of the results, achieving median reduced $\chi^2$ values equal to 1.03 between the observed and synthesised umbral line profiles.