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

Conor D. MacBride*, David B. Jess, Samuel D. T. Grant, Elena Khomenko, Peter H. Keys, Marco Stangalini

*Corresponding author for this work

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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 Å 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 Χ2 values equal to 1.03 between the observed and synthesised umbral line profiles.
Original languageEnglish
JournalPhilosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
Volume379
Issue number2190
Early online date21 Dec 2020
DOIs
Publication statusEarly online date - 21 Dec 2020

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