Comparison of échelle spectra to synthetic models has become a computational statistics challenge, with over 10,000 individual spectral lines affecting a typical cool star échelle spectrum. Telluric artifacts, imperfect line lists, inexact continuum placement, and inflexible models frustrate the scientific promise of these informationrich data sets. Here we debut an interpretable machinelearning framework
 Award ID(s):
 1707419
 Publication Date:
 NSFPAR ID:
 10111444
 Journal Name:
 International Conference on Atomic Processes in Plasmas
 Sponsoring Org:
 National Science Foundation
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