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Area of Science:

  • Solar physics
  • Plasma spectroscopy
  • Machine learning applications

Background:

  • Accurate measurement of plasma Doppler velocities from spectroscopic data is difficult.
  • Weak chromospheric absorption lines often exhibit rapid evolution and multiple spectral components.

Purpose of the Study:

  • To develop a novel machine learning method for identifying spectral line components.
  • To enable accurate isolation of active and quiescent components for further study.
  • To benchmark the method's suitability for analyzing sunspot chromospheres.

Main Methods:

  • Utilizing machine learning to identify underlying spectral line components.
  • Constraining spectral profiles using single or multiple Voigt fits.
  • Applying the method to a Ca II 8542 Å spectral imaging dataset of sunspot chromospheres.

Main Results:

  • Successfully identified and isolated multiple spectral components within observed lines.
  • Demonstrated the method's capability in extracting two-component atmospheric profiles.
  • Achieved a median reduced chi-squared value of 1.03, validating results against observed umbral line profiles.

Conclusions:

  • The novel machine learning approach effectively determines plasma Doppler velocities.
  • The method provides a reliable tool for analyzing complex spectral line profiles in the solar atmosphere.
  • This technique enhances the study of high-resolution wave dynamics in the lower solar atmosphere.