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.

High-frequency torsional Alfvén waves as an energy source for coronal heating.

The existence of the Sun's hot atmosphere and the solar wind acceleration continues to be an outstanding problem in solar-astrophysics. Although magnetohydrodynamic (MHD) modes and dissipation of magnetic energy contribute to heating and the mass cycle of the solar atmosphere, yet direct evidence of such processes often generates debate. Ground-based 1-m Swedish Solar Telescope (SST)/CRISP, Hα 6562.

Zernike basis optimization for solar adaptive optics by using information theory.

Karhunen-Loève functions represent the best choice for modal wavefront reconstruction. They are usually built up as a linear combination of Zernike polynomials by using principal component analysis methods; thus they are ordered by covariance. Using Shannon information theory, we provide a best reordering procedure based on the concept of mutual information.