Non-parametric stellar LOSVD analysis
  Ill-posed inverse problems are common in astronomy, and their solutions are
unstable with respect to noise in the data. Solutions of such problems are
typically found using two classes of methods: parametrization and fitting the
data against some predefined function or a solution with a non-parametrical
function using regularization. Here we are focusing on the latter
non-parametric approach applied for the recovery of complex stellar
line-of-sight velocity distribution (LOSVD) from the observed galaxy spectra.
Development of such an approach is crucial for galaxies hosting multiple
kinematically misaligned stellar components, such as 2 stellar counter-rotating
disks, thin and thick disks, kinematically decoupled cores, and others.
  Stellar LOSVD recovery from the observed galaxy spectra is equivalent to a
deconvolution and can be solved as a linear inverse problem. To overcome its
ill-posed nature we apply smoothing regularization. Searching for an optimal
degree of smoothing regularization is a challenging part of this approach. Here
we present a non-parametric fitting technique, discuss its potential caveats,
perform numerous tests based on synthetic mock spectra, and show real-world
application to MaNGA spectral data cubes and some long-slit spectra of stellar
counter-rotating galaxies.
  GitHub repository: https://github.com/gasymovdf/sla
