Nonparametric stellar LOSVD analysis
Abstract
Illposed 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 nonparametrical function using regularization. Here we are focusing on the latter nonparametric approach applied for the recovery of complex stellar lineofsight 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 counterrotating 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 illposed nature we apply smoothing regularization. Searching for an optimal degree of smoothing regularization is a challenging part of this approach. Here we present a nonparametric fitting technique, discuss its potential caveats, perform numerous tests based on synthetic mock spectra, and show realworld application to MaNGA spectral data cubes and some longslit spectra of stellar counterrotating galaxies. GitHub repository: https://github.com/gasymovdf/sla
 Publication:

arXiv eprints
 Pub Date:
 December 2021
 DOI:
 10.48550/arXiv.2112.08386
 arXiv:
 arXiv:2112.08386
 Bibcode:
 2021arXiv211208386G
 Keywords:

 Astrophysics  Instrumentation and Methods for Astrophysics;
 Astrophysics  Astrophysics of Galaxies
 EPrint:
 4 pages, 3 figure