Аннотация
Measurements of radial velocity variations from the spectroscopic monitoring
of stars and their companions are essential for a broad swath of astrophysics,
providing access to the fundamental physical properties that dictate all phases
of stellar evolution and facilitating the quantitative study of planetary
systems. The conversion of those measurements into both constraints on the
orbital architecture and individual component spectra can be a serious
challenge, however, especially for extreme flux ratio systems and observations
with relatively low sensitivity. Gaussian processes define sampling
distributions of flexible, continuous functions that are well-motivated for
modeling stellar spectra, enabling proficient search for companion lines in
time-series spectra. We introduce a new technique for spectral disentangling,
where the posterior distributions of the orbital parameters and intrinsic,
rest-frame stellar spectra are explored simultaneously without needing to
invoke cross-correlation templates. To demonstrate its potential, this
technique is deployed on red-optical time-series spectra of the mid-M dwarf
binary LP661-13. We report orbital parameters with improved precision compared
to traditional radial velocity analysis and successfully reconstruct the
primary and secondary spectra. We discuss potential applications for other
stellar and exoplanet radial velocity techniques and extensions to
time-variable spectra. The code used in this analysis is freely available as an
open source Python package.
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