Gaia Data Release 3 (DR3) has substantially expanded the study of unresolved binaries by providing combined astrometric and spectroscopic orbital solutions for a large sample of non-single stars. We present a Bayesian forward-modelling framework for estimating companion masses in Gaia DR3 \texttt{AstroSpectroSB1} systems. The method samples the physical parameters of each binary, predicts the corresponding astrometric Thiele-Innes constants and spectroscopic orbital coefficients, and evaluates their likelihood using the full covariance matrix published by Gaia. An external prior on the primary mass is incorporated to resolve the remaining degeneracy between the component masses and orbital inclination in single-lined systems. We apply this framework to 1035 \texttt{AstroSpectroSB1} systems with primary-mass estimates drawn from the Gaia DR3 \texttt{astrophysical\_parameters} table. Of these, 760 systems also have Gaia-reported estimates for both component masses and are used as a validation sample. The remaining 275 systems constitute an inference sample for which the mass of the fainter, unresolved companion is estimated. For systems with well converged posteriors, the inferred parameters reproduce the Gaia orbital observables, while the companion masses recovered for the validation sample are consistent with the Gaia estimates. In the inference sample we find 113 well converged systems for which the companion masses are meaningfully inferred. We find that the population is dominated by ordinary stars, together with a smaller high-mass tail that may contain massive white dwarfs or other compact-object candidates. Our framework provides a scalable approach for converting Gaia AstroSpectroSB1 solutions into companion-mass posterior distributions, identifying promising systems for targeted follow-up, and preparing for the larger and more precise orbital samples anticipated with Gaia DR4.
With microarcsecond precision, \textit{Gaia} has made the astrometric discovery of substellar companions feasible. However, follow-up observations are needed to check on orbital solutions and rule out astrophysical false positives. We validate and characterise a sample of 20 \textit{Gaia} Data Release 3 (DR3) astrometr...
M. Marcussen, S. Albrecht, Kamil K. Kalinowski et al.· 1 citation
Stellar multiplicity at different orbital periods is probed by different techniques: radial velocities at the shortest periods, direct imaging at the longest, and astrometry in between. Gaia DR3 provides unprecedented astrometric information to constrain binary populations with periods of tens to thousands of days. Bey...
B. Pennell, H. Rix, K. El-Badry et al.· 0 citations
Astrometry from the Gaia mission has revealed a large and homogeneous population of binaries containing compact objects, including more than 3000 white dwarf--main sequence (WDMS) binaries with orbital periods of $P_{\rm orb}\sim100$--$1000$ d. Most of these binaries likely underwent mass transfer from asymptotic giant...
Natsuko Yamaguchi, K. El-Badry, R. Essick et al.· 1 citation
We present new JWST/NIRSpec IFU high contrast spectroscopic observations of the brown dwarf companion GJ 758 B. Extensive radial velocity monitoring of the primary has enabled high-precision measurements of the companion mass through characterization of their joint orbital dynamics. The combination of prior space- and...
Alexander Madurowicz, E. Rickman, Daniella C. Bardalez Gagliuffi et al.· 0 citations
We compiled a sample of 47 single-lined spectroscopic binaries (SB1s) with available relative astrometric measurements. By combining radial velocities, relative astrometry, and absolute astrometry when available and reliable, we determine the component masses and orbital solutions of these SB1s. The orbital periods spa...
Accurate stellar parameters underpin much of astrophysics, from exoplanetary systems to Galactic archaeology. For isolated field stars, masses and especially ages are usually inferred from stellar evolution models, yet these inferences depend on the chosen model grid, and the systematic differences between grids can ri...
J. Vines, A. Ware· 0 citations
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