POSTELLAR: Posterior Stellar Spectrum Sampling - An Alternative to Approximate Stellar Spectra for Exoplanetary Analysis
We present a novel approach to perform posterior sampling of the underlying stellar spectrum in high-resolution spectroscopic observations. Our method, postellar, coherently combines information from empirical observations and physics-based models, enabling more accurate spectral recovery while providing uncertainties that can be propagated into downstream analyses. This is accomplished by treating the intrinsic stellar spectrum as a latent variable and performing posterior sampling under a Gaussian likelihood with an informative prior constructed using a score-based diffusion model trained on PHOENIX stellar models. We validate the framework on synthetic SPIRou radial velocity (RV) observations generated from the empirical spectra of Barnard's Star and Proxima Centauri. Spectra inferred with postellar more accurately recover the ground truth than standard empirical templates. For medium signal-to-noise observations, postellar improves RV accuracy by up to a factor of three, while RVs derived from empirical templates tend to be biased. This method performs particularly well in low signal-to-noise and low-cadence regimes. The postellar framework is broadly applicable to other high-resolution spectroscopy science cases including stellar abundance analyses and exoplanet atmospheric characterization.