A single end-to-end Transformer model unifies stellar labels from heterogeneous spectroscopic surveys into a self-consistent scale without post-hoc recalibration.
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A homogeneous catalogue of age, [Fe/H], heliocentric distance and E(G_BP-G_RP) for 5056 open clusters is produced from uniform Bayesian nested sampling of Gaia DR3 CMDs against PARSEC isochrones with physically motivated priors.
citing papers explorer
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Homogeneous Stellar Parameters from Heterogeneous Spectra with Deep Learning
A single end-to-end Transformer model unifies stellar labels from heterogeneous spectroscopic surveys into a self-consistent scale without post-hoc recalibration.
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Astrophysical Parameters of 5056 Open Star Clusters from Bayesian Nested Sampling with PARSEC Isochrones
A homogeneous catalogue of age, [Fe/H], heliocentric distance and E(G_BP-G_RP) for 5056 open clusters is produced from uniform Bayesian nested sampling of Gaia DR3 CMDs against PARSEC isochrones with physically motivated priors.