Reprojects abundances of 199k stars into 4 patterns, identifying enrichment pathways with strong chemo-spatial, age, and vertical correlations plus a transition at ~6 Gyr.
L., Das P., 2018, @doi [ ] 10.1093/mnras/sty2490 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.481.4093S 481
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
verdicts
UNVERDICTED 4representative citing papers
A public catalogue provides geometric and photogeometric distances plus uncertainties for 1.47 billion Gaia EDR3 stars derived via probabilistic inference with a three-dimensional Galactic prior.
Optimized filters centered at 3920-3960 Angstrom yield metallicity estimates with 0.18-0.39 dex precision down to [Fe/H] ~ -4 for metal-poor stars from Gaia XP spectra, enabling a catalog of 14.5 million such stars.
Ages inferred for red giant stars via machine learning are generally insensitive to hyperparameters and architecture but somewhat sensitive to training set choice, especially for the oldest, coolest, and lowest-metallicity stars.
citing papers explorer
-
Estimating distances from parallaxes. V: Geometric and photogeometric distances to 1.47 billion stars in Gaia Early Data Release 3
A public catalogue provides geometric and photogeometric distances plus uncertainties for 1.47 billion Gaia EDR3 stars derived via probabilistic inference with a three-dimensional Galactic prior.