ML regressors trained on APOGEE DR17 red giants predict C, O, Mg, Si abundances from kinematics and [Fe/H] more accurately than [Fe/H] baseline, with external validation on HARPS FGK dwarfs and reproduction of Galactic chemical evolution trends.
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3 Pith papers cite this work. Polarity classification is still indexing.
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Revised mass of 0.503 M_Earth and radius of 0.736 R_Earth for GJ 367 b give a density of 6.9 g cm^{-3} and an iron fraction of 50-70% via new tidal and composition modeling.
Analysis of SPH simulations and collision velocity models predicts that collisionally-produced super-Mercuries have higher densities at low mass and short period, identifying GJ 367b as the strongest observed candidate.
citing papers explorer
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Inferring stellar metallicity and elemental abundances from kinematic and spectroscopic data using machine learning -- Implications for exoplanet host stars
ML regressors trained on APOGEE DR17 red giants predict C, O, Mg, Si abundances from kinematics and [Fe/H] more accurately than [Fe/H] baseline, with external validation on HARPS FGK dwarfs and reproduction of Galactic chemical evolution trends.
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Revisiting the Exo-Mercury Candidate GJ 367 b with ESPRESSO and a Self-Consistent Tidal Distortion Model
Revised mass of 0.503 M_Earth and radius of 0.736 R_Earth for GJ 367 b give a density of 6.9 g cm^{-3} and an iron fraction of 50-70% via new tidal and composition modeling.
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The Maximum Density of a Collisionally-Produced Planet is A Function of its Mass and Orbital Period
Analysis of SPH simulations and collision velocity models predicts that collisionally-produced super-Mercuries have higher densities at low mass and short period, identifying GJ 367b as the strongest observed candidate.