Parameters for > 300 million Gaia stars: Bayesian inference vs. machine learning

no code implementations • 14 Feb 2023 • F. Anders, A. Khalatyan, A. B. A. Queiroz, S. Nepal, C. Chiappini

The Gaia Data Release 3 (DR3), published in June 2022, delivers a diverse set of astrometric, photometric, and spectroscopic measurements for more than a billion stars.

Bayesian Inference

The RAdial Velocity Experiment: Parameterization of RAVE spectra based on Convolutional Neural Network

no code implementations • 27 Apr 2020 • G. Guiglion, G. Matijevic, A. B. A. Queiroz, M. Valentini, M. Steinmetz, C. Chiappini, E. K. Grebel, P. J. McMillan, G. Kordopatis, A. Kunder, T. Zwitter, A. Khalatyan, F. Anders, H. Enke, I. Minchev, G. Monari, R. F. G. Wyse, O. Bienayme, J. Bland-Hawthorn, B. K. Gibson, J. F. Navarro, Q. Parker, W. Reid, G. M. Seabroke, A. Siebert

We trained a CNN, adopting stellar atmospheric parameters and chemical abundances from APOGEE DR16 (resolution R~22500) data as training set labels.

Astrophysics of Galaxies Instrumentation and Methods for Astrophysics Solar and Stellar Astrophysics