Identification of metal-poor stars using the artificial neural network

2013/07/05 by Sunetra Giridhar, Aruna Goswami, Andrea Kunder +2
Engineering · Mathematics · Physics and Astronomy · #Artificial intelligence #Artificial neural network #Astronomical Observations and Instrumentation #Astronomy and Astrophysical Research #Astrophysics #Biology #Computer science #Context (archaeology) #Ecology #Field (mathematics) #Galaxy #Geology #Identification (biology) #Mathematics #Physics #Stars #Stellar, planetary, and galactic studies #astro-ph.SR

paper · pdf · doi:10.1051/0004-6361/201219918

Accepted for publication in Astronomy and Astrophysics, 15 pages, 7 figures

openalex publication_date 2013/07/05 · arxiv created 2013/07/24 · arxiv updated 2015/06/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05

Abstract

Context. Identification of metal-poor stars among field stars is extremely useful for studying the structure and evolution of the Galaxy and of external galaxies.

Citations

• The atmospheric parameters and spectral interpolator for the MILES stars
• New release of the ELODIE library: Version 3.1
• New release of the ELODIE library
• A standard stellar spectral sequence in the red/near-infrared - Classes K5 to M9