Photometric segregation of dwarf and giant FGK stars using the SVO Filter Profile Service and photometric tools

TL;DR

This study demonstrates a photometric method using narrow-band filters and machine learning to accurately distinguish between dwarf and giant FGK stars, and estimates stellar temperatures with high precision.

Contribution

It introduces a new approach combining SVO photometric tools and machine learning for star classification and temperature estimation using J-PAS filters.

Findings

Achieved ~97% accuracy in classifying FGK stars.

Developed a colour-temperature relation for dwarf stars.

Extended classification accuracy to M-type giants and dwarfs.

Abstract

This paper is focused on the segregation of FGK dwarf and giant stars through narrow-band photometric data using the Spanish Virtual Observatory (SVO) Filter Profile Service and associated photometric tools. We selected spectra from the MILES, STELIB, and ELODIE stellar libraries, and used SVO photometric tools to derive the synthetic photometry in 15 J-PAS narrow filters, which were especially selected to cover spectral features sensitive to gravity changes. Using machine-learning techniques as the Gaussian mixture model and the support vector machine, we defined several criteria based on J-PAS colours to discriminate between dwarf and giant stars. We selected five colour-colour diagrams that presented the most promising separation between both samples. Our results show an overall accuracy in the studied sample of $\sim$0.97 for FGK stars, although a dependence on the luminosity type and the stellar effective temperature was found. We also defined a colour-temperature relation for dwarf stars with effective temperatures between 4\,000 and 7\,000\,K, which allows one to estimate the stellar effective temperature from four J-PAS filters ($J0450$, $J0510$, $J0550$, and $J0620$). Additionally, we extended the study to M-type giant and dwarf stars, achieving a similar accuracy to that for FGK stars.