Identification of field dwarfs and giants in RAVE

TL;DR

This paper presents a simple, magnitude-based method to distinguish dwarfs from giants in RAVE data, validated against spectroscopic log g values and statistical models, improving classification accuracy without spectroscopic analysis.

Contribution

It introduces a novel, spectroscopic-independent approach for identifying stellar types in RAVE, validated against existing data and models, enhancing classification efficiency.

Findings

The magnitude-based method effectively estimates dwarf and giant populations.

The approach is unbiased by CHISQ or signal-to-noise ratio effects.

Giants constitute 34% of the entire sample, rising to 59% when including subgiants.

Abstract

The second RAdial Velocity Experiment (RAVE) Data Release (DR2) derives log g values but we present a simpler and cleaner method of identifying dwarfs and giants using only magnitudes, which does not require spectroscopic analysis. We confirm the Bilir et al. (2006) procedure which estimates the number of dwarfs and giants via their positions in the J-V two magnitude diagram by applying it to RAVE DR2. It is effective in estimating the number of dwarfs and giants at J-H>0.4 compared to RAVE's log g values. For J-H<=0.4, where dwarfs and subgiants show a continuous transition in the J magnitude histogram, we used the Besancon Galaxy model predictions to statistically isolate giants. The percentages of giants for red stars and for the whole sample are 85% and 34%, respectively. If we add the subgiants, the percentage of evolved stars for the whole sample raises to 59%. For the first time in the literature, we analysed the effect of CHISQ on RAVE's log g values (CHISQ is the penalised chi^2 from RAVE's technique of finding an optimal match between the observed spectrum and synthetic spectra to derive stellar parameters). Neither the CHISQ values nor the signal-to-noise ratio bias RAVE log g values. Therefore the method of identifying dwarfs and giants via the two magnitude diagram has been verified against an unbiased dataset.