Photometric segregation of FGK dwarf and giant stars

The term FGK star designates a star the spectrum of which makes it either an F-type star (yellow-white, a bit hotter than then Sun ), a G-type star (yellow, similar to the Sun), or a K-type star (orange, a bit cooler than the Sun). FGK stars come in a variety of sizes but segregating them is far from trivial. Actually, the segregation of FGK dwarf and giant stars may seem evident and easy after 100 yr of papers presenting different colour-magnitude, colour-colour, colour-index, or index-index discriminating diagrams but, still, is not straightforward. To contribute to the task, a new work ¹ presents new results from a compromise between filter width and gravity-dependent features, using Javalambre Physics of the Accelerating universe Astrophysical Survey (J-PAS) narrow filters and the photometric services from the Spanish Virtual Observatory (SVO).

Astronomical photometry refers to measuring the apparent brightness of an astrophysical object. A photometric system can be defined as a set of filters at different wavelengths with a well-characterised response to the incident radiation. They are typically designed to be sensitive to regions of the electromagnetic spectrum in which the variations in a given parameter (for instance, stellar effective temperature, surface gravity, and metallicity) are more prominent. Since the beginning of the 21^st century, large-area, multi-filter surveys have provided photometric information for millions of astronomical objects.

By comparing information at different wavelengths using colours, it is possible to estimate physical parameters that can be used in very diverse research projects. Of particularly interest to many astrophysical studies is the separation between dwarf and giant stars. For instance, studies of the structure, kinematics, and chemical distribution of our Galaxy, in particular the outer disc and halo, need a clean sample of red giants. Also, studies on the star formation history in the solar neighbourhood or on the occurrence rate of exoplanets as a function of the mass of the host stars require a clean sample of giants. In both cases, contamination from members of the non-desired class may lead to incorrect conclusions. Furthermore, working with contaminated samples may result in telescope time being wasted when researchers attempt, for instance, to spectroscopically characterize these objects.

How to discriminate between dwarf and giant stars

The question of how to discriminate between dwarf and giant stars has been of interest for a long time. In recent years, machine-learning techniques have joined the cause. Different algorithms have achieved reducing the contamination rates (giants classified as dwarfs) down to 6,4 %. A good figure, but not enough. One approach to reduce contamination rates is to use narrowband filters, since the features that show temperature, gravity, or metallicity dependence can be better constrained, and thus provide better physical parameter estimations.

The Filter Profile Service (FPS) is a web service built and maintained by the SVO project. It was originally conceived as a mechanism to efficiently manage the photometric information required by the VO SED Analyzer, a Virtual Observatory (VO) tool also built and maintained by the SVO project. VOSA is designed for the analysis of the spectral energy distribution (SED) of stellar objects based on the comparison of photometric observations and theoretical models. FPS is not only a repository of information but also a central resource around which other services and applications can be built.

The authors used 15 J-PAS narrow filters, especially selected to cover spectral features that are known to be sensitive to gravity, to develop a methodology of discriminating between dwarf and giant stars, limited to the F, G, and K spectral types. For this purpose, they selected a sample of known stars from the MILES, STELIB, and ELODIE stellar libraries, and used the SVO photometric tools to derive the J-PAS synthetic photometry from their spectra.

The 15 selected filters resulted in 105 J-PAS colours with a larger probability of separating both types of stars. Then, a machine-learning approach was used to identify the colour-colour pairs adequate for the dwarf-giant segregation among the total of 5.460 possible pairs generated. Using a Gaussian mixture model, the most promising five colour-colour diagrams were selected to discriminate between giant and dwarf stars. Finally, the researchers used the support vector machine technique to define a criteria that maximises the separation of the two luminosity classes, which performs excellently, with an average accuracy of approximately 0.97 for FGK stars.

The authors show the power and capabilities of FPS and associated tools in the discrimination of dwarf and giant stars using photometry data.

Author: César Tomé López is a science writer and the editor of Mapping Ignorance

Disclaimer: Parts of this article may have been copied verbatim or almost verbatim from the referenced research paper/s.