Binary Star Detection and Parameter Estimation in the RAVE Survey

TL;DR

This paper analyzes approximately 250,000 RAVE survey spectra to identify binary stars and estimate their atmospheric parameters using correlation function analysis, demonstrating the survey's potential for binary population studies.

Contribution

It introduces a method for binary star detection and parameter estimation in large spectroscopic surveys based on correlation analysis, applicable to extensive datasets without color restrictions.

Findings

Successful identification of binary stars from RAVE spectra.

Effective estimation of atmospheric parameters for binaries.

Large, unbiased sample suitable for population studies.

Abstract

Although primarily aimed at the galactic archeology and evolution, automated all-sky spectroscopic surveys (RAVE, SDSS) are also a valuable source for the binary star research community. Identification of double-lined spectra is easy and it is not limited by the rare occurrences of eclipses. When the spectrum is properly classified, its atmospheric parameters can be calculated by comparing the spectrum with the best fit atmosphere model. We present the analysis of the binary stars from the sample of roughly 250.000 RAVE survey spectra. The classification and binary discovery method is based on the correlation function analysis. The comparison of these spectra with the model shows that it is possible to estimate the essential atmospheric parameters relatively well. Large number of such estimates and the fact that RAVE consists of a magnitude selected sample without any color cuts makes it suitable for a binary star population study.