Stellar distances from spectroscopic observations: a new technique

TL;DR

This paper introduces a Bayesian method for accurately estimating stellar distances using combined photometric and spectroscopic data, improving precision by leveraging all available information and also deriving stellar properties.

Contribution

The paper presents a novel Bayesian technique that simultaneously estimates stellar distances, metallicities, ages, and masses, outperforming previous methods in precision and comprehensiveness.

Findings

Uncertainty in metallicity is reduced by 44% compared to input errors.

Method performs well on both simulated and real survey data.

Provides comprehensive stellar parameters with improved accuracy.

Abstract

A Bayesian approach to the determination of stellar distances from photometric and spectroscopic data is presented and tested both on pseudodata, designed to mimic data for stars observed by the RAVE survey, and on the real stars from the Geneva-Copenhagen survey. It is argued that this method is optimal in the sense that it brings to bear all available information and that its results are limited only by observational errors and the underlying physics of stars. The method simultaneously returns the metallicities, ages and masses of programme stars. Remarkably, the uncertainty in the output metallicity is typically 44 per cent smaller than the uncertainty in the input metallicity.