Estimation of distances to stars with stellar parameters from LAMOST

TL;DR

This paper introduces a Bayesian method to estimate stellar distances using LAMOST spectroscopic data, accounting for population biases and achieving ~20% accuracy with potential improvements.

Contribution

The authors develop a novel Bayesian approach tailored for LAMOST data that accurately estimates stellar distances without relying on biased population models.

Findings

Distances are recovered within ~20% accuracy for most stars.

Systematic overestimation of distances to halo giants identified.

Current LAMOST data allows ~40% distance measurement errors.

Abstract

We present a method to estimate distances to stars with spectroscopically derived stellar parameters. The technique is a Bayesian approach with likelihood estimated via comparison of measured parameters to a grid of stellar isochrones, and returns a posterior probability density function for each star's absolute magnitude. This technique is tailored specifically to data from the Large Sky Area Multi-object Fiber Spectroscopic Telescope (LAMOST) survey. Because LAMOST obtains roughly 3000 stellar spectra simultaneously within each ~5-degree diameter "plate" that is observed, we can use the stellar parameters of the observed stars to account for the stellar luminosity function and target selection effects. This removes biasing assumptions about the underlying populations, both due to predictions of the luminosity function from stellar evolution modeling, and from Galactic models of stellar populations along each line of sight. Using calibration data of stars with known distances and stellar parameters, we show that our method recovers distances for most stars within ~20%, but with some systematic overestimation of distances to halo giants. We apply our code to the LAMOST database, and show that the current precision of LAMOST stellar parameters permits measurements of distances with ~40% error bars. This precision should improve as the LAMOST data pipelines continue to be refined.