http://arxiv.org/abs/1501.05500
We have developed a procedure that estimates distances to stars using measured spectroscopic and photometric quantities. It employs a Bayesian approach to build the probability distribution function over stellar evolutionary models given the data, delivering estimates of expected distance for each star individually. Our method provides several alternative distance estimates for each star in the output, along with their associated uncertainties. The code was first tested on simulations, successfully recovering input distances to mock stars with errors that scale with the uncertainties in the adopted spectro-photometric parameters, as expected. The code was then validated by comparing our distance estimates to parallax measurements from the Hipparcos mission for nearby stars (< 60 pc), to asteroseismic distances of CoRoT red giant stars, and to known distances of well-studied open and globular clusters. The photometric data of these reference samples cover both the optical and near infra-red wavelengths. The spectroscopic parameters are also based on spectra taken at various wavelengths, with varying spectral coverage and resolution: the Radial Velocity Experiment, the Sloan Digital Sky Survey programs SEGUE and APOGEE, and the ESO HARPS instrument. For Hipparcos and CoRoT samples, the typical random distance scatter is 20% or less, both for the nearby and farther data. There is a trend towards underestimating the distances by < 10%. The comparison to star clusters from SEGUE and APOGEE has led to systematic differences < 5% for most cluster stars although with significant scatter. Finally, we tested our distances against those previously determined for a high quality sample of giant stars from the RAVE survey, again finding a reasonable agreement, with only a small systematic trend. Efforts are underway to provide our code to the community by running it on a public server.
B. Santiago, D. Brauer, F. Anders, et. al.
Fri, 23 Jan 15
56/65
Comments: 20 pages, 11 figures
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