Spectro-photometric distances to stars: a general-purpose Bayesian approach
Bas\'ilio X. Santiago, Doroth\'ee E. Brauer, Friedrich Anders,, Cristina Chiappini, Anna B. Queiroz, L\'eo Girardi, Helio J. Rocha-Pinto,, Eduardo Balbinot, Luiz N. da Costa, Marcio A.G. Maia, Mathias Schultheis,, Matthias Steinmetz, Andrea Miglio, Josefina Montalb\'an

TL;DR
This paper presents a Bayesian method for estimating stellar distances using spectroscopic and photometric data, validated against various independent measurements, achieving high accuracy with minimal biases.
Contribution
Introduces a general-purpose Bayesian code for stellar distance estimation that outperforms previous methods in accuracy and bias reduction.
Findings
Successfully recovers mock star distances with <1% bias
Achieves ~18% random distance scatter for dwarfs
Systematic biases are minimal and well-characterized
Abstract
We developed a code that estimates distances to stars using measured spectroscopic and photometric quantities. We employ a Bayesian approach to build the probability distribution function over stellar evolutionary models given these data, delivering estimates of model parameters for each star individually. The code was first tested on simulations, successfully recovering input distances to mock stars with <1% bias.The method-intrinsic random distance uncertainties for typical spectroscopic survey measurements amount to around 10% for dwarf stars and 20\% for giants, and are most sensitive to the quality of measurements. The code was validated by comparing our distance estimates to parallax measurements from the Hipparcos mission for nearby stars (< 300 pc), to asteroseismic distances of CoRoT red giant stars, and to known distances of well-studied open and globular clusters.…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
