Estimating distances from parallaxes IV: Distances to 1.33 billion stars in Gaia Data Release 2 (1804.10121v2)

Abstract: For the vast majority of stars in the second Gaia data release, reliable distances cannot be obtained by inverting the parallax. A correct inference procedure must instead be used to account for the nonlinearity of the transformation and the asymmetry of the resulting probability distribution. Here we infer distances to essentially all 1.33 billion stars with parallaxes published in the second \gaia\ data release. This is done using a weak distance prior that varies smoothly as a function of Galactic longitude and latitude according to a Galaxy model. The irreducible uncertainty in the distance estimate is characterized by the lower and upper bounds of an asymmetric confidence interval. Although more precise distances can be estimated for a subset of the stars using additional data (such as photometry), our goal is to provide purely geometric distance estimates, independent of assumptions about the physical properties of, or interstellar extinction towards, individual stars. We analyse the characteristics of the catalogue and validate it using clusters. The catalogue can be queried on the Gaia archive using ADQL at http://gea.esac.esa.int/archive/ and downloaded from http://www.mpia.de/~calj/gdr2_distances.html .

• The paper presents a catalogue estimating distances to 1.33 billion stars in Gaia DR2 using a probabilistic Bayesian method with a weak Galaxy model prior to handle parallax uncertainties.
• The methodology employs an exponentially decreasing space density prior varying across the Galaxy and successfully handles low signal-to-noise ratios and negative parallaxes through Bayesian inference.
• This distance catalogue serves as a vital resource for astronomical studies, providing geometrically derived distances, though users should consider inherent uncertainties and potential refinements with additional data.

Estimating Distances from Parallaxes in Gaia Data Release 2

The paper by Bailer-Jones et al. introduces a comprehensive catalogue that estimates distances to 1.33 billion stars using parallaxes from the second Gaia data release. The lack of reliable distance estimations strictly from parallax inversion in previous datasets highlights the necessity of a probabilistic approach that considers the nonlinear transformation and the asymmetrical nature of the resulting probability distribution. This work addresses these issues by employing a weak distance prior based on a Galaxy model, which adjusts as a function of Galactic longitude and latitude, aiming to mitigate biases while providing estimations that are independent of stellar physical properties or interstellar extinction.

Methodology Overview

The paper leverages the exponentially decreasing space density (EDSD) prior, which incorporates a length scale parameter varying according to our position in the Galaxy. This approach accounts for systematic variations in stellar distributions and prioritizes proximity under limited observational precision. The main algorithm focuses on the Bayesian inference framework, which consistently integrates both likelihood and prior to offer posterior distributions for star distances. Through this method, the authors define the point estimate distances and asymmetric confidence intervals using the mode and the highest density interval (HDI).

Numerical Insights

The authors report substantial variations in the parallax measurement quality, with a significant portion exhibiting low signal-to-noise ratios (SNRs). The inferred distance estimates and their associated confidence intervals illustrate the variability and spread of stars in the Galaxy. The catalogue embraces cases of both positive and negative parallaxes, demonstrating how the probabilistic method adapts to handle distance inference even when parallax measurements suggest contradiction by their sign. The researchers emphasize the suitability of their approach for datasets dominated by noise or systematic errors, thereby providing a potentially more robust interpretation for a vast array of stars where model-based priors govern their distances.

Implications and Future Directions

This paper's distance catalogue serves as a vital and independent resource across various astronomical studies, allowing the scientific community to employ geometric distance measures transparently. However, the inherent uncertainties in these estimates warrant careful consideration, particularly when further assumptions or pre-processed data may deliver narrower, although potentially biased, distance intervals. The catalogue facilitates consistent comparative analyses and offers a foundational framework for enhanced, model-based estimations. Future research might integrate additional spectral data and refine prior knowledge to address some limitations inherent in purely geometric analyses.

This paper thoughtfully presents its methods and results, acknowledging the complexity and exploratory nature of astronomical data, while maintaining a practical approach to analyse the Gaia DR2 dataset. It offers valuable insights and foundational methodologies that other researchers can adapt in addressing similar problems in galactic astronomy and related fields.