Machine Learning Regression of extinction in the second $Gaia$ Data Release

TL;DR

This paper applies supervised machine learning to estimate stellar extinction in the Gaia DR2, achieving high accuracy and covering 133 million stars, highlighting the advantages of spectrum-based over photometry-based methods.

Contribution

The study introduces a machine learning approach for extinction regression in Gaia DR2, utilizing a combined dataset and demonstrating improved accuracy over previous methods.

Findings

Extinction estimates have a standard deviation of 0.0127 mag in cross-validation.

The model predicts extinction for 133 million stars, with 106 million having uncertainties below 0.1 mag.

Spectrum-based methods show higher reliability than photometry-based methods.

Abstract

Machine learning has become a popular tool to help us make better decisions and predictions, based on experiences, observations and analysing patterns within a given data set without explicitly functions. In this paper, we describe an application of the supervised machine-learning algorithm to the extinction regression for the second Gaia data release, based on the combination of Large Sky Area Multi-Object Fiber Spectroscopic Telescope, Sloan Extension for Galactic Understanding and Exploration and the Apache Point Observatory Galactic Evolution Experiment. The derived extinction in our training sample is consistent with other spectrum-based estimates, and its standard deviation of the cross validations is 0.0127 mag. A blind test is carried out using the RAdial Velocity Experiment catalog, and the standard deviation is 0.0372 mag. Such precise training sample enable us to regress the extinction, E(BP-RP), for 133 million stars in the second Gaia data release. Of these, 106 million stars have the uncertainties less than 0.1 mag, which suffer less bias from the external regression. We also find that there are high deviations between the extinctions form photometry-based methods, and between spectrum- and photometry-based methods. This implies that spectrum-based method could bring more signal to a regressing model than multi-band photometry, and a higher signal-to-noise ratio would acquire a more reliable result.