Correction to the photometric magnitudes of the Gaia Early Data Release 3

TL;DR

This study validates Gaia EDR3 photometry using standard stars and machine learning, confirming calibration improvements and identifying modest magnitude-dependent trends for precise astronomical applications.

Contribution

It introduces an independent validation method of Gaia EDR3 photometry using machine learning and standard stars, providing correction insights for high-precision use.

Findings

Calibration improvements in Gaia EDR3 confirmed

Modest magnitude-dependent trends identified

Absolute corrections derived for high-accuracy applications

Abstract

In this letter, we have carried out an independent validation of the Gaia EDR3 photometry using about 10,000 Landolt standard stars from Clem & Landolt (2013). Using a machine learning technique, the UBVRI magnitudes are converted into the Gaia magnitudes and colors and then compared to those in the EDR3, with the effect of metallicity incorporated. Our result confirms the significant improvements in the calibration process of the Gaia EDR3. Yet modest trends up to 10 mmag with G magnitude are found for all the magnitudes and colors for the 10 < G < 19 mag range, particularly for the bright and faint ends. With the aid of synthetic magnitudes computed on the CALSPEC spectra with the Gaia EDR3 passbands, absolute corrections are further obtained, paving the way for optimal usage of the Gaia EDR3 photometry in high accuracy investigations.