Gaia EDR3 source code

Several Gaia EDR3 release papers are accompanied by source code which can be used for the analysis of the Gaia EDR3 data. The links to the repositories where the codes are stored are provided below together with a short description of the purpose of the code.

parallax zero-point correction example recipe

The paper Gaia Early Data Release 3: Parallax bias vs. magnitude, colour, and position by Lindegren et al. (2021) provides a tentative recipe which allows one to correct the parallax of a given Gaia (E)DR3 source for the zero point bias. The correction is provided separately for 5- and 6-parameter astrometric solutions and is given as a function of source magnitude, colour, and celestial position. This correction, which applies equally to Gaia EDR3 and to Gaia DR3 astrometry, is implemented as a Python code which can be found here: 

https://gitlab.com/icc-ub/public/gaiadr3_zeropoint

G-band magnitude correction for sources with 6-parameter astrometric solutions 

The paper Gaia Early Data Release 3: Photometric content and validation by Riello et al. (2021) explains that, for sources with 6-parameter astrometric solutions, the G-band magnitude in Gaia EDR3 should be corrected and a formula to do so is provided. Note: this correction has already been applied in the G-band photometry that has been published in Gaia DR3. The corresponding Python code is presented in Gaia Early Data Release 3: Summary of the contents and survey properties (Gaia Collaboration et al., 2020). The source code can be found as a Jupyter notebook in this repository:

https://github.com/agabrown/gaiaedr3-6p-gband-correction

Corrected flux excess factor

The paper Gaia Early Data Release 3: Photometric content and validation by Riello et al. (2021) presents a corrected version of the photometric flux excess factor as published in the Gaia EDR3 as well as in the Gaia DR3 catalogues. The corrected version accounts for the average variation of the flux excess for 'normal' sources. A formula for calculating the corrected excess factor is provided. The corresponding Python code to do this is presented in Gaia Early Data Release 3: Summary of the contents and survey properties (Gaia Collaboration et al., 2020). The source code can be found as a Jupyter notebook in this repository:

https://github.com/agabrown/gaiaedr3-flux-excess-correction