Gaia DR3 software tools

Gaia data release 3 brings various new and "complex" data products such as BP and RP spectra, RVS spectra, Monte Carlo Markov Chain samples from the astrophysical parameter inference, etc. To help the scientific community use these new data products, several software tools have been developed by the Gaia Data Processing and Analysis Consortium. Below a summary of the different software tools is provided.

GaiaXPy Tool

BP/RP spectra (often shortened as XP spectra) are published for the first time in Gaia Data Release 3 (DR3). In their first release, only source mean spectra will be available: these are spectra that have been generated from a number of single observations (transits) of the same object. Epoch spectra, i.e. spectra consisting of one single observation, will become available in future releases. The GaiaXPy tool is a Python library to facilitate handling Gaia BP/RP spectra as distributed from the Gaia archive.

G-band bolometric Correction Tool

This is a tool to calculate bolometric corrections in the Gaia (E)DR3 G-band. It is based on the same set of models and interpolation method that has been used in Gaia DR3 to produce luminosities in the astrophysical parameter tables.

GSP-Phot Metallicity Calibration Tool

This is a tool to calibrate the stellar metallicity estimates provided by GSP-Phot, the Generalised Stellar Parametrizer pipeline based on Gaia photometry. It reproduces the LAMOST DR6 [Fe/H] scale.

GSP-Spec metallicity/logG calibration tool

Gaia DR3 provides the stellar atmospheric parameters and chemical abundances determined from the RVS spectra analysed by the GSP-Spec and ANN algorithms. This tool provides the calibrations recommended for these data since they were intentionally published with no corrections. Tables, codes and examples are included as references.

OA Self-Organising Map Tool (GUASOM)

For Gaia DR3 Outlier Analysis (OA), 56 million objects are analyzed, with a probability of membership to typical astronomical object classes below a certain threshold (see documentation), i.e. classification outliers. Self-Organizing Maps (SOM) is the unsupervised clustering method selected to perform this task. To facilitate the analysis of the Gaia DR3 outliers map, a free access adaptive visualization tool has been developed, GUASOM flavour DR3, that allows to explore the relations between millions of objects of complex nature, with several oriented domain visualizations.

Extinction as a function of (l,b)

This tool provides access to the total Galactic extinction as published in Gaia DR3 at user defined coordinates (or a list of coordinates). The maps are available at four different angular resolutions (HEALPix levels 6, 7, 8, and 9). Users may also obtain the uncertainty of the measured extinction as well as other flags associated with the creation of the extinction maps.

Extinction coefficients in various passbands

Dustapprox is a suite to compute photometric extinction coefficients in various passbands.

Fitted gaia (E)DR3 photometric uncertainties tool

This tool reproduces the median Gaia (E)DR3 photometric uncertainty for the G, BP, and RP instruments and associated broad-band magnitudes and allows to scale the uncertainties to a user-defined number of observations.

Tools for non-single stars

This software provides two functionalities to manage the output of the non-single star (NSS) solutions: one to derive the covariance matrix from the uncertainties and the correlation matrix, and one to transform the Thiele-Innes orbital elements to the classical Campbell elements with propagation of the uncertainties.

TUTORIAL: ACCESS OF ALL DATALINK PRODUCTS

This Jupyter notebook tutorial explains how to access DataLink products (epoch photometry, BP/RP spectra, RVS spectra, Monte Carlo Markov Chain samples) for multiple sources, explaining the various data structures (RAW, INDIVIDUAL, and COMBINED).

tutorial: programmatic download of large datasets provided through datalink

This Jupyter notebook tutorial shows how to access and download DataLink products (epoch photometry, BP/RP spectra, RVS spectra, Monte Carlo Markov Chain samples) for datasets with more than 5000 sources (which is the limit of the Gaia ESA Archive web interface).

BULK DOWNLOAD SUMMARY AND EXAMPLE CONE SEARCH SCRIPTS

This tutorial, supported by a Jupyter notebook, explains how the bulk download repository for Gaia DR3 has been organised and structured using the HEALPix sky tessellation and how to retrieve all Gaia DR3 data for a particular, user-defined region in the sky from this repository.

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., 2021). The source code can be found as a Jupyter notebook in this repository:

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

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

Using Gaia parallaxes

The paper Gaia Data Release 2: Using Gaia Parallaxes by Luri et al. (2018) provides generic, data release independent advise on how to best make use of Gaia's astrometric data. The associated Python and R codes, for instance to derive mean distances to open clusters or to make a Bayesian inference of the distance of a star based on its measured parallax, can be found here:

https://github.com/agabrown/astrometry-inference-tutorials