PyGaia#
Python modules for the simulation and basic manipulation of Gaia catalogue data and the corresponding uncertainties. To simulate Gaia astrometric data the following functionalities are provided:
transform phase space variables to astrometric observables as well as radial velocities and vice versa
transformations between sky coordinate systems
epoch transformations of the astrometric data, including the transformation of the astrometric covariance matrix
uncertainty models for the astrometric, photometric, and radial velocity data from Gaia
This toolkit is basically an implementation of the performance models for Gaia which are publicly available at: http://www.cosmos.esa.int/web/gaia/science-performance. In addition much of the material in chapter 4 of the book Astrometry for Astrophysics: Methods, Models, and Applications (2012, van Altena et al.) is implemented.
Warning
The code in this package is intended for simulating Gaia catalogue data and its uncertainties and manipulating (Gaia) astrometric data, but is not intended for accurate on-sky astrometry applications, such as predicting in detail astrometric paths of stars on the sky.
Contents:
Acknowledgements#
PyGaia is based on the effort by Jos de Bruijne to create and maintain the Gaia Science Performance pages (with support from David Katz, Paola Sartoretti, Francesca De Angeli, Dafydd Evans, Marco Riello, and Josep Manel Carrasco), and benefits from the suggestions and contributions by Morgan Fouesneau, Tom Callingham, John Helly, Javier Olivares, Henry Leung, Johannes Sahlmann.
The photometric uncertainties code in PyGaia is based on the tool provided by Gaia DPAC to reproduce (E)DR3 Gaia photometric uncertainties described in the GAIA-C5-TN-UB-JMC-031 technical note using data presented in Riello et al (2021).
Attribution#
Please acknowledge the Gaia Project Scientist Support Team and the Gaia Data Processing and Analysis Consortium (DPAC) if you used this code in your research.