The GaiaUnlimited selection function tools can be accessed from our Github pages.
Version 1 (September 2024)
Version 1 of the selection function tools will be used during the third community workshop. Version 1 contains all the code and data from prototype V2 described below.
Source code
The source code for version 1 contains the followin new additions:
- A module to construct and study the selection function for binaries selected on the basis of the RUWE parameter. This is described in Castro-Ginard et al. (2024).
- Example notebooks that show how to construct the selection function for the combination of the Gaia and AllWISE surveys. This will be described in a forthcoming paper by Khanna et al.
- Example notebooks that show how to construct a specialized selection function for the Milky Way’s Aurora population (as seen in RGB stars). This will described in a forthcoming paper by Kurbatov et al.
The code is available here: https://github.com/gaia-unlimited/gaiaunlimited.
Data
The data necessary to use the tools is automatically downloaded from the repository where it resides. Data sets on Zenodo:
- Data for emprical selection function: https://zenodo.org/record/8063930
- Legacy files for the GaiaUnlimited tools: https://zenodo.org/record/8300616
- Detectability of unresolved binary systems: https://zenodo.org/records/11102437
- Uniting Gaia and APOGEE to unveil the cosmic chemistry of the Milky Way disc: https://zenodo.org/records/10628724
- GaiaUnlimited Zenodo community: https://zenodo.org/communities/gaiaunlimited
Prototype V2 (September 2023)
The prototype V2 version of the selection function tools will be used during the second community workshop. The feedback received during and after the workshop will be used as inputs to final version of the tools (planned to appear in 2024). Prototype V2 contains all the code and data from prototype V1 described below.
Source code
The source for prototype V2 contains the following new additions:
- The Gaia RVS selection function
- A module to construct the selection function of subsets of the Gaia data. These most be selected from the Gaia catalogue alone, but can be (for example) a subset that is in other surveys (say, target selection. The typical application will be to construct selection function for samples selected from the gaia catalogue using restrictions on the values of certain columns in the catalogue (for example data quality indicators). Selection functions for more complex selections based on derived quantities can also be computed. The subset selection function tools are described in Castro-Ginard et al. (2023).
- Example notebooks that show how to construct the selection function for the combination of the Gaia and APOGEE spectroscopic surveys. This is described in Cantat-Gaudin et al. (2024).
The code is available here: https://github.com/gaia-unlimited/gaiaunlimited.
Data
The data necessary to use the tools is automatically downloaded from the repository where it resides. Data sets on Zenodo:
- Data for emprical selection function: https://zenodo.org/record/8063930
- Legacy files for the GaiaUnlimited tools: https://zenodo.org/record/8300616
Prototype V1 (September 2022)
The prototype V1 version of the selection function tools will be used during the first community workshop. The feedback received during and after the workshop will be used as inputs to Prototype V2 (planned to appear in 2023).
Source code
The source code for this prototype implements two version of the top level Gaia survey selection function, one based on the approach from the GaiaVerse series of papers, and one based on the approach described in Cantat-Gaudin et al. (2022). The code is available here: https://github.com/gaia-unlimited/gaiaunlimited.
Data
The data necessary to use the tools is automatically downloaded from the repository where it resides. Data sets on Zenodo:
- Data for emprical selection function: https://zenodo.org/record/8063930
Other tools
Principles of selection functions
The basic principles of the GaiaUnlimited approach to selection functions is described in the paper by Rix et al. (2021). The Python notebook to reproduce the results from the paper can be found here.