This file describes the content in https://www.cosmos.esa.int/web/gaia/dr3-software-tools under the following title:


Title: FITTED (E)DR3 PHOTOMETRIC UNCERTAINTIES TOOL 

One line description: Tool to reproduce the median (E)DR3 photometric uncertainty for G, BP and RP instruments and scale them to different number of observations.



We provide a tool (Python Jupyter notebook) to easily reproduce the median behaviour of (E)DR3 photometric uncertainties in the three Gaia passbands (G, BP and RP). The process to derive this median behaviour is explained in the GAIA-C5-TN-UB-JMC-031 Technical note. The code needs an input file with the B-spline knots and coefficients also provided here. The same tool is also able to scale the fitted B-splines to sources with different numbers of observations.

Note that these uncertainties do not take into account systematic effects remaining in the data that originate in the properties of the source e.g. magnitude and colour. Systematic effects remaining in the data that vary with each CCD or FoV are accounted for by this tool and can be scaled to future releases with the understanding that these systematic effects will be reduced. We recommend reading Riello et al. (2021) and Fabricius et al. (2021) for getting a better understanding of the remaining systematics within the (E)DR3 photometry.

This folder contains three files:

- GAIA-C5-TN-UB-JMC-031-2.pdf: Technical note explaining the procedure we used to fit (using B-splines) the (E)DR3 photometric uncertainties as a function of magnitude and how to scale the fitting at different numbers of observations. 

- EDR3_Photometric_Uncertainties.ipynb: Python Jupyter notebook able to reproduce the (E)DR3 photometric uncertainties and scale them for different numbers of observations. It needs LogErrVsMagSpline.csv file as input, also included in this page.

- LogErrvsMagSpline.csv: File including the B-spline knots and coefficients fitted in GAIA-C5-TN-UB-JMC-031-3.pdf. This file is used as input in EDR3_Photometric_Uncertainties_2.ipynb Python Jupyter notebook to reproduce the (E)DR3 photometric uncertainties.


This code was developed by CU5 calibration unit, under Gaia Data Processing and Analysis Consortium (DPAC). In particular, it was developed by the authors of the attached technical note (J.M. Carrasco, F. De Angeli, M. Riello, C. Jordi) and some helpful comments were provided by J. de Bruijne and D.W. Evans.


If your research benefits from the use of this code, we would appreciate if you could include the following acknowledgement in your publication:

"This research has made use of the tool provided by Gaia DPAC (https://www.cosmos.esa.int/web/gaia/dr3-software-tools) to reproduce (E)DR3 Gaia photometric uncertainties described in the GAIA-C5-TN-UB-JMC-031 technical note using data in Riello et al (2021)."