2.1 Cross-match criteria

By position

\includegraphics[width=\textwidth ]{img/algoSimple.png}

This cross-match is a simple cross-match: for each source of the catalogue A, it returns the sources of the catalogue B lying at an angular distance less than Radius ($\theta $) from the catalogue A source.

In other words, for each source of the catalogue A, it returns the sources of the catalogue B which are inside the cone of aperture $2\theta $ having for apex (also called vertex) the center of the ICRS frame and for axis the direction of the catalogue A source in the ICRS frame.

By position including error

\includegraphics[width=\textwidth ]{img/algoPosErr.png}

This cross-match is a cross-match based on the positional uncertainties of both catalogue A and catalogue B sources. The selection of the candidates is the same as described in section 3.1.1 of Pineau et al. (2011b).

To look for candidates, we first estimate the maximum of the semi-major axis of errors on the positions of catalogue B sources by a boxplot: $e_{B max} = q_3 + 1.5 (q_3 - q_1)$ where $q_1$ and $q_3$ are the lower and upper quartiles respectively.

For each source of the catalogue A, the search radius we use to look for catalogue B source candidates is the minimum between $k_{\gamma } \sqrt {e_ A^2 + e_{B max}^2}$ (in which $e_ A$ is the semi-major axis of the error on the source of the catalogue A), and the watchdog Max. distance. We then keep only candidates satisfying eq. 3 of Pineau et al. (2011b).

INFO:

WARNING:

  1. Sources having for positional error a NULL (or a NaN) value are ignored.

  2. Because of the use of a boxplot on positional errors to avoid outliers, the result of the cross-match of 2 catalogues is not necessarily symmetric: the number of associations between A and B can be different from the number of associations between B and A.