Context. Sky surveys produce enormous quantities of data on extensive regions of the sky. The easiest way to access this information is through catalogues of standardised data products. XMM-Newton has been surveying the sky in the X-ray, ultra-violet, and optical bands for 20 years. Aims. The XMM-Newton Survey Science Centre has been producing standardised data products and catalogues to facilitate access to the serendipitous X-ray sky. Methods. Using improved calibration and enhanced software, we re-reduced all of the 14 041 XMM-Newton X-ray observations, of which 11 204 observations contained data with at least one detection and with these we created a new, high quality version of the XMM-Newton serendipitous source catalogue, 4XMM-DR9. Results. 4XMM-DR9 contains 810 795 detections down to a detection significance of 3σ, of which 550 124 are unique sources, which cover 1152 degrees2 (2.85%) of the sky. Filtering 4XMM-DR9 to retain only the cleanest sources with at least a 5σ detection significance leaves 433 612 detections. Of these detections, 99.6% have no pileup. Furthermore, 336 columns of information on each detection are provided, along with images. The quality of the source detection is shown to have improved significantly with respect to previous versions of the catalogues. Spectra and lightcurves are also made available for more than 288 000 of the brightest sources (36% of all detections).
Context. Catalogue cross-correlation is essential to building large sets of multi-wavelength data, whether it be to study the properties of populations of astrophysical objects or to build reference catalogues (or timeseries) from survey observations. Nevertheless, resorting to automated processes with limited sets of information available on large numbers of sources detected at different epochs with various filters and instruments inevitably leads to spurious associations. We need both statistical criteria to select detections to be merged as unique sources, and statistical indicators helping in achieving compromises between completeness and reliability of selected associations. Aims. We lay the foundations of a statistical framework for multi-catalogue cross-correlation and cross-identification based on explicit simplified catalogue models. A proper identification process should rely on both astrometric and photometric data. Under some conditions, the astrometric part and the photometric part can be processed separately and merged a posteriori to provide a single global probability of identification. The present paper addresses almost exclusively the astrometrical part and specifies the proper probabilities to be merged with photometric likelihoods. Methods. To select matching candidates in n catalogues, we used the Chi (or, indifferently, the Chi-square) test with 2(n−1) degrees of freedom. We thus call this cross-match a χ -match. In order to use Bayes' formula, we considered exhaustive sets of hypotheses based on combinatorial analysis. The volume of the χ -test domain of acceptance -a 2(n − 1)-dimensional acceptance ellipsoid -is used to estimate the expected numbers of spurious associations. We derived priors for those numbers using a frequentist approach relying on simple geometrical considerations. Likelihoods are based on standard Rayleigh, χ and Poisson distributions that we normalized over the χ -test acceptance domain. We validated our theoretical results by generating and cross-matching synthetic catalogues. Results. The results we obtain do not depend on the order used to cross-correlate the catalogues. We applied the formalism described in the present paper to build the multi-wavelength catalogues used for the science cases of the Astronomical Resource Cross-matching for High Energy Studies (ARCHES) project. Our cross-matching engine is publicly available through a multi-purpose web interface. In a longer term, we plan to integrate this tool into the CDS XMatch Service.
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