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Abstract: Since its launch in 1999, the XMM-Newton mission has compiled the largest catalogue of serendipitous X-ray sources, with the 3XMM being the third version of this catalogue. This was possible thanks to the combination of a large effective area (5000 cm2 at 1 keV) and a wide field of view (30 arcmin). The 3XMM-DR6 catalogue contains about 470 000 unique X-ray sources over an area of 982 deg2. A significant fraction of these (100 178 sources) have reliable optical, near-(NIR), and mid-infrared (MIR) counterparts in the SDSS, PANSTARRS, VIDEO, UKIDSS, and WISE surveys. In a previous paper we presented photometric redshifts for these sources using the TPZ machine-learning algorithm. About a quarter of these (22 677) have adequate photon statistics, meaning that a reliable X-ray spectrum can be extracted. Owing to both the X-ray counts selection and the optical counterpart constraint, the sample above is biased towards the bright sources. Here, we present XMMFITCAT-Z: a spectral fit catalogue for these sources using the Bayesian X-ray Analysis technique. To demonstrate the potential of the present catalogue, we comment on the optical and MIR colours of the 765 X-ray absorbed sources with NH?> ?1022?cm?2. We show that a considerable fraction of X-ray-selected AGNs would not be classified as AGNs following the MIR W1?W2 versus W2 selection criterion. These are AGNs with lower luminosities, where the contribution of the host galaxy to the MIR emission is non-negligible. Only one-third of obscured AGNs in X-rays present red colours or r?W2 > 6. Also, it appears that the r?W2 criterion, often used in the literature for the selection of obscured AGNs, produces very different X-ray absorbed AGN samples compared to the standard X-ray selection criteria.
Fuente: Astronomy & Astrophysics. Volume 645, January 2021. A74
Editorial: EDP Sciences
Año de publicación: 2021
Nº de páginas: 11
Tipo de publicación: Artículo de Revista
Proyecto español: RTI2018-096686-B-C21
Url de la publicación: https://doi.org/10.1051/0004-6361/202039431
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RUIZ CAMUÑAS, ÁNGEL
AMALIA CORRAL RAMOS