Obscured and unobscured AGN populations in a hard-X-ray selected sample of the XMDS survey

Abstract

Our goal is to probe the populations of obscured and unobscured AGN investigating their optical-IR and X-ray properties as a function of X-ray flux, luminosity and redshift within a hard X-ray selected sample of 136 X-ray sources in the XMM Medium Deep Survey (XMDS) with wide multiwavelength coverage. The XMDS area is covered with optical photometry from the VVDS and CFHTLS surveys and infrared Spitzer data. Based on the X-ray luminosity and X-ray to optical ratio, 132 sources are likely AGN, of which 122 have unambiguous optical - IR identification. The observed optical and IR spectral energy distributions of sources are fitted with AGN/galaxy templates in order to classify them and compute photometric redshifts. 70% of the AGN are fitted by a type 2 AGN or a star forming galaxy template and are grouped together in a single class of ``optically obscured'' AGN. They have ``red'' optical colors and generally show significant X-ray absorption from X-ray spectra or hardness ratios (N${}_H>10^{22}$ cm${}^{-2}$). Sources with SEDs typical of type 1 AGN have ``blue'' optical colors and exhibit X-ray absorption in about 30% of cases. We performed a stacking analysis for obscured and type 1 AGN. The stacked X-ray spectrum of obscured AGN is flatter than that of type 1 AGN and has an average spectral slope of Gamma = 1.6. The subsample of objects fitted by a galaxy template has an even harder stacked spectrum, with Gamma = 1.2 - 1.3. The obscured fraction is larger at lower fluxes, lower redshifts and lower luminosities. X-ray absorption is less common than ``optical'' obscuration and its incidence is nearly constant with redshift and luminosity. This implies that X-ray absorption is not necessarily related to optical obscuration.

33 pages, 21 figures, accepted for publication in A&A

References in corpus (14)

• X-ray spectral properties of AGN in the Chandra Deep Field South
• Radiative Transfer Modeling of Three-Dimensional Clumpy AGN Tori and its Application to NGC 1068
• The UKIRT Infrared Deep Sky Survey Early Data Release
• Chandra and Spitzer unveil heavily obscured quasars in the SWIRE/Chandra Survey
• The XMM Large Scale Structure survey: The X-ray pipeline and survey selection function
• XMM and Chandra measurements of the AGN intrinsic absorption: dependence on luminosity and redshift
• XMM-Newton observations of the Lockman Hole V: time variability of the brightest AGN
• ImpZ: a new photometric redshift code for galaxies and quasars
• The Serendipitous Extragalactic X-Ray Source Identification (SEXSI) Program. III. Optical Spectroscopy
• The XMM-LSS Survey: A well controlled X-ray cluster sample over the D1 CFHTLS area
• The VIMOS VLT Deep Survey: the faint type-1 AGN sample
• The HELLAS2XMM survey.VIII. Optical identifications of the extended sample
• X-ray spectra of sources in the 13H XMM-Newton / Chandra deep field
• An X-ray bright ERO hosting a type 2 QSO

Cited by in corpus (12)

• SuperRAENN: A Semi-supervised Supernova Photometric Classification Pipeline Trained on Pan-STARRS1 Medium Deep Survey Supernovae
• A Full Year's Chandra Exposure on SDSS Quasars from the Chandra Multiwavelength Project
• Discovery of Hot Gas in Outflow in NGC3379
• Properties of AGN Multiband Optical Variability in the HSC SSP Transient Survey
• Obscured and powerful AGN and starburst activities at z~3.5
• VVDS-SWIRE: Clustering evolution from a spectroscopic sample of galaxies with redshift 0.2<z<2.1 selected from Spitzer IRAC 3.6 micron and 4.5 micron photometry
• Photometric Classification of 2315 Pan-STARRS1 Supernovae with Superphot
• A multi-wavelength survey of AGN in the XMM-LSS field: I. Quasar selection via the KX technique
• Frying Doughnuts: What can the reprocessing of X-rays to IR tell us about the AGN environment?
• Identifying type II quasars at intermediate redshift with few-shot learning photometric classification
• X-Ray Redshifts of Obscured Chandra Source Catalog AGN
• Reconstructing AGN X-ray spectral parameter distributions with Bayesian methods I: Spectral analysis