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Obscured AGN at z~1 from the zCOSMOS-Bright Survey I. Selection and Optical Properties of a [Ne v]-selected sample

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 Added by Marco Mignoli
 Publication date 2013
  fields Physics
and research's language is English




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A sample of 94 narrow line AGN with 0.65<z<1.20 has been selected from the 20k-Bright zCOSMOS galaxy sample by detection of the high-ionization [NeV]3426 line. Taking advantage of the large amount of data available in the COSMOS field, the properties of the [NeV]-selected Type-2 AGN have been investigated, focusing on their host galaxies, X-ray emission, and optical line flux ratios. Finally, the diagnostic developed by Gilli et al. (2010), based on the X-ray to [NeV] luminosity ratio, has been exploited to search for the more heavily obscured AGN. We found that [Ne v]-selected narrow line AGN have Seyfert 2-like optical spectra, although with emission line ratios diluted by a star-forming component. The ACS morphologies and stellar component in the optical spectra indicate a preference for our Type-2 AGN to be hosted in early-spirals with stellar masses greater than 10^(9.5-10)Msun, on average higher than those of the galaxy parent sample. The fraction of galaxies hosting [NeV]-selected obscured AGN increases with the stellar mass, reaching a maximum of about 3% at 2x10^11 Msun. A comparison with other selection techniques at z~1 shows that the detection of the [Ne v] line is an effective method to select AGN in the optical band, in particular the most heavily obscured ones, but can not provide by itself a complete census of AGN2. Finally, the high fraction of [NeV]-selected Type-2 AGN not detected in medium-deep Chandra observations (67%) is suggestive of the inclusion of Compton-thick sources in our sample. The presence of a population of heavily obscured AGN is corroborated by the X-ray to [NeV] ratio; we estimated, by mean of X-ray stacking technique and simulations, that the Compton-thick fraction in our sample of Type-2 AGN is 43+-4%, in good agreement with standard assumptions by the XRB synthesis models.



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134 - C. Vignali 2014
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