2017
DOI: 10.1051/0004-6361/201629301
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The WISSH quasars project

Abstract: Models and observations suggest that both power and effects of AGN feedback should be maximised in hyper-luminous (L Bol > 10 47 erg s −1 ) quasars, i.e. objects at the brightest end of the AGN luminosity function. In this paper, we present the first results of a multi-wavelength observing program, focusing on a sample of WISE/SDSS selected hyper-luminous (WISSH) broad-line quasars at z ≈ 1.5 − 5. The WISSH quasars project has been designed to reveal the most energetic AGN-driven outflows, estimate their occur… Show more

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Cited by 185 publications
(232 citation statements)
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“…Information about optical rest-frame spectral properties are also reported since 14 out of 41 X-WISSH sources, i.e. ∼ 35% of the sample, have available LBT/LUCI spectroscopy (Bischetti et al 2017;Vietri et al in prep. ) In the right panel of Fig.…”
Section: The X-ray-to-optical Flux Ratio (X/o)mentioning
confidence: 99%
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“…Information about optical rest-frame spectral properties are also reported since 14 out of 41 X-WISSH sources, i.e. ∼ 35% of the sample, have available LBT/LUCI spectroscopy (Bischetti et al 2017;Vietri et al in prep. ) In the right panel of Fig.…”
Section: The X-ray-to-optical Flux Ratio (X/o)mentioning
confidence: 99%
“…Accordingly, we are able to study the behaviour of k Bol,X as a function of M BH and Eddington ratio (λ EDD = L Bol /L EDD ). The SMBH mass for 14 out of 41 objects (∼ 35% of X-WISSH) are estimated from Hβ lines (Bischetti et al 2017;Vietri et al in prep. ), while the remaining ones are calculated from CIV lines (Weedman et al 2012).…”
Section: X-ray Bolometric Corrections and Eddington Ratiosmentioning
confidence: 99%
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“…The IR luminosities of the 36 IR-bright DOG sample are log L IR [L ⊙ ] = 10.5 -13.1, and 25/36 (∼ 69 %) objects are classified as ULIRGs/HyLIRGs (see Table 1). Recently some authors have discovered many (obscured) ULIRGs/HyLIRGs based on the SDSS and WISE data and reported powerful ionized outflows seen in their spectra (Ross et al 2015;Zakamska et al 2016;Bischetti et al 2017;Hamann et al 2017;Zhang et al 2017), although they basically focus on high-z (z > 2) objects.…”
Section: Data and Analysismentioning
confidence: 99%