Hyper-luminous dust-obscured galaxies discovered by the Hyper Suprime-Cam on Subaru and WISE

2015
We present the photometric properties of a sample of infrared (IR) bright dust obscured galaxies (DOGs). Combining wide and deep optical images obtained with the Hyper Suprime-Cam (HSC) on the Subaru Telescopeand all-sky mid-IR (MIR) images taken with Wide-Field Infrared Survey Explorer (WISE), we discovered 48 DOGs with $i - K_\mathrm{s} > 1.2$ and $i - [22] > 7.0$, where $i$, $K_\mathrm{s}$, and [22] represent AB magnitudein the $ i$- band, $K_\mathrm{ s}$- band, and 22 $\mu$m, respectively, in the GAMA 14hr field ($\sim$ 9 deg$^2$). Among these objects, 31 ($\sim$ 65 %) show power-law spectral energy distributions(SEDs) in the near-IR (NIR) and MIR regime, while the remainder show a NIR bump in their SEDs. Assuming that the redshift distribution for our DOGs sample is Gaussian, with mean and sigma $z$ = 1.99 $\pm$ 0.45, we calculated their total IR luminosityusing an empirical relation between 22 $\mu$m luminosityand total IR luminosity. The average value of the total IR luminosityis (3.5 $\pm$ 1.1) $\times$ $10^{13}$ L$_{\odot}$, which classifies them as hyper- luminous infrared galaxies(HyLIRGs). We also derived the total IR luminosity function(LF) and IR luminositydensity (LD) for a flux-limitedsubsample of 18 DOGs with 22 $\mu$m flux greater than 3.0 mJy and with $ i$- bandmagnitude brighter than 24 AB magnitude. The derived space density for this subsample is log $\phi$ = -6.59 $\pm$ 0.11 [Mpc$^{-3}$]. The IR LF for DOGs including data obtained from the literature is well fitted by a double-power law. The derived lower limit for the IR LD for our sample is $\rho_{\mathrm{IR}}$ $\sim$ 3.8 $\times$ 10$^7$ [L$_{\odot}$ Mpc$^{-3}$] and its contributions to the total IR LD, IR LD of all ultra- luminous infrared galaxies(ULIRGs), and that of all DOGs are $>$ 3 %, $>$ 9 %, and $>$ 15 %, respectively.
    • Correction
    • Source
    • Cite
    • Save
    122
    References
    39
    Citations
    NaN
    KQI
    []
    Baidu
    map