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Spitzer IRAC selection is a powerful tool for identifying luminous AGN. For deep IRAC data, however, the AGN selection wedges currently in use are heavily contaminated by star-forming galaxies, especially at high redshift. Using the large samples of luminous AGN and high-redshift star-forming galaxies in COSMOS, we redefine the AGN selection criteria for use in deep IRAC surveys. The new IRAC criteria are designed to be both highly complete and reliable, and incorporate the best aspects of the current AGN selection wedges and of infrared power-law selection while excluding high redshift star-forming galaxies selected via the BzK, DRG, LBG, and SMG criteria. At QSO-luminosities of log L(2-10 keV) (ergs/s) > 44, the new IRAC criteria recover 75% of the hard X-ray and IRAC-detected XMM-COSMOS sample, yet only 38% of the IRAC AGN candidates have X-ray counterparts, a fraction that rises to 52% in regions with Chandra exposures of 50-160 ks. X-ray stacking of the individually X-ray non-detected AGN candidates leads to a hard X-ray signal indicative of heavily obscured to mildly Compton-thick obscuration (log N_H (cm^-2) = 23.5 +/- 0.4). While IRAC selection recovers a substantial fraction of luminous unobscured and obscured AGN, it is incomplete to low-luminosity and host-dominated AGN.
We present a study of X-ray AGN overdensities in 16 Abell clusters, within the redshift range 0.073<z<0.279, in order to investigate the effect of the hot inter-cluster environment on the triggering of the AGN phenomenon. The X-ray AGN overdensities,
In order to find the most extreme dust-hidden high-redshift galaxies, we select 196 extremely red objects in the Ks and IRAC bands (KIEROs, [Ks-4.5um](AB)>1.6) in the 0.06 deg^2 GOODS-N region. This selection avoids the Balmer breaks of galactic spec
Gravitational lensing magnification modifies the observed spatial distribution of galaxies and can severely bias cosmological probes of large-scale structure if not accurately modelled. Standard approaches to modelling this magnification bias may not
A novel method images to estimate cosmological parameters based on images is presented. In this paper, we demonstrate the use of a convolutional neural network (CNN) for constraining the mass of dark matter particle. For this purpose, we perform a su
The IfA Deep survey uncovered ~130 thermonuclear supernovae (TNSNe, i.e. Type Ia) candidates at redshifts from z=0.1 out to beyond z=1. The TNSN explosion rates derived from these data have been controversial, conflicting with evidence emerging from