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Paving the way for Euclid and JWST via probabilistic selection of high-redshift quasars
ABSTRACT We introduce a probabilistic approach to select 6 ≤ $z$ ≤ 8 quasar candidates for spectroscopic follow-up, which is based on density estimation in the high-dimensional space inhabited by the optical and near-infrared photometry. Densities are modelled as Gaussian mixtures with principled accounting of errors using the extreme deconvolution (XD) technique, generalizing an approach successfully used to select lower redshift ($z$ ≤ 3) quasars. We train the probability density of contaminants on 1902 071 7-d flux measurements from the 1076 deg2 overlapping area from the Dark Energy Camera Legacy Survey (DECaLS) ($z$), VIKING (YJHKs), and unWISE (W1W2) imaging surveys, after requiring they dropout of DECaLS g and r, whereas the distribution of high-$z$ quasars are trained on synthetic model photometry. Extensive simulations based on these density distributions and current estimates of the quasar luminosity function indicate that this method achieves a completeness of $\ge 56{{\ \rm per\ cent}}$ and an efficiency of $\ge 5{{\ \rm per\ cent}}$ for selecting quasars at 6 < $z$ < 8 with JAB < 21.5. Among the classified sources are 8 known 6 < $z$ < 7 quasars, of which 2/8 are selected suggesting a completeness $\simeq 25{{\ \rm per\ cent}}$, whereas classifying the 6 known more »
Authors:
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Award ID(s):
Publication Date:
NSF-PAR ID:
10381308
Journal Name:
Monthly Notices of the Royal Astronomical Society
Volume:
515
Issue:
3
Page Range or eLocation-ID:
3224 to 3248
ISSN:
0035-8711
National Science Foundation
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