Paving the Way for Euclid and JWST via Optimal Selection of High-z
  Quasars
  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.
Density distributions are modeled 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 733,694 7-d flux
measurements from the 1076 square degrees overlapping area from the DECaLS (z),
VIKING (YJHK), 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 >75% and an efficiency of >15% for
selecting quasars at 6<z<8 with J<21.5. Among the classified sources are 8
known 6<z<7 quasars, of which 2/8 are selected suggesting a completeness ~25%,
whereas classifying the 6 known (J<21.5) quasars at z>7 from the entire sky, we
select 5/6 or a completeness of ~80%.The failure to select the majority of
6<z<7 quasars arises because our model of quasar SEDs underestimates the
scatter in the distribution of fluxes. This new optimal approach to quasar
selection paves the way for efficient spectroscopic follow-up of Euclid quasar
candidates with ground based telescopes and JWST.
