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ABSTRACT We present the high-z quasar candidate archive (HzQCA), summarizing the spectroscopic observations of 207 z ≳ 5 quasar candidates using Keck/LRIS, Keck/MOSFIRE, and Keck/NIRES. We identify 14 candidates as z ∼ 6 quasars, with 10 of them newly reported here and 63 candidates as brown dwarfs. In the remaining sources, 79 candidates are unlikely to be quasars; 2 sources are inconclusive; the others could not be fully reduced or extracted. Based on the classifications, we investigate the distributions of quasars and contaminants in colour space with photometry measurements from DELS (z), VIKING/UKIDSS (YJHKs/YJHK), and unWISE (W1W2). We find that the identified brown dwarfs are consistent with the empirical brown dwarf model that is commonly used in quasar candidate selection methods. To refine spectroscopic confirmation strategies, we simulate synthetic spectroscopy of high-z quasars and contaminants for all three instruments. The simulations utilize the spectroscopic data in HzQCA. We predict the required exposure times for quasar confirmation and propose an optimal strategy for spectroscopic follow-up observations. For instance, we demonstrate that we can identify a mJ = 21.5 at z = 7.6 or a mJ = 23.0 at z = 7.0 within 15 min of exposure time with LRIS. With the publication of the HzQCA, we aim to provide guidance for future quasar surveys and candidate classification.
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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 (JAB < 21.5) quasars at $$z$$ > 7 from the entire sky, we select 5/6 or a completeness of $$\simeq 80{{\ \rm per\ cent}}$$. The failure to select the majority of 6 < $$z$$ < 7 quasars arises because our quasar density model is based on an empirical quasar spectral energy distribution model that underestimates the scatter in the distribution of fluxes. This new approach to quasar selection paves the way for efficient spectroscopic follow-up of Euclid quasar candidates with ground-based telescopes and James Webb Space Telescope.
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- Award ID(s):
- 1908284
- PAR ID:
- 10381308
- Date Published:
- Journal Name:
- Monthly Notices of the Royal Astronomical Society
- Volume:
- 515
- Issue:
- 3
- ISSN:
- 0035-8711
- Page Range / eLocation ID:
- 3224 to 3248
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1093/mnras/stac1944
