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High-z quasar candidate archive: a spectroscopic catalogue of quasars and contaminants in various quasar searches
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....
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Published in: | Monthly notices of the Royal Astronomical Society 2024-01, Vol.528 (2), p.2679-2710 |
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Main Authors: | , , , , , , , , , , , |
Format: | Article |
Language: | English |
Citations: | Items that this one cites |
Online Access: | Get full text |
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Summary: | 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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ISSN: | 0035-8711 1365-2966 |
DOI: | 10.1093/mnras/stae094 |