ABSTRACT We present two catalogues of active galactic nucleus (AGN) candidates selected from the latest data of two all-sky surveys – Data Release 2 of the Gaia mission and the unWISE catalogue of the Wide-field Infrared Survey Explorer (WISE). We train a random forest classifier to predict the probability of each source in the Gaia–unWISE joint sample being an AGN, PRF, based on Gaia astrometric and photometric measurements and unWISE photometry. The two catalogues, which we designate C75 and R85, are constructed by applying different PRF threshold cuts to achieve an overall completeness of 75 per cent (≈90 per cent at GaiaG ≤ 20 mag) and reliability of 85 per cent, respectively. The C75 (R85) catalogue contains 2734 464 (2182 193) AGN candidates across the effective 36 000 deg2 sky, of which ≈0.91 (0.52) million are new discoveries. Photometric redshifts of the AGN candidates are derived by a random forest regressor using Gaia and WISE magnitudes and colours. The estimated overall photometric redshift accuracy is 0.11. Cross-matching the AGN candidates with a sample of known bright cluster galaxies, we identify a high-probability strongly lensed AGN candidate system, SDSS J1326+4806, with a large image separation of 21${^{\prime\prime}_{.}}$06. All the AGN candidates in our catalogues will have ∼5-yr long light curves from Gaia by the end of the mission, and thus will be a great resource for AGN variability studies. Our AGN catalogues will also be helpful in AGN target selections for future spectroscopic surveys, especially those in the Southern hemisphere. The C75 catalogue can be downloaded at https://www.ast.cam.ac.uk/~ypshu/AGN_Catalogues.html.
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The probabilistic random forest applied to the QUBRICS survey: improving the selection of high-redshift quasars with synthetic data
Several recent works have focused on the search for bright, high-z quasars (QSOs) in the South. Among them, the QUasars as BRIght beacons for Cosmology in the Southern hemisphere (QUBRICS) survey has now delivered hundreds of new spectroscopically confirmed QSOs selected by means of machine learning algorithms. Building upon the results obtained by introducing the probabilistic random forest (PRF) for the QUBRICS selection, we explore in this work the feasibility of training the algorithm on synthetic data to improve the completeness in the higher redshift bins. We also compare the performances of the algorithm if colours are used as primary features instead of magnitudes. We generate synthetic data based on a composite QSO spectral energy distribution. We first train the PRF to identify QSOs among stars and galaxies, then separate high-z quasar from low-z contaminants. We apply the algorithm on an updated data set, based on SkyMapper DR3, combined with Gaia eDR3, 2MASS, and WISE magnitudes. We find that employing colours as features slightly improves the results with respect to the algorithm trained on magnitude data. Adding synthetic data to the training set provides significantly better results with respect to the PRF trained only on spectroscopically confirmed QSOs. We estimate, on a testing data set, a completeness of $\sim 86{{\ \rm per\ cent}}$ and a contamination of $\sim 36{{\ \rm per\ cent}}$. Finally, 206 PRF-selected candidates were observed: 149/206 turned out to be genuine QSOs with z > 2.5, 41 with z < 2.5, 3 galaxies and 13 stars. The result confirms the ability of the PRF to select high-z quasars in large data sets.
more » « less- NSF-PAR ID:
- 10376052
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Monthly Notices of the Royal Astronomical Society
- Volume:
- 517
- Issue:
- 2
- ISSN:
- 0035-8711
- Page Range / eLocation ID:
- p. 2436-2453
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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