J/AJ/162/72 Random forests method to discover high-redshift QSOs (Wenzl+, 2021)
Random forests as a viable method to select and discover high-redshift quasars.
Wenzl L., Schindler J.-T., Fan X., Andika I.T., Banados E., Decarli R.,
Jahnke K., Mazzucchelli C., Onoue M., Venemans B.P., Walter F., Yang J.
<Astron. J., 162, 72-72 (2021)>
=2021AJ....162...72W 2021AJ....162...72W (SIMBAD/NED BibCode)
ADC_Keywords: QSOs; Active gal. nuclei; Black holes; Redshifts;
Spectra, optical; Spectra, infrared
Keywords: Active galactic nuclei; Quasars; Supermassive black holes
High-redshift galaxies; Random Forests; Classification
Abstract:
We present a method of selecting quasars up to redshift ∼6 with random
forests, a supervised machine-learning method, applied to Pan-STARRS1
and WISE data. We find that, thanks to the increasing set of known
quasars, we can assemble a training set that enables supervised
machine-learning algorithms to become a competitive alternative to
other methods up to this redshift. We present a candidate set for the
redshift range 4.8-6.3, which includes the region around z=5.5 where
selecting quasars is difficult due to their photometric similarity to
red and brown dwarfs. We demonstrate that, under our survey
restrictions, we can reach a high completeness (66%±7% below
redshift 5.6/83-9+6% above redshift 5.6) while maintaining a high
selection efficiency (78-8+10%/94-8+5% ). Our selection
efficiency is estimated via a novel method based on the different
distributions of quasars and contaminants on the sky. The final
catalog of 515 candidates includes 225 known quasars. We predict the
candidate catalog to contain additional 148-33+41 new quasars
below redshift 5.6 and 45-8+5 above, and we make the catalog
publicly available. Spectroscopic follow-up observations of 37
candidates led us to discover 20 new high redshift quasars (18 at
4.6≲z≲5.5, 2z∼5.7). These observations are consistent with our
predictions on efficiency. We argue that random forests can lead to
higher completeness because our candidate set contains a number of
objects that would be rejected by common color cuts, including one of
the newly discovered redshift 5.7 quasars.
Description:
The data we are mining for quasars is a cross-match between the
publicly available Pan-STARRS DR1 and ALLWISE catalogs.
Photometric follow-up observations have been performed with the Nordic
Optical Telescope (NOT) using the NOT near-infrared Camera and
spectrograph (NOTCam). The observations were taken
on 2019 May 17-20. We used the OB generator for scripting. For our
observations in the J band, we used nine-point dithering. We read out
the detector in ramp-sampling mode with 9s between readouts, a total
of 10 times.
Additionally, we were able to secure optical spectroscopy with the
Goodman High Throughput Spectrograph (HTS) on the Southern
Astrophysical Research Telescope (SOAR; R∼830 at ∼7300Å), with MODS
on the Large Binocular Telescope (LBT), with the Magellan Baade
telescope's Folded port InfraRed Echellette (FIRE; R∼400 at
∼8250-25200Å), and with FORS2 on the Very Large Telescope (VLT).
File Summary:
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FileName Lrecl Records Explanations
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ReadMe 80 . This file
table7.dat 182 515 List of quasar candidates that are part of the
high-z candidates selection and information
about their observation status
table9.dat 99 20 List of newly discovered quasars in this work
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See also:
VII/233 : The 2MASS Extended sources (IPAC/UMass, 2003-2006)
II/328 : AllWISE Data Release (Cutri+ 2013)
II/349 : The Pan-STARRS release 1 (PS1) Survey - DR1 (Chambers+, 2016)
VII/285 : Gaia DR2 quasar and galaxy classification (Bailer-Jones+, 2019)
VII/289 : SDSS quasar catalog, sixteenth data release (DR16Q) (Lyke+, 2020)
J/ApJ/768/105 : z∼5 QSO luminosity function SDSS Stripe 82 (McGreer+, 2013)
J/ApJ/773/14 : BOSS: quasar luminosity function (Ross+, 2013)
J/ApJ/819/24 : z>4.5 QSOs with SDSS and WISE. I. Opt. spectra (Wang+, 2016)
J/ApJS/227/11 : PS1 z>5.6 quasars follow-up (Banados+, 2016)
J/AJ/156/18 : APOGEE DR14:Bin companions evolved stars (Price-Whelan+, 2018)
J/A+A/624/A13 : KiDS DR3 QSO catalog (Nakoneczny+, 2019)
J/A+A/632/A56 : KiDS-BEXGO catalog (Khramtsov+, 2019)
J/ApJS/243/5 : The ELQS in the PS1 footprint (PS-ELQS) (Schindler+, 2019)
Byte-by-byte Description of file: table7.dat
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Bytes Format Units Label Explanations
--------------------------------------------------------------------------------
1- 3 I3 -- Index [0/514] Sequential running index
5- 23 A19 -- WISE Name in ALLWISE catalog
25- 45 F21.17 deg RAdeg [0/360] Pan-STARRS Right Ascension, (J2000)
47- 67 F21.17 deg DEdeg [-30/86] Pan-STARRS Declination (J2000)
69- 86 F18.15 mag zPSF [18.3/20.5] z stacked PSF mag in Pan-STARRS
88-106 F19.17 -- Pz [0.4/1] Probability for high-z class
108-126 F19.17 -- PQSO [0.4/1] Summed probability for quasar classes
128-149 A22 -- PMstar Probability for M star class
151-168 F18.16 -- zPredicted [4.8/6.2] High redshift regression result
170-174 F5.3 -- zspec [4.7/6.3]? Redshift determined from spectrum
176-176 I1 -- Known? Boolean whether quasar is known in literature
178-178 I1 -- Photo? Boolean whether we obtained photometric
follow-up
180-180 I1 -- Obs? Boolean whether we took a spectrum of the
object
182-182 I1 -- Still? Boolean whether object still has to be
observed
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Byte-by-byte Description of file: table9.dat
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Bytes Format Units Label Explanations
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1- 19 A19 --- WISE Name in ALLWISE catalog
21- 32 F12.8 deg RAdeg [1.1/353] Pan-STARRS mean Right Ascension (J2000)
34- 45 F12.8 deg DEdeg [-30/30] Pan-STARRS mean Declination (J2000)
47- 57 F11.8 mag zMag [17.8/20.4] z stacked PSF magnitude in Pan-STARRS,
extinction corrected
59- 70 F12.8 mag M1450 [-28.3/-25.7] M1450 based on PS photometry
72- 86 A15 --- Inst Telescope and Instrument used for observation
88- 89 I2 yr Obs.Y [18/21] Observation year
90- 91 I2 "month" Obs.M Observation month
92- 93 I2 d Obs.D Observation day
95- 99 F5.3 -- z [4.6/5.8] Observed Spectroscopic redshift
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History:
From electronic version of the journal
(End) Prepared by [AAS], Coralie Fix [CDS], 22-Nov-2021