J/ApJS/254/6 Finding QSOs behind the Galactic Plane. I. The GPQ cat. (Fu+, 2021)
Finding quasars behind the Galactic Plane.
I. Candidate selections with transfer learning.
Fu Y., Wu X.-B., Yang Q., Brown A.G.A., Feng X., Ma Q., Li S.
<Astrophys. J. Suppl. Ser., 254, 6 (2021)>
=2021ApJS..254....6F 2021ApJS..254....6F
ADC_Keywords: QSOs; Galactic plane; Photometry, ugriz; Photometry, infrared;
Redshifts; Cross identifications; Proper motions; Surveys
Keywords: Active galactic nuclei ; Astrostatistics techniques ; Classification ;
Catalogs ; Quasars ; Galactic and extragalactic astronomy
Abstract:
Quasars behind the Galactic plane (GPQs) are important astrometric
references and useful probes of Milky Way gas. However, the search for
GPQs is difficult due to large extinctions and high source densities
in the Galactic plane. Existing selection methods for quasars
developed using high Galactic latitude (high-b) data cannot be applied
to the Galactic plane directly because the photometric data obtained
from high-b regions and the Galactic plane follow different
probability distributions. To alleviate this data set shift problem
for quasar candidate selection, we adopt a transfer-learning framework
at both the data and algorithm levels. At the data level, to make a
training set in which a data set shift is modeled, we synthesize
quasars and galaxies behind the Galactic plane based on SDSS sources
and the Galactic dust map. At the algorithm level, to reduce the
effect of class imbalance, we transform the three-class classification
problem for stars, galaxies, and quasars into two binary
classification tasks. We apply the XGBoost algorithm to Pan-STARRS1
(PS1) and AllWISE photometry for classification and an additional cut
on Gaia proper motion to remove stellar contaminants. We obtain a
reliable GPQ candidate catalog with 160946 sources located at
|b|≤20° in the PS1-AllWISE footprint. Photometric redshifts of
GPQ candidates achieved with the XGBoost regression algorithm show
that our selection method can identify quasars in a wide redshift
range (0<z≲5). This study extends the systematic searches for quasars
to the dense stellar fields and shows the feasibility of using
astronomical knowledge to improve data mining under complex conditions
in the big-data era.
File Summary:
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FileName Lrecl Records Explanations
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ReadMe 80 . This file
catalog.dat 553 160946 The Quasar behind the Galactic plane (GPQ)
candidate catalog
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See also:
II/207 : Palomar-Green catalog UV-excess stellar objects (Green+ 1986)
I/345 : Gaia DR2 (Gaia Collaboration, 2018)
VIII/65 : 1.4GHz NRAO VLA Sky Survey (NVSS) (Condon+ 1998)
VII/233 : The 2MASS Extended sources (IPAC/UMass, 2003-2006)
II/328 : AllWISE Data Release (Cutri+ 2013)
VII/273 : The Half Million Quasars (HMQ) catalogue (Flesch, 2015)
II/349 : The Pan-STARRS release 1 (PS1) Survey - DR1 (Chambers+, 2016)
V/153 : LAMOST DR4 catalogs (Luo+, 2018)
VII/285 : Gaia DR2 quasar and galaxy classification (Bailer-Jones+, 2019)
VII/289 : SDSS quasar catalog, sixteenth data release (DR16Q) (Lyke+, 2020)
VII/290 : The Million Quasars (Milliquas) catalogue, version 7.2 (Flesch, 2021)
J/AJ/112/407 : The FIRST bright QSO survey (Gregg+, 1996)
J/AJ/119/2540 : Asiago-ESO/RASS QSO survey. I. (Grazian+, 2000)
J/ApJS/126/133 : The FIRST bright quasar survey. II. (White+, 2000)
J/ApJS/135/227 : The FIRST bright quasar survey. III. (Becker+, 2001)
J/MNRAS/335/673 : DA white dwarfs in 2dF QSO Redshift Survey (Vennes+, 2002)
J/ApJS/155/257 : NBC Quasar Candidate Catalog (Richards+, 2004)
J/AJ/131/2722 : New L and T dwarfs from the SDSS (Chiu+, 2006)
J/ApJ/640/579 : Near-infrared spectra of 27 SDSS quasars (Glikman+, 2006)
J/AJ/134/973 : SDSS Stripe 82 star catalogs (Ivezic+, 2007)
J/MNRAS/392/19 : The 2dF-SDSS QSO survey (Croom+, 2009)
J/ApJ/701/508 : 5000 AGNs behind the Magellanic clouds (Kozlowski+, 2009)
J/MNRAS/406/1583 : Quasar from SDSS and UKIDSS (Wu+, 2010)
J/A+A/542/A110 : Neutral gas in the Milky Way halo (Ben Bekhti+, 2012)
J/AJ/144/49 : Quasars from SDSS-DR7, WISE and UKIDSS surveys (Wu+, 2012)
J/AJ/145/159 : LAMOST. II. ugriz phot. of 526 new quasars (Huo+, 2013)
J/ApJS/218/23 : Fermi LAT third source catalog (3FGL) (Acero+, 2015)
J/other/RAA/15.1438 : LAMOST new QSOs in M31 and M33 vicinity (Huo+, 2015)
J/ApJS/221/12 : AGNs in the MIR using AllWISE data (Secrest+, 2015)
J/other/RAA/16.C7 : LAMOST-SDSS galaxy pairs (Shen+, 2016)
J/AJ/154/269 : A new photo-z method for quasars in Stripe 82 (Yang+, 2017)
J/ApJS/234/23 : The WISE AGN candidates catalogs (Assef+, 2018)
J/A+A/611/A97 : Phot. quasar candidates in Stripe 82 (Pasquet-Itam+, 2018)
J/other/RAA/19.29 : Compilation of known QSOs for Gaia (Liao+, 2019)
Byte-by-byte Description of file: catalog.dat
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Bytes Format Units Label Explanations
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1- 4 A4 --- --- [GPQC]
6- 24 A19 --- GPQC Catalog designation based on PS1 coordinates
(Jhhmmss.ss+ddmmss.s) (Designation)
26- 37 F12.8 deg RAdeg PS1 right ascension (J2000); weighted mean
at mean epoch (ra)
39- 50 F12.8 deg DEdeg [-31.5/83] PS1 declination (J2000) (weighted
mean) at mean epoch (dec)
52- 61 F10.6 deg GLON Galactic longitude (l)
63- 72 F10.6 deg GLAT [-20.1/20.1] Galactic latitude (b)
74- 78 F5.3 --- zphot [0.016/4.8] Photometric redshift predicted
with XGBoost regressor (photoz)
80- 87 E8.3 --- Pstar [1.2e-5/0.01] Probability of the object
being a star (p_star) (1)
89- 93 F5.3 --- Pext [0.99/1] Probability of the object being an
extragalactic object (p_ext) (1)
95- 102 E8.3 --- Pgal [2.6e-5/0.05] Probability of the object
being a galaxy (p2_gal) (2)
104- 108 F5.3 --- Pqso [0.95/1] Probability of the object being a
quasar (p2_qso) (2)
110- 117 F8.5 --- fpm0 [0.0001/20.8]?=- Probability density of zero
proper motion (fPM0) of the source
119- 126 F8.5 --- logfpm0 [-4/1.4]?=99 Log of fpm0 (log_fpm0)
128- 132 F5.3 mag E(B-V) [0.018/8.95] Line-of-sight E(B-V) given by
the Planck14 dust map (ebv)
134- 151 I18 --- objID PS1 unique object identifier (PS_objID)
153- 159 F7.4 mag gmag [14.2/23.8] PS1 mean PSF g-band AB magnitude
161- 166 F6.4 mag e_gmag [0/0.5]?=- Uncertainty in gmag
168- 174 F7.4 mag gKmag [13.4/24]?=- PS1 mean Kron g-band AB magnitude
176- 181 F6.4 mag e_gKmag [0/0.4]?=- Uncertainty in gKmag
183- 189 F7.4 mag rmag [14/22.4] PS1 mean PSF r-band AB magnitude
191- 196 F6.4 mag e_rmag [0/0.5]?=- Uncertainty in rmag
198- 204 F7.4 mag rKmag [14/22.9]?=- PS1 mean Kron r-band band AB
magnitude
206- 211 F6.4 mag e_rKmag [0/0.4]?=- Uncertainty in rKmag
213- 219 F7.4 mag imag [14.1/22.2] PS1 mean PSF i-band AB magnitude
221- 226 F6.4 mag e_imag [0/0.3]?=- Uncertainty in imag
228- 234 F7.4 mag iKmag [14.1/22.2] PS1 mean Kron i-band AB magnitude
236- 241 F6.4 mag e_iKmag [0/0.4] Uncertainty in iKmag
243- 249 F7.4 mag zmag [14/21.7] PS1 mean PSF z-band AB magnitude
251- 256 F6.4 mag e_zmag [0/0.5]?=- Uncertainty in zmag
258- 264 F7.4 mag zKmag [14/22.1] PS1 mean Kron z-band AB magnitude
266- 271 F6.4 mag e_zKmag [0/0.4] Uncertainty in zKmag
273- 279 F7.4 mag ymag [11.5/22.2] PS1 mean PSF y band AB magnitude
281- 286 F6.4 mag e_ymag [0/0.5]?=- Uncertainty in ymag
288- 294 F7.4 mag yKmag [10.6/22.9]?=- PS1 mean Kron y-band AB
magnitude
296- 301 F6.4 mag e_yKmag [0/0.4]?=- Uncertainty in yKmag
303- 321 I19 --- AllWISE AllWISE unique source ID (AllWISE_ID)
323- 328 F6.3 mag W1mag [8.8/18.4] WISE W1 (3.35um) band magnitude
330- 334 F5.3 mag e_W1mag [0.02/0.3] Uncertainty in W1mag
336- 341 F6.3 mag W2mag [7.8/17.2] WISE W2 (4.6um) band magnitude
343- 347 F5.3 mag e_W2mag [0.018/0.3] Uncertainty in W2mag
349- 354 F6.3 mag W3mag [4.2/13.7]?=- WISE W3 (11.6um) band magnitude
356- 360 F5.3 mag e_W3mag [0.01/0.6]?=- Uncertainty in W3mag
362- 367 F6.3 mag W4mag [1.2/10.2]?=- WISE W4 (22.1um) band magnitude
369- 373 F5.3 mag e_W4mag [0.01/0.6]?=- Uncertainty in W4mag
375- 380 F6.3 mag Jmag [12.6/18.7]?=- 2MASS J band (1.25um) magnitude
382- 386 F5.3 mag e_Jmag [0.02/0.4]?=- Uncertainty in Jmag
388- 393 F6.3 mag Hmag [11.8/17.9]?=- 2MASS H band (1.65um) magnitude
395- 399 F5.3 mag e_Hmag [0.019/0.4]?=- Uncertainty in Hmag
401- 406 F6.3 mag Ksmag [10.8/17.4]?=- 2MASS Ks band (2.17um)
magnitude (Kmag)
408- 412 F5.3 mag e_Ksmag [0.017/0.5]?=- Uncertainty in Ksmag (e_Kmag)
414- 432 I19 --- Gaia ?=- Gaia DR2 identifier (Gaiasourceid)
434- 440 F7.3 mas plx [-14.3/11]?=- Gaia DR2 parallax (parallax)
442- 446 F5.3 mas e_plx [0.03/4.4]?=- Parallax uncertainty
(parallax_error)
448- 454 F7.3 mas/yr pmRA [-14.5/15]?=- Gaia DR2 proper motion in
right ascension direction (pmRA*cosDE)
456- 460 F5.3 mas/yr e_pmRA [0.03/5.3]?=- PmRA standard error
(pmra_error)
462- 468 F7.3 mas/yr pmDE [-12.7/13.1]?=- Gaia DR2 proper motion in
declination direction (pmdec)
470- 474 F5.3 mas/yr e_pmDE [0.028/5.2]?=- PmDE standard error
(pmdec_error)
476- 481 F6.3 --- Corpm [-0.94/0.92]?=- Correlation between pmRA and
pmDE (pmrapmdeccorr)
483- 487 F5.3 mas/yr s_pmRA [0.075/5.7]?=- True external uncertainty of
pmRA (pmraerrorext)
489- 493 F5.3 mas/yr s_pmDE [0.073/5.6]?=- True external uncertainty of
pmDE (pmdecerrorext)
495- 524 A30 --- Name Main identifier for an object in SIMBAD
(sbmainid)
526- 544 A19 --- OType Main object type for an object in SIMBAD
(sbmaintype)
546- 553 F8.6 --- z [0.048/4.9]?=- Redshift of an object
recorded in SIMBAD (sb_redshift)
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Note (1): Probability of the object being a star or an extragalactic object,
predicted by the first XGBoost classifier (Pstar+Pext=1).
Note (2): Probability of the object being a galaxy or a quasar,
predicted by the second XGBoost classifier (Pgal+Pqso=1).
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Nomenclature note:
Pan-STARRS sources are named in Simbad and
AllWISE sources are in Simbad.
History:
From electronic version of the journal
References:
Fu et al. Paper II. 2022ApJS..261...32F 2022ApJS..261...32F Cat. J/ApJS/261/32
(End) Emmanuelle Perret [CDS] 05-Jul-2021