J/MNRAS/489/663 Photometric redshifts for X-ray-selected AGNs (Brescia+, 2019)
Photometric redshifts for X-ray-selected active galactic nuclei in the eROSITA
era.
Brescia M., Salvato M., Cavuoti S., Ananna T.T., Riccio G., Lamassa S.M.,
Urry C.M., Longo G.
<Mon. Not. R. Astron. Soc., 489, 663-680 (2019)>
=2019MNRAS.489..663B 2019MNRAS.489..663B (SIMBAD/NED BibCode)
ADC_Keywords: Active gal. nuclei ; X-ray sources ; Redshifts ;
Positional data ; Optical
Keywords: methods: data analysis - methods: statistical - galaxies: active -
galaxies: distances and redshifts - X-rays: galaxies
Abstract:
With the launch of eROSITA (extended Roentgen Survey with an Imaging
Telescope Array), successfully occurred on 2019 July 13, we are facing
the challenge of computing reliable photometric redshifts for 3
million of active galactic nuclei (AGNs) over the entire sky, having
available only patchy and inhomogeneous ancillary data. While we have
a good understanding of the photo-z quality obtainable for AGN using
spectral energy distribution (SED)-fitting technique, we tested the
capability of machine learning (ML), usually reliable in computing
photo-z for QSO in wide and shallow areas with rich spectroscopic
samples. Using MLPQNA as example of ML, we computed photo-z for the
X-ray-selected sources in Stripe 82X, using the publicly available
photometric and spectroscopic catalogues. Stripe 82X is at least as
deep as eROSITA will be and wide enough to include also rare and
bright AGNs. In addition, the availability of ancillary data mimics
what can be available in the whole sky. We found that when optical,
and near- and mid-infrared data are available, ML and SED fitting
perform comparably well in terms of overall accuracy, realistic
redshift probability density functions, and fraction of outliers,
although they are not the same for the two methods. The results could
further improve if the photometry available is accurate and including
morphological information. Assuming that we can gather sufficient
spectroscopy to build a representative training sample, with the
current photometry coverage we can obtain reliable photo-z for a large
fraction of sources in the Southern hemisphere well before the
spectroscopic follow-up, thus timely enabling the eROSITA science
return. The photo-z catalogue is released here.
Description:
With the launch of eROSITA, we are facing the challenge of computing
the photo-z in a reliable and fast manner for about 3 million sources
distributed in the entire sky, with multiwavelength data that are
non-homogeneous in depth and wavelength coverage. Given that photo-z
computed with ML are becoming the trend in cosmological surveys
involving normal galaxies, we wanted to test whether this is a viable
solution also for AGN. With this purpose in mind we have used the
multiwavelength catalogue of the counterparts to the X-ray-selected
sources detected in Stripe 82X (LaMassa et al. 2016ApJ...817..172L 2016ApJ...817..172L,
Cat. J/ApJ/817/172), presented in Ananna et al. (2017ApJ...850...66A 2017ApJ...850...66A,
Cat. J/ApJ/850/66). As machine learning method we have tested MLPQNA
(Brescia et al. 2013ApJ...772..140B 2013ApJ...772..140B). The catalogue containing the
photo-z computed for this paper is presented in table A1.
The catalogue is indexed on the first column, which can be used to
retrieve all other information about spectroscopic redshifts and X-ray
source counterparts, by cross-matching this catalogue with the one
referred in Ananna et al. (2017ApJ...850...66A 2017ApJ...850...66A, Cat. J/ApJ/850/66).
The other columns are right ascension and declination of optical
counterparts, followed by the photo-z estimations obtained by all
combinations of surveys, i.e. SDSS, VHS, IRAC, and WISE. The last
column is related to the best photo-z obtained from all the previous
combinations.
File Summary:
--------------------------------------------------------------------------------
FileName Lrecl Records Explanations
--------------------------------------------------------------------------------
ReadMe 80 . This file
tablea1.dat 154 5990 Example of contents of the photo-z catalogue
made publicly available
--------------------------------------------------------------------------------
See also:
J/ApJ/817/172 : X-ray Observations of Stripe 82 (LaMassa+, 2016)
J/ApJ/850/66 : Stripe 82X survey multiwavelength catalog (Ananna+, 2017)
Byte-by-byte Description of file: tablea1.dat
--------------------------------------------------------------------------------
Bytes Format Units Label Explanations
--------------------------------------------------------------------------------
1- 6 I6 --- ID [1/196076] Object identifier from
Ananna et al. (2017ApJ...850...66A 2017ApJ...850...66A,
Cat. J/ApJ/850/66)
8- 27 F20.16 deg RAdeg Right ascension (J2000)
29- 51 F23.20 deg DEdeg Declination (J2000)
53- 64 F12.8 --- zsdssVWI ?=-99.0 Photometric redshift obtained by
combining SDSS, VHS, IRAC and WISE surveys
66- 78 F13.9 --- zsdssVW ?=-99.0 Photometric redshift obtained by
combining SDSS, VHS and WISE surveys
80- 92 F13.9 --- zsdssVI ?=-99.0 Photometric redshift obtained by
combining SDSS, VHS and IRAC surveys
94-102 F9.5 --- zsdssW ?=-99.0 Photometric redshift obtained by
combining SDSS and WISE
104-116 F13.9 --- zsdssI ?=-99.0 Photometric redshift obtained by
combining SDSS and IRAC
118-126 F9.5 --- zsdssV ?=-99.0 Photometric redshift obtained by
combining SDSS and VHS
128-140 F13.9 --- zsdss ?=-99.0 Photometric redshift from SDSS
142-154 F13.9 --- zMLPQNA ?=-99.0 Best photometric redshift obtained
from all the previous combinations
--------------------------------------------------------------------------------
History:
From electronic version of the journal
(End) Ana Fiallos [CDS] 02-Jan-2023