J/A+A/696/A110 CIGALE intermediate redshifts Type II QSOs cand. (Cunha+, 2025)
Exploring the physical properties of type II quasar candidates at intermediate
redshifts with CIGALE.
Cunha P.A.C., Humphrey A., Brinchmann J., Paulino-Afonso A., Bisigello L.,
Bolzonella M., Vaz D.
<Astron. Astrophys. 696, A110 (2025)>
=2025A&A...696A.110C 2025A&A...696A.110C (SIMBAD/NED BibCode)
ADC_Keywords: Surveys ; QSOs ; Active gal. nuclei ; Combined data ; Optical ;
Infrared
Keywords: galaxies: active - galaxies: evolution - galaxies: photometry -
quasars: general - quasars: supermassive black holes
Abstract:
Active galactic nuclei (AGNs) play a vital role in the evolution of
galaxies over cosmic time, significantly influencing their star
formation and growth. As obscured AGNs are difficult to identify due
to obscuration by gas and dust, our understanding of their full impact
is still under study. It is essential to investigate their properties
and distribution, in particular type II quasars (QSO2s), to
comprehensively account for AGN populations and understand how their
fraction evolves over time. Such studies provide critical insights
into the co-evolution of AGNs and their host galaxies.
Following our previous study, where a machine learning approach was
applied to identify 366 QSO2 candidates from SDSS and WISE surveys
(median z∼1.1), we now aim to characterise this QSO2 candidate
sample by analysing their spectral energy distributions (SEDs) and
deriving their physical properties.
We estimated relevant physical properties of the QSO2 candidates,
including the star formation rate (SFR), stellar mass (M*), AGN
luminosity, and AGN fraction, using SED fitting with CIGALE. We
compared the inferred properties with analogous populations in the
semi-empirical simulation SPRITZ, placing these results in the context
of galaxy evolution.
The physical properties derived for our QSO2 candidates indicate a
diverse population of AGNs at various stages of evolution. QSO2
candidates cover a wide range in the SFR-M* diagram, with numerous
high-SFR sources lying above the main sequence at their redshift,
suggesting a link between AGN activity and enhanced star formation.
Additionally, we identify a population of apparently quenched
galaxies, which may be due to obscured star formation or AGN feedback.
Furthermore, the physical parameters of our sample align closely with
those of composite systems and type 2 AGNs from SPRITZ, supporting the
classification of these candidates as obscured AGNs.
This study confirms that our QSO2 candidates, selected via a machine
learning approach, exhibit properties consistent with being AGN-host
galaxies. This method can identify AGNs within large galaxy samples by
considering AGN fractions and their contributions to the infrared
luminosity, going beyond the limitations of traditional
colour-colour selection techniques. The diverse properties of our
candidates demonstrate the capability of this approach to identify
complex AGN-host systems that might otherwise be missed. This shows
the help that machine learning can provide in refining AGN
classifications and advancing our understanding of galaxy evolution
driven by AGN activity with new target selection.
Description:
In this study, a sample of Type 2 Quasar candidates was identified
within the redshift range of 1 to 2 using a machine-learning approach
applied to data from the Sloan Digital Sky Survey (SDSS) and
Wide-field Infrared Survey Explorer (WISE). To further characterise
these candidates, their spectral energy distributions (SEDs) were
analysed using the Code Investigating GALaxy Emission (CIGALE). This
fitting process allowed for the estimation of various physical
properties, including the fractional contribution of the active
galactic nucleus (AGN) to the total infrared luminosity (fracAGN),
stellar mass (M*), star formation rate (SFR), and AGN luminosity. The
results of this analysis are presented in a catalogue of 366 QSO2
candidates, offering insights into the diverse properties of these
obscured AGNs at intermediate redshifts.
This catalogue contains physical properties of Type 2 Quasar
candidates at intermediate redshifts derived using CIGALE SED fitting.
The data includes fractional AGN contribution, stellar mass, star
formation rate, and AGN luminosity, along with their associated
uncertainties. The sample consists of 366 QSO2 candidates selected
from SDSS and WISE surveys with a median redshift of ∼1.1. This
catalogue complements the analysis presented in Cunha et al.
(2024A&A...687A.269C 2024A&A...687A.269C), where the QSO2 candidates were identified using
a machine-learning approach.
File Summary:
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FileName Lrecl Records Explanations
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ReadMe 80 . This file
catalog.dat 228 364 CIGALE QSO2 catalogue
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See also:
V/154 : Sloan Digital Sky Surveys (SDSS), Release 16 (DR16) (Ahumada+, 2020)
III/286 : APOGEE-2 DR17 final allStar catalog (Abdurro'uf+, 2022)
Byte-by-byte Description of file: catalog.dat
--------------------------------------------------------------------------------
Bytes Format Units Label Explanations
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1- 19 F19.15 deg RAdeg Right Ascension (J2000)
21- 34 F14.10 deg DEdeg Declination (J2000)
36- 58 A23 --- SDSS SDSS identifier, SDSSJHHMMSS.ss+DDMMSS.s
60- 77 F18.16 --- fracAGN AGN fraction defined as
Ldust,AGN/(Ldust,galaxy+Ldust,AGN)
79- 96 F18.16 --- e_fracAGN Uncertanty for AGN fraction
98-117 F20.7 Msun M* Stellar mass
119-138 F20.7 Msun e_M* Uncertainty for stellar mass
140-161 E22.17 Msun/yr SFR Star formation rate
163-182 F20.16 Msun/yr e_SFR Uncertainty for Star formation rate
184-205 E22.17 W LAGN AGN Luminosity
207-228 E22.17 W e_LAGN Uncertainty of AGN Luminosity
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Acknowledgements:
Pedro Cunha, pedro.cunha(at)astro.up.pt
(End) Patricia Vannier [CDS] 05-Mar-2025