J/MNRAS/522/3439 AGNs Optical/MIR time series analysis properties (Chen+, 2023)
Mid-infrared dusty torus sizes in active galactic nuclei with Hβ
reverberation mapping.
Chen Y.-J., Liu J.-R., Zhai S., Yao Z.-H., Li Y.-R., Du P., Hu C.,
Guo W.-J., Xiao M., Songsheng Y.-Y., Wang J.-M.
<Mon. Not. R. Astron. Soc. 522, 3439-3457 (2023)>
=2023MNRAS.522.3439C 2023MNRAS.522.3439C (SIMBAD/NED BibCode)
ADC_Keywords: Active gal. nuclei ; Photometry ; Optical ; Infrared ;
Positional data ; Black holes ; H I data ; Magnitudes, absolute ;
Mass loss ; References
Keywords: galaxies: nuclei - galaxies: photometry -
(galaxies:) quasars: supermassive black holes
Abstract:
We compile optical and mid-infrared light curves from the time-domain
surveys (i.e. CRTS, PTF, ZTF, and ASAS-SN) and Wide-field Infrared
Survey Explorer (WISE) archive for a selected sample of active
galactic nuclei (AGNs) with Hβ reverberation mapping (RM)
measurements. We measure the time lags (and thus torus sizes) of
W1 (∼3.4 µm) and W2 (∼4.6 µm) band light curves relative to the
optical one using the MICA method. Through Hβ RM, the sample has
well-measured AGN properties, therefore allowing us to reliably
constrain the relations between torus sizes and AGN properties. We
perform linear regressions for the relations between torus sizes and
5100 Å luminosities (R ∝ Lβ5100) in two cases:
β = 0.5 and β set free. The latter case yields
β ≃ 0.37 ± 0.028 for both W1 and W2 bands, shallower than the
expected value of 0.5, possibly due to the dependence of torus size on
accretion rate. For β = 0.5, by combining with the previous K
band RM measurements, we obtain the characteristic broad-line region
(BLR) and tours sizes following
RBLR : RK : RW1 : RW2 = 1.0 : 6.2 : 9.2 : 11.2. We investigate
the deviations of the W1 and W2 band observed torus sizes from the
corresponding best-fitting relations (with β = 0.5) and find that
they both are correlated with accretion rate. As the accretion rate
increases, the torus sizes tend to be shortened compared to the
anticipated sizes from the best-fitting relations, similar to the
behaviour found in BLRs. Such behaviours can be explained by the
self-shadowing effect of slim discs. This is further supported by
ratios of the W1 and W2 band torus sizes to BLR sizes, which do not
show significant correlations with AGN properties.
Description:
The structure of an AGN is generally believed to consist of, from
inside out, a central supermassive black hole (SMBH), a hot
X-ray-emitting corona, an accretion disc, a broad-line region (BLR), a
dusty torus, and a narrow-line region. In the paradigm of the AGN
unification model the dusty torus plays a key role in classifying AGNs
into two types according to the spectrum whether or not showing broad
emission lines. Type 1 AGNs are those objects observed at low viewing
angles (closer to edge-on than face-on) so that the BLR is directly
observable. Conversely, Type 2 AGNs are observed at high viewing
angles so that the dusty torus obscure the BLR emissions and one only
sees the narrow emission lines in the spectra.
Recently, by combining optical photometric data sets from available
ground-based photometric sky surveys with MIR data sets from the
all-sky WISE had systematically measured sizes of dusty torus. In
these works, the relationship between torus size and luminosity (MIR
R-L relationship) was well investigated, which has the same slope as
the NIR band. Nevertheless, the correlations of torus sizes with other
AGN properties remain unclear. Those relationships can help us better
constrain torus models. Over the past of 40 yr, there have been more
than one hundred AGNs with BLR RM observations from different
campaigns. Most of them had accurate measurements of the AGN
properties, including luminosities at 5100 Å, black hole masses,
and accretion rates. These AGNs are generally bright and show
significant variability, therefore providing a good sample to
systematically measure the sizes of the dusty torus and most
importantly, investigate the relationships between torus sizes and AGN
properties. Moreover, this sample of AGNs with BLR RM also allows us
to make a comparison between the BLR and torus sizes.
As exposed in section 2, AGN sample is mainly based on smaller RM
samples as from the literature as presented in table1.dat which also
contains basic informations as (positions, z, Opt/MIR magnitudes, time
lags) and physical AGN properties as (MBH, dimensionless accretion
rate Mdot, logL5100Å). Next, we focus Opt/MIR light curves data
sets as explained in section 2.2, then we have analysed, compiled and
combined multiple optical, WISE/NEOWISE photometric data to obtain
light curve sets in table3.dat of 11 AGNs selected contained in
table1.dat. Finally, we proceed to time-series analysis to compute
time lags between the optical and MIR light curves from two different
methods ICCF and MICA techniques. The obtained results as (time lags,
widths and uncertainties from distributions) are provided in
table4.dat for 92 AGNs.
File Summary:
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FileName Lrecl Records Explanations
--------------------------------------------------------------------------------
ReadMe 80 . This file
table1.dat 157 101 The sample of reverberation mapped AGNs selected
from literature and their physical properties
table3.dat 91 50028 Optical and MIR photometric light curves for our
selected AGNs sample based on work in sect. 2.2
table4.dat 217 92 *Results of time lag measurements for W1 and
W2 bands time series
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Note on table4.dat: The time lags are given in the observed frame for both
ICCF and MICA techniques .
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See also:
J/A+AS/130/285 : Sample of starburst nucleus galaxies (Contini+ 1998)
J/ApJ/900/58 : Opt-IR LC compilation of DES Stripe 82 quasars (Yang+, 2020)
J/ApJ/886/150 : VRI and K-band light curves of type 1 AGNs (Minezaki+, 2019)
J/ApJ/886/33 : MIR reverberation mapping analysis of 87 z<0.5 PG AGNs
(Lyu+, 2019)
J/ApJ/886/42 : Reverberation mapping & opt. spectra data of AGNs (Du+, 2019)
J/ApJ/876/102 : Reverberation mapping of the Seyfert Zw I 1 (Huang+, 2019)
J/ApJ/876/49 : A 10yr reverberation mapping campaign for 3C273 (Zhang+, 2019)
J/ApJ/840/97 : Optical reverberation mapping campaign of 5 AGNs
(Fausnaugh+, 2017)
J/ApJ/806/22 : SEAMBHs IV. Hβ time lags (Du+, 2015)
J/ApJ/793/108 : SEAMBHs. II. Continuum and Hbeta LCs (Wang+, 2014)
J/ApJ/792/30 : NEOWISE magnitudes for near-Earth objects (Mainzer+, 2014)
J/ApJ/788/159 : 17 Seyfert 1 galaxies light curves (Koshida+, 2014)
J/ApJ/698/895 : Variations in QSOs optical flux (Kelly+, 2009)
J/ApJ/613/682 : AGN central masses and broad-line region sizes
(Peterson+, 2004)
J/ApJS/262/14 : AGNs with Hβ asymmetry. III. 15 PG quasars (Bao+, 2022)
J/ApJS/253/20 : SEAMBHs XII. Reberberation mapping for 15 PG QSOs (Hu+, 2021)
J/ApJS/194/45 : QSO properties from SDSS-DR7 (Shen+, 2011)
VII/258 : Quasars and Active Galactic Nuclei (13th Ed.)
(Veron-Cetty+ 2010)
Byte-by-byte Description of file: table1.dat
--------------------------------------------------------------------------------
Bytes Format Units Label Explanations
--------------------------------------------------------------------------------
1- 21 A21 --- Name AGN name designation (Name)
22 A1 --- n_Name Note on AGNs (1)
24- 25 I2 h RAh Right ascension (J2000)
27- 28 I2 min RAm Right ascension (J2000)
30- 33 F4.1 s RAs Right ascension (J2000)
35 A1 --- DE- Declination sign (J2000)
36- 37 I2 deg DEd Declination (J2000)
39- 40 I2 arcmin DEm Declination (J2000)
42- 43 I2 arcsec DEs Declination (J2000)
45- 50 F6.4 --- z Redshift (z)
52- 56 F5.2 mag Vmag Apparent magnitude in V-band (V) (2)
58- 62 F5.2 mag W1mag Apparent magnitude in W1-band (W1) (2)
64- 68 F5.2 mag W2mag Apparent magnitude in W2-band (W2) (2)
70- 75 F6.2 --- SNRv Signal to noise ratio in V band (S/NV)
77- 82 F6.2 --- SNRw1 Signal to noise ratio in W1 band (S/NW1)
84- 89 F6.2 --- SNRw2 Signal to noise ratio in W2 band (S/NW2)
91- 94 A4 --- Type The subclasses of intermediate Seyfert
galaxies (Type) (3)
96-100 F5.1 d tauHbeta The time lag of the Hβ line
(τ_Hβ)
102-105 F4.1 d e_tauHbeta Lower uncertainties of tau_Hbeta
(errτ_Hβ)
107-110 F4.1 d E_tauHbeta Upper uncertainties of tau_Hbeta
(Errτ_Hβ)
112-115 F4.2 [Msun] log(MBH) Black hole mass (logM./M☉)
117-120 F4.2 [Msun] e_log(MBH) Lower uncertainties of log (MBH)
(errlogM./M☉)
122-125 F4.2 [Msun] E_log(MBH) Upper uncertainties of log (MBH)
(ErrlogM./M☉)
127-131 F5.2 [10-7W] logL5100 Optical luminosity at 5100 Å (logL5100)
133-136 F4.2 [10-7W] e_logL5100 Uncertainties of logL5100 (errlogL5100)
138-142 F5.2 --- dM/dt Dimensionless accretion rates (Mdot)
144-147 F4.2 --- e_dM/dt Lower uncertainties of dM/dt (errMdot)
149-152 F4.2 --- E_dM/dt Upper uncertainties of dM/dt (ErrMdot)
154-157 A4 --- r_Name Reference literature of the AGN (Ref.) (4)
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Note (1): Notes are as follows:
c = Only the measurements from Hu et al.
(2020ApJ...905...75H 2020ApJ...905...75H, Cat. J/ApJ/905/75 and
2021ApJS..253...20H 2021ApJS..253...20H, Cat. J/ApJS/253/20) are adopted.
The Hβ lags were overestimated due to the sparse sampling
in RM campaigns for those objects before 2019
e = Changing-look AGNs were reported by Marin et al.
(2019sf2a.conf..509M)
d = The objects are discarded due to no reliable dust lag
measurements (see sections 2.2 and 3)
Note (2): The magnitudes are calculated using the average of data from
ASAS-SN (V band) and WISE, respectively.
Note (3): The subclasses of intermediate Seyfert galaxies for the objects in
our sample are obtained from Khachikian & Weedman
(1974ApJ...192..581K 1974ApJ...192..581K), Contini et al. (1998A&AS..130..285C 1998A&AS..130..285C,
Cat. J/A+AS/130/285), and Veron-Cetty & Veron (2010A&A...518A..10V 2010A&A...518A..10V,
Cat. VII/258). The narrow-line Seyfert 1 are labelled as S1n.
Note (4): Reference literature are as follows:
1 = Du & Wang 2019ApJ...886...42D 2019ApJ...886...42D, Cat. J/ApJ/886/42
2 = Hu et al. 2021ApJS..253...20H 2021ApJS..253...20H, Cat. J/ApJS/253/20
3 = Hu et al. 2020ApJ...905...75H 2020ApJ...905...75H, Cat. J/ApJ/905/75
4 = U et al. 2022ApJ...925...52U 2022ApJ...925...52U, Cat. J/ApJ/925/52
5 = Huang et al. 2019ApJ...876..102H 2019ApJ...876..102H, Cat. J/ApJ/876/102
6 = Lu et al. 2019ApJ...887..135L 2019ApJ...887..135L, Cat. J/ApJ/887/135
7 = Chen et al. 2023MNRAS.520.1807C 2023MNRAS.520.1807C
8 = Li et al. 2021ApJ...920....9L 2021ApJ...920....9L, Cat. J/ApJ/920/9
9 = Lu et al. 2022ApJS..263...10L 2022ApJS..263...10L, Cat. J/ApJS/263/10
10 = Lu et al. 2021ApJ...918...50L 2021ApJ...918...50L, Cat. J/ApJ/918/50
11 = Hu et al. 2020ApJ...890...71H 2020ApJ...890...71H, Cat. J/ApJ/890/71
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Byte-by-byte Description of file: table3.dat
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Bytes Format Units Label Explanations
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1- 21 A21 --- Name AGN name designation (Name) (1)
23- 30 F8.3 d MJD Modified julian date of observation
JD-2452000 from photometry (JD)
32- 37 F6.3 10-17W/m2/nm Flux Flux density from photometry (Flux)
39- 44 F6.3 10-17W/m2/nm e_Flux Uncertainty of Flux (errFlux)
46- 55 A10 --- Filter Filter name (Filter) (2)
57- 68 A12 --- Survey Survey name (Survey) (3)
70- 77 F8.3 d MJDc ? Modified julian date of observation
JD-2452000 from combined photometry
(JD_combined)
79- 85 F7.3 10-17W/m2/nm Fluxc ? Flux density from combined photometry
(Flux_combined)
87- 91 F5.3 10-17W/m2/nm e_Fluxc ? Uncertainty of Fluxc (errFlux_combined)
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Note (1): AGNs names are as follows:
3C 382 = 5218 occurences in our sample
Ark120 = 4956 occurences in our sample
Mrk 110 = 4573 occurences in our sample
Mrk 335 = 4404 occurences in our sample
Mrk 486 = 6552 occurences in our sample
Mrk 79 = 3251 occurences in our sample
Mrk 817 = 6025 occurences in our sample
NGC 5548 = 3878 occurences in our sample
NGC 7469 = 3723 occurences in our sample
PG 0026+129 = 3959 occurences in our sample
SDSS J100402 = 3489 occurences in our sample
Note (2): Photometric filters are as follows:
V = 12601 occurences in our sample
W1 = 2561 occurences in our sample
W2 = 2531 occurences in our sample
g = 23080 occurences in our sample
r = 6941 occurences in our sample
unfiltered = 2314 occurences in our sample
Note (3): Survey are as follows:
ASAS-SN = 35681 occurences in our sample, filters V, g
CRTS = 2314 occurences in our sample, unfiltered
PTF = 218 occurences in our sample, filter r
WISE/NEOWISE = 5092 occurences in our sample, filters W1, W2(W3, W4)
ZTF = 6723 occurences in our sample, filters r, g, i
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Byte-by-byte Description of file: table4.dat
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Bytes Format Units Label Explanations
--------------------------------------------------------------------------------
1- 21 A21 --- Name AGN name designation (Name)
23 A1 --- n_Name Note on AGN (1)
25- 27 F3.1 --- rmaxW1ICCF For the ICCF method we determine the lag
as the centroid of the ICCF above 80 %
of the peak as rmax of the W1 WISE band
(rmaxW1ICCF)
29- 34 F6.1 d tauW1ICCF Time lag computed with W1 WISE at
2.6-3.4 µm and ICCF method
(tauW1ICCF)
36- 40 F5.1 d e_tauW1ICCF Lower uncertainty of tauW1ICCF
(errtauW1ICCF)
42- 47 F6.1 d E_tauW1ICCF Upper uncertainty of tauW1ICCF
(ErrtauW1ICCF)
49- 51 F3.1 --- rmaxW2ICCF For the ICCF method we determine the lag
as the centroid of the ICCF above 80% of
the peak as rmax of the W2 WISE band
(rmaxW2ICCF)
53- 58 F6.1 d tauW2ICCF Time lag computed with W2 WISE at
3.5-4.6 µm and ICCF method
(tauW2ICCF)
60- 65 F6.1 d e_tauW2ICCF Lower uncertainty of tauW2ICCF
(errtauW2ICCF)
67- 72 F6.1 d E_tauW2ICCF Upper uncertainty of tauW2ICCF
(ErrtauW2ICCF)
74- 79 F6.1 d tauW1MICA Time lag computed with W1 WISE at
2.6-3.4 µm and MICA method
(tauW1MICA) (2)
81- 86 F6.1 d e_tauW1MICA Lower uncertainty of tauW1MICA
(errtauW1MICA)
88- 92 F5.1 d E_tauW1MICA Upper uncertainty of tauW1MICA
(ErrtauW1MICA)
94- 99 F6.1 d tauW2MICA Time lag computed with W2 WISE at
3.5-4.6 µm and MICA method
(tauW2MICA) (2)
101-106 F6.1 d e_tauW2MICA Lower uncertainty of tauW2MICA
(errtauW2MICA)
108-112 F5.1 d E_tauW2MICA Upper uncertainty of tauW2MICA
(ErrtauW2MICA)
114-119 F6.1 d tauW1zcorMICA Time lag computed with W1 WISE at
2.6-3.4 µm and MICA method which adds
a redshift corrected MICA lag
(tauW1zcor_MICA)
121-126 F6.1 d e_tauW1zcorMICA Lower uncertainty of tauW1zcorMICA
(errtauW1zcorMICA)
128-132 F5.1 d E_tauW1zcorMICA Upper uncertainty of tauW1zcorMICA
(ErrtauW1zcorMICA)
134-139 F6.1 d tauW2zcorMICA Time lag computed with W2 WISE at
3.5-4.6 µm and MICA method which adds
a redshift corrected MICA lag
(tauW2zcor_MICA)
141-145 F5.1 d e_tauW2zcorMICA Lower uncertainty of tauW2zcorMICA
(errtauW2zcorMICA)
147-151 F5.1 d E_tauW2zcorMICA Upper uncertainty of tauW2zcorMICA
(ErrtauW2zcorMICA)
153-155 F3.1 [d] log(taud) The logarithm of damped time-scale
parameters (logtau_d) (3)
157-159 F3.1 [d] e_log(taud) Lower uncertainty of log(taud)
(errlogtau_d)
161-163 F3.1 [d] E_log(taud) Upper uncertainty of log(taud)
(Errlogtau_d)
165-168 F4.1 [-] logSigmad The variability amplitudes at long
time-scale (logSigma_d) (3)
170-172 F3.1 [-] e_logSigmad Lower uncertainty of logSigmad
(errlogSigma_d)
174-176 F3.1 [-] E_logSigmad Upper uncertainty of logSigmad
(ErrlogSigma_d)
178-183 F6.1 d SigmaMICAW1 Gaussian MICA width in W1 WISE band
(SigmaMICAW1)
185-190 F6.1 d e_SigmaMICAW1 Lower uncertainty of SigmaMICAW1
(errSigmaMICAW1)
192-197 F6.1 d E_SigmaMICAW1 Upper uncertainty of SigmaMICAW1
(ErrSigmaMICAW1)
199-204 F6.1 d SigmaMICAW2 Gaussian MICA width in W2 WISE band
(SigmaMICAW2)
206-210 F5.1 d e_SigmaMICAW2 Lower uncertainty of SigmaMICAW2
(errSigmaMICAW2)
212-217 F6.1 d E_SigmaMICAW2 Upper uncertainty of SigmaMICAW2
(ErrSigmaMICAW2)
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Note (1): Note are as follows:
b = The objects require long-term detrending when measuring time
lags
c = The time lags are identified to be unreliable due to the poor
response between optical and MIR light curves and/or low quality
of the photometry (see more details in Section 3).
Note (2): The MICA method assumes that light curve is described by the DRW
process and models the transfer function as a sum of a family of
relatively displaced Gaussian. For simplicity, we adopt one Gaussian
in this work. The time lag and its uncertainty are estimated by
50 per cent, 15.87 per cent, and 84.13 per cent quantiles of the
posterior distribution of the Gaussian centre, which is generated by
the Markov Chain Monte Carlo (MCMC) technique.
Note (3): As explicited in section 3.2, we determine the two parameters
(τd and σd) and their uncertainties by fitting the
observed light curve using the MCMC method.
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(End) Luc Trabelsi [CDS] 02-Jul-2026