# Mapping the stellar population and gas excitation of MaNGA galaxies with
megacubes. Results for AGN versus control sample

Rogério Riffel1,2,4, Nicolas D. Mallmann1,2, Sandro B. Rembold3,2, Gabriele S.
Ilha3,2,7, Rogemar A. Riffel3,2, Thaisa Storchi-Bergmann1,2, Daniel Ruschel-
Dutra5, Alexandre Vazdekis 4,6, Ignacio Martín-Navarro4,6, Jaderson S.
Schimoia3,2, Cristina Ramos Almeida4,6, Luiz N. da Costa2, Glauber C. Vila-
Verde2, Lara Gatto1,2
1 Departamento de Astronomia, Instituto de Física, Universidade Federal do Rio
Grande do Sul, CP 15051, 91501-970, Porto Alegre, RS, Brazil
2Laboratório Interinstitucional de e-Astronomia - LIneA, Rua Gal. José
Cristino 77, Rio de Janeiro, RJ - 20921-400, Brazil
3 Departamento de Física, Centro de Ciências Naturais e Exatas, Universidade
Federal de Santa Maria, 97105-900, Santa Maria, RS, Brazil
4 Instituto de Astrofísica de Canarias, Calle Vía Láctea s/n, E-38205 La
Laguna, Tenerife, Spain
5Departamento de Física, Universidade Federal de Santa Catarina, P.O. Box 476,
88040-900, Florianópolis, SC, Brazil
6 Departamento de Astrofísica, Universidad de La Laguna, E-38205, Tenerife,
Spain 7 Universidade do Vale do Paraíba. Av. Shishima Hifumi, 2911, CEP:
12244-000, São José dos Campos, SP, Brazil
E-mail<EMAIL_ADDRESS>
(Accepted XXX. Received YYY; in original form ZZZ)

###### Abstract

We present spaxel-by-spaxel stellar population fits for the $\sim$10 thousand
MaNGA datacubes. We provide multiple extension fits files, nominated as
megacubes, with maps of several properties as well as emission-line profiles
that are provided for each spaxel. All the megacubes are available through a
web interface (https://manga.linea.org.br/ or
http://www.if.ufrgs.br/~riffel/software/megacubes/). We also defined a final
Active Galactic Nuclei (AGN) sample, as well as a control sample matching the
AGN host galaxy properties. We have analysed the stellar populations and
spatially resolved emission-line diagnostic diagrams of these AGNs and
compared them with the control galaxies sample. We find that the relative
fractions of young ($t\leq$56 Myr) and intermediate-age (100 Myr $\leq t\leq$
2 Gyr) show predominantly a positive gradient for both AGNs and controls. The
relative fraction of intermediate-age stellar population is higher in AGN
hosts when compared to the control sample, and this difference becomes larger
for higher [O iii] luminosity AGNs. We attribute this to the fact that extra
gas is available in these more luminous sources and that it most likely
originates from mass-loss from the intermediate-age stars. The spatially
resolved diagnostic diagrams reveal that the AGN emission is concentrated in
the inner 0.5 $R_{e}$ (effective radius) region of the galaxies, showing that
the AGN classification is aperture dependent and that emission-line ratios
have to be taken together with the H$\alpha$ equivalent width for proper
activity classification. We present a composite “BPT+WHAN" diagram that
produces a more comprehensive mapping of the gas excitation.

###### keywords:

galaxies: active – galaxies: evolution – galaxies: ISM – galaxies: star
formation –galaxies: stellar content

††pubyear: 2023††pagerange: Mapping the stellar population and gas excitation
of MaNGA galaxies with megacubes. Results for AGN versus control
sample–LABEL:lastpage

##  1 Introduction

The evolution of the present-day galaxies during cosmic time is the result of
the transformation of molecular gas into stars, the production (and release)
of metals by stellar evolution, and the interaction among these processes and
their environments (e.g. internal and external processes). All these
evolution/transformation processes leave signatures in the observed properties
of galaxies that we can analyze and thus reconstruct the galaxies’ star
formation histories (SFHs). The study of this fossil record is key to our
understanding of the structure, composition, and evolution of galaxies (e.g.
Cid Fernandes et al., 2004; Cid Fernandes et al., 2005; Conselice, 2014;
Tacconi et al., 2020; Sánchez, 2020; Sánchez et al., 2021, and references
therein).

Significant improvement in the understanding of galaxy evolution processes has
been reached in the last decades, primarily due to new results based on large
spectroscopic and imaging surveys such as the Sloan Digital Sky Survey (SDSS,
York et al., 2000). These studies have shown that galaxies can roughly be
divided into passive and star-forming. The passive galaxies are not forming
(or forming at a very low rate) stars and host a red and old stellar
population (e.g. the red-and-dead galaxies), while the star-forming galaxies
are blue, hosting large fractions of young stellar populations. Such bi-modal
behaviour has been observed in a number of studies over the years (e.g. Baldry
et al., 2004; Wetzel et al., 2012; Kauffmann et al., 2003a; Noeske et al.,
2007; van der Wel et al., 2014; Muzzin et al., 2013; Brammer et al., 2009).
However, so far it is not yet clear which mechanisms are stopping the star
formation process and transforming the blue star-forming spiral galaxies into
red-and-dead galaxies, being thus a major challenge in modern astrophysics to
understand the nature of the physical mechanisms quenching star formation in
galaxies.

One mechanism that a number of studies have invoked to explain the suppression
of the star-formation in galaxies is the active galactic nuclei (AGN)
feedback. It can quench star formation by (re)moving and/or heating the gas.
In this context, AGN outflows are considered a process that suppresses star-
formation (e.g. as negative feedback, see Fabian, 2012; King & Pounds, 2015;
Zubovas & Bourne, 2017; Trussler et al., 2020, and references therein). On the
other hand, some models and simulations suggest that these outflows and jets
can in some cases compress the galactic gas, and therefore enhance/trigger the
star-formation (e.g. Rees, 1989; Hopkins, 2012; Nayakshin & Zubovas, 2012;
Ishibashi & Fabian, 2012; Zubovas et al., 2013; El-Badry et al., 2016; Bieri
et al., 2016; Zubovas et al., 2013; Zubovas & Bourne, 2017; Wang & Loeb, 2018;
Gallagher et al., 2019; Bessiere & Ramos Almeida, 2022). Additionally,
cosmological simulations (e.g. Springel et al., 2005; Vogelsberger et al.,
2014; Crain et al., 2015) without the inclusion of feedback effects effects do
not reproduce the galaxies luminosity function, underestimate the ages of the
most massive galaxies (see Figs. 8 and 10 of Croton et al., 2006) and do
provide a limited insight into the nature and source of the feedback processes
(e.g. AGN or SN dominated Schaye et al., 2015).

To better understand this kind of process it is very important to study the
galaxies’ properties in a spatially resolved way so that the AGN effects can
be probed at different locations in the host galaxies. This can be done via
the use of Integral Field Units (IFUs), which allow the study of ionization
sources across the galaxies, the interplay among the global/integrated
properties of galaxies with the local (spatially resolved) ones, and the link
between these two types of relations as well as the spatial (radial)
distribution of such properties (Sánchez, 2020; Sánchez et al., 2021).

However, extracting information from IFU data can be complex (Steiner et al.,
2009), and hence the analysis of such data with different techniques is
desirable. Over the years, our AGNIFS team has developed a series of studies
analysing IFU data (e.g. Storchi-Bergmann et al., 2009, 2012; Mallmann et al.,
2018; Schnorr-Müller et al., 2021; Riffel et al., 2011, 2017, 2018, 2021a,
2021b, 2021c, 2022; Ilha et al., 2022) and methods to analyze this type of
data.

In the present paper, we present megacubes (Mallmann et al., 2018) with our
fitting procedures for the $\sim$ 10 000 datacubes of the Mapping Nearby
Galaxies at Apache Point Observatory (MaNGA) survey (Bundy et al., 2015), as
well as an analysis of the stellar content of the final AGN sample and a
matched control sample (Rembold et al., 2017). This paper is structured as
follows: in section 2 we present the updated samples. The fitting procedures
and the megacubes are described in section 3. The results are presented and
discussed in section 4 and final remarks are made in section 5. We have used
throughout the paper $H_{0}=73$ km s-1 Mpc-1 (Riess et al., 2022).

##  2 data

The data used here are those provided by the fourth-generation Sloan Digital
Sky Survey (SDSS IV) sub-project Mapping Nearby Galaxies at Apache Point
Observatory (MaNGA, Bundy et al., 2015). The survey has provided optical IFU
spectroscopy ($3600$ Å-$10400$ Å), high quality data, of $\sim 10,010$ nearby
galaxies (with $\langle z\rangle\,\approx\,0.03$). The observations were
carried out with fiber bundles of different sizes (19-127 fibers) covering a
field of $12^{\prime\prime}$ to $32^{\prime\prime}$ in diameter. MaNGA
observations are divided into “primary” and “secondary” targets, the former
was observed up to $1.5$ effective radius ($R_{e}$) while the latter was
observed up to $2.5\,R_{e}$. For more details, see Drory et al. (2015); Law et
al. (2015); Yan et al. (2016a, b).

We have fitted the stellar population and emission lines for the 10 010 high
quality unique111We have discounted the Coma, IC342, M31, and globular cluster
targets, and cubes with “UNUSUAL” or “CRITICAL” data quality flags, as well as
repeated observations. data cubes available in the MaNGA final data release
(DR17, Abdurro’uf et al., 2022; Aguado et al., 2019; Blanton et al., 2017;
Bundy et al., 2015; Belfiore et al., 2019; Westfall et al., 2019; Cherinka et
al., 2019; Wake et al., 2017; Law et al., 2015, 2016, 2021; Yan et al., 2016b,
a; Drory et al., 2015; Gunn et al., 2006; Smee et al., 2013).

###  2.1 Active Galactic Nuclei and Control Samples in MaNGA

In this work, we update and expand the sample of AGN hosts optically
identified in the MaNGA survey by Rembold et al. (2017). We have cross-matched
all galaxy data cubes observed in DR17 with the SDSS-III spectroscopic data
from DR12 (Alam et al., 2015). The fluxes and equivalent widths of the
emission lines H$\beta$, [O iii]$\lambda$5007, H$\alpha$ and [N
ii]$\lambda$6583, measured in the SDSS-III integrated nuclear spectrum, were
drawn from Thomas et al. (2013). We then classified the galaxies according to
the ionizing source of the gas using both the [N ii] based Baldwin et al.
(1981, BPT) diagram222It is worth mentioning that this diagram identify LINERs
that would be missed by other diagrams (Agostino et al., 2021). and the WHAN
diagram (Cid Fernandes et al., 2010), following Rembold et al. (2017). A
galaxy is confirmed as an optical AGN host if it is located simultaneously in
the Seyfert/LINER region of both the BPT and the WHAN diagrams. We refer to
Rembold et al. (2017) for more details on the selection of AGN hosts. This
methodology results in 298 confirmed optical AGN hosts in MaNGA.

As in Rembold et al. (2017), we have defined a sample of non-active control
galaxies comparable to the AGN hosts in terms of redshift, stellar mass and
morphology. We selected from DR17, as potential control galaxies, those
presenting or not detectable emission lines in their SDSS-III fiber spectra,
except those already classified as AGN hosts (see above). Besides the objects
lacking detectable emission lines altogether, potential control galaxies are
those located, within the uncertainties, in the star-forming region of the BPT
diagram, or in other regions of the BPT diagram if the WHAN diagram discards
ionization from an AGN. These comprise therefore objects ionized by young
stars or by hot, low-mass evolved stars (HOLMES). For each AGN host, we then
select a preliminary subsample of galaxies from the list of potential control
objects that match their redshifts and stellar masses at deviations lower than
30 percent deviation. Such preliminary galaxies (typically a couple hundred
for each AGN host) were then visually inspected, and the two objects that best
match the morphology and axial ratio of the AGN host were selected as their
control “partners”. For five AGN hosts (MaNGA id 1-37440, 1-189584, 31-115,
1-641156 and 1-300461), no control “partners” were selected, either because
their redshifts are too close to the redshift limits of the MaNGA survey (e.g.
1-189584 at $z=0.0037$) or because their morphologies are not reproduced by
other objects in the survey (e.g. 1-641156).

Our final sample is therefore composed of 293 AGN hosts paired with two non-
active control galaxies each. The control sample comprises 492 unique objects,
less than the expected 586, due to multiple occurrences of the same control
galaxy as control “partners” of more than one AGN host. Table 1 and Table 2
list relevant parameters of the AGN hosts and control galaxies respectively.

As a verification of the quality of the control sample selection process, we
confirmed using the Kolmogorov-Smirnov test that the distributions of stellar
mass and redshift are indistinguishable between AGN hosts and control
galaxies. We show the distribution of redshift, stellar mass, stellar velocity
dispersion, and [O iii] luminosity in Fig. 1. Also, according to the Galaxy
Zoo project (Lintott et al., 2011), 32%, 60% and 5% of our AGN hosts are
elliptical, spiral, and merging galaxies respectively. This morphological
distribution is well matched by the control sample, comprised of 35%, 61% and
2% of elliptical, spiral and merging galaxies respectively. We have also
compared the numerical T-Type between AGN hosts and control galaxies from
Vázquez-Mata et al. (2022) and found that both distributions are very similar
(and indistinguishable as verified using the Kolmogorov-Smirnov test). For
more details on the control sample selection see Rembold et al. (2017).

Figure 1:  Distributions of redshift, stellar mass, stellar velocity dispersion, and [O iii] luminosity of the AGN host sample (blue) and the control sample (orange). These properties are from SDSS single fiber observations. The AGN and the control sample present markedly different distributions of L([O iii]), which is expected (see Rembold et al., 2017, for a discussion), but have other parameters similar between the two samples.  Table 1: Parameters of AGN in MaNGA-DR17. (1) galaxy identification in the MaNGA survey; (2)-(3): RA/DEC (2000) in degrees; (4) spectroscopic redshift from SDSS-III; (5): integrated absolute $r$-band magnitude from SDSS-III; (6): logarithmic stellar mass in units of $M_{\odot}$; (7)-(8) morphological classification in the Hubble scheme and numerical T-Type from Vázquez-Mata et al. (2022); (9): [OIII] luminosity in units of $10^{40}$ erg s-1. We only present here the first 6 lines of this table; the full version is available with the electronic version of the paper. mangaID | RA | DEC | $z$ | $M_{r}$ | log $M^{\star}/M_{\odot}$ | Type | T-Type | $L(\rm{[OIII]})$
---|---|---|---|---|---|---|---|---
(1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9)
1-558912 | 166.129410 | 42.624554 | 0.1261 | -20.46 | 11.25 | E | -5 | 56.82$\pm$1.25
1-150947 | 183.263992 | 51.648598 | 0.0849 | -20.23 | 10.96 | Sb |  -3 | 53.82$\pm$1.75
1-39376 |  111.999101 | 13.742678 | 0.0567 | -19.76 | 10.92 | SBb |  -3 | 43.88$\pm$0.65
1-295542 | 246.255981 | 24.263155 | 0.0503 | -19.34 | 10.73 | Edc | -5 | 39.10$\pm$0.87
1-458092 | 203.190094 | 26.580376 | 0.0470 | -18.08 |  19.64 | E | -5 | 35.30$\pm$1.04
1-211165 | 248.628647 | 37.695442 | 0.0991 | -21.04 | 11.24 | S0 | -2 | 33.83$\pm$0.57
Table 2: Control sample parameters. (1) identification of the AGN host associated to the control galaxy; (2)-(10) same as (1)-(9) of Table 1 for the control sample. We only present here the first 12 lines of this table; the full version is available with the electronic version of the paper. AGN mangaID | mangaID | RA | DEC | $z$ | $M_{r}$ | log $M^{\star}/M_{\odot}$ | Type | T-Type | $L(\rm{[OIII]})$
---|---|---|---|---|---|---|---|---|---
(1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | (10)
1-558912 | 1-71481 | 117.456001 | 34.883911 | 0.1312 | -20.95 | 11.70 | E | -5 |  10.10$\pm$0.20
| 1-72928 | 127.256485 | 45.016773 | 0.1270 | -20.62 | 11.52 | E | -5 |  10.09$\pm$0.23
1-150947 | 1-338555 | 114.374146 | 41.737232 | 0.0893 | -17.42 |  18.99 | E | -5 |  12.05$\pm$0.39
| 1-248214 | 240.347031 | 43.085041 | 0.0600 | -19.80 | 10.97 | Edc | -5 |  10.41$\pm$0.14
1-39376 | 1-285052 | 199.061493 | 47.599365 | 0.0573 | -19.77 | 10.85 | SBab |  -2 |  10.11$\pm$0.03
| 1-283662 | 191.217728 | 42.422195 | 0.0536 | -19.27 | 10.66 | SB0a |  -0 |  10.00$\pm$0.00
1-295542 | 1-94228 | 248.018265 | 47.764759 | 0.0494 | -19.19 | 10.58 | Sa |  -1 |  10.30$\pm$0.06
| 1-92774 | 243.407715 | 49.069408 | 0.0552 | -19.28 | 10.75 | Edc | -5 |  10.39$\pm$0.23
1-458092 | 1-38319 |  151.609463 | -0.311706 | 0.0376 | -17.24 |  19.76 | Edc | -5 |  10.00$\pm$0.00
| 1-323888 | 245.686630 | 32.659161 | 0.0410 | -18.64 |  19.54 | S0 | -2 | 11.66$\pm$0.31
1-211165 | 1-55272 | 146.661270 |  12.658917 | 0.0933 | -20.90 | 11.49 | S0 | -2 |  11.24$\pm$0.27
| 1-262966 | 229.138367 | 32.243935 | 0.0913 | -20.79 | 11.22 | Edc | -5 |  10.16$\pm$0.16

##  3 Fitting procedures

###  3.1 Stellar Population

In this section, we describe the procedures adopted in the stellar population
fits.

####  3.1.1 Starlight

To perform a full spectral fitting stellar population synthesis on our
datacubes we employed the starlight code (Cid Fernandes et al., 2005; Cid
Fernandes, 2018) which combines the spectra of a base of elements of
$N_{\star}$ simple stellar population (SSP) template spectra $b_{j,\lambda}$,
weighted in different proportions, in order to reproduce the observed spectrum
$O_{\lambda}$. For this modeling, the observed and modeled spectra
$M_{\lambda}$ are normalized at a user-defined wavelength $\lambda_{0}$. The
reddening is given by the term
$r_{\lambda}=10^{-0.4(A_{\lambda}-A_{\lambda_{0}})}$, weighted by the
population vector $x_{j}$ (which represents the fractional contribution of the
$j$th SSP to the light at the normalisation wavelength $\lambda_{0}$), and
convolved with a Gaussian distribution $G(v_{\star},\sigma_{\star})$ to
account for velocity shifts $v_{\star}$, and velocity dispersion
$\sigma_{\star}$. Each model spectra can be expressed as:

$M_{\lambda}=M_{\lambda_{0}}\left[\sum_{n=1}^{N_{\star}}x_{j}\,b_{j,\lambda}\,r_{\lambda}\right]\otimes
G(v_{\star},\sigma_{\star}),$ (1)

where $M_{\lambda_{0}}$ is the flux of the synthetic spectrum at the
wavelength $\lambda_{0}$. To find the best parameters for the fit, the code
searches for the minimum of
$\chi^{2}=\sum_{\lambda_{i}}^{\lambda_{f}}[(O_{\lambda}-M_{\lambda})\omega_{\lambda}]^{2}$,
where $\omega_{\lambda}$ is the inverse of the error, using a simulated
annealing plus Metropolis scheme.

The base of elements we use is the $GM$ described in Cid Fernandes et al.
(2013, 2014) that is constructed using the Miles (Vazdekis et al., 2010) and
González Delgado et al. (2005) models. We have updated it with the Miles V11
models (Vazdekis et al., 2016). We used 21 ages (t= 0.001, 0.006, 0.010,
0.014, 0.020, 0.032, 0.056, 0.1, 0.2, 0.316, 0.398, 0.501 0.631, 0.708, 0.794,
0.891, 1.0, 2.0, 5.01, 8.91 and 12.6 Gyr) and four metallicities (Z= 0.19,
0.40, 1.00 and 1.66 Z⊙). Following Cid Fernandes et al. (2004) we have also
added to the base of elements a power law of the form
$F_{\nu}\propto\nu^{-1.5}$ to account for the contribution of a possible AGN
featureless continuum (FC), observed directly or as scattered light (see also
Koski, 1978; Cid Fernandes et al., 2005; Martins et al., 2013; Riffel et al.,
2009; Riffel et al., 2021a, 2022, for discussion on the FC contribution). It
is worth mentioning that FC is only considered in the light fractions being
removed from the calculations of stellar properties (e.g. mean ages and
metallicities, see § 3.1.3).

Finally, the normalization flux at $\lambda_{0}$ was adopted to be the mean
value between 5650Å and 5750Å. The reddening law we used was that of Cardelli
et al. (1989) and the synthesis was performed in the spectral range from
$3700$ Å to $6900$ Å.

####  3.1.2 Urutau

starlight is not developed to work with datacubes or fits  files, therefore we
have developed an in house software: urutau. It was developed in Python to
handle input and output data from multiple sources via a modular pipeline
execution. The modules of the pipeline can be swapped depending on the user
case (such as datacube extraction method, spectral fitting software, desired
scientific result, etc.). Urutau was also planned with parallel processing
capability in order to execute many instances of a closed source executable
(such as starlight) for different sets of data. The code is available at:
https://github.com/ndmallmann/urutau.

####  3.1.3 Stellar population megacube modules

To improve the management of the starlight inputs and outputs, the
organization of the results and the analysis, we developed a series of modules
to run under urutau and appended the data-products to the original datacube.
These modules are as follows:

a) libMaNGA: This library is used to prepare and convert the MaNGA datacubes
to the format expected by starlight. The main steps are as follows (Mallmann
et al., 2018) :

  1. 1.

Filtering of the datacubes using a two-dimensional butterworth filter to
remove spurious data and increase the signal-to-noise ratio. It does not
require any addition of adjacent spaxel, thus allowing to a better exploration
of the spatial resolution. A better description of this technique can be found
in Riffel et al. (2016);

  2. 2.

Correct each spaxel for Galactic reddening using the Schlegel et al. (1998)
extinction maps and the Cardelli et al. (1989) reddening law;

  3. 3.

Correct for redshift using the SDSS-III redshift values available in the
drpall tables of the MaNGA database;

  4. 4.

Estimation of the signal-to-noise ratio (SNR) maps in the wavelength range
5650-5750 Å for every spaxel. We provide masks with cuts of SNR values of 3,
5, and 10, as well as the SNR map. We have performed our fits for spaxels with
SNR$\geq$1\. Note, however, that any cutoff in SNR larger than 1 can be
applied by the user since we provide the SNR maps.

b) libStarlightCaller: This module is used to actually run starlight with the
configuration listed in § 3.1.1. In other words, it sets up all the
configuration files needed for the fits and runs starlight for each individual
spaxel.

c) libCreateMegacube: With this function we derive additional parameters like:
mean ages and metallicities, star formation rates, and binned population
vectors from the starlight output as follows:

Binned population vectors:

Since small differences in ages are washed away due to noise effects, one
coarser, but more robust way is to define binned population vectors (e.g. Cid
Fernandes et al., 2005; Riffel et al., 2009). We included in our megacubes the
following binned population vectors (where y=young, i=intermediate, and
o=old):

FC1.50:

The percent contribution (at $\lambda_{0}$) of a featureless component of the
form $F_{\nu}\sim\nu^{-1.5}$;

xyy_light (mass):

Light (mass) binned population vector in the age range $t\leq$ 10 Myr;

xyo_light (mass):

Light (mass) binned population vector in the age range 14 Myr $<t\leq$ 56 Myr;

xiy_light (mass):

Light (mass) binned population vector in the age range 100 Myr $<t\leq$ 500
Myr;

xii_light (mass):

Light (mass) binned population vector in the age range 630 Myr $<t\leq$ 800
Myr;

xio_light (mass):

Light (mass) binned population vector in the age range 890 Myr $<t\leq$ 2.0
Gyr;

xo_light (mass):

Light (mass) binned population vector in the age range 5.0 Gyr $<t\leq$ 13
Gyr;

Star Formation Rates via stellar population fit:

the star formation rate obtained from the stellar population fit
($SFR_{\star}$) over an user-defined age interval ($\Delta
t=t_{j_{f}}-t_{j_{i}}$) is also computed in this module. This can be computed
since the SSPs model spectra are in units of L⊙ Å${}^{-1}M_{\odot}^{-1}$, and
the observed spectra ($O_{\lambda}$) are in units of $\rm erg/s/cm^{2}/$Å. The
SFR⋆ over the chosen $\Delta t$ can be computed assuming that the mass of each
base component ($j$) which has been processed into stars can be obtained as:

$M_{\star,j}^{\rm ini}=\mu_{j}^{\rm ini}\times\frac{4\pi d^{2}}{3.826\times
10^{33}},$ (2)

where $M_{\star,j}^{\rm ini}$ is given in $M_{\odot}$, $\mu_{j}^{\rm ini}$ is
associated with the mass that has been converted into stars for the $j$-th
element and its flux. This parameter is given in $M_{\odot}\,{\rm
ergs^{-1}cm^{-2}}$; $d$ is the luminosity distance to the galaxy in cm and
3.826$\times 10^{33}$ is the Sun’s luminosity in erg s-1. Thus, the SFR over
the $\Delta t$ as defined above can be obtained from the equation:

${\rm SFR_{\star}}=\frac{\sum_{j_{i}}^{j_{f}}M_{\star,j}^{\rm ini}}{\Delta
t}.$ (3)

For more details see Riffel et al. (2021a). Here we computed the ${\rm
SFR_{\star}}$ over the last ($\Delta t$) 1 Myr, 5.6 Myr, 10 Myr, 14 Myr, 20
Myr, 32 Myr, 56 Myr, 100 Myr, and 200 Myr (they were labelled as SFR_1, SFR_5,
SFR_10, SFR_14, SFR_20, SFR_30, SFR_56, SFR_100, and SFR_200, respectively).

Mean ages and metallicities:

Following Cid Fernandes et al. (2005) we have computed the mean ages (the
logarithm of the age, actually) for each spaxel weighted by the stellar light

$\langle{\rm
log}t_{\star}\rangle_{L}=\displaystyle\sum^{N_{\star}}_{j=1}x_{j}{\rm
log}t_{j},$ (4)

and weighted by the stellar mass,

$\langle{\rm
log}t_{\star}\rangle_{M}=\displaystyle\sum^{N_{\star}}_{j=1}\mu_{j}{\rm
log}t_{j}.$ (5)

The light-weighted mean metallicity is defined as

$\langle Z_{\star}\rangle_{L}=\displaystyle\sum^{N_{\star}}_{j=1}x_{j}Z_{j},$
(6)

and the mass-weighted mean metallicity is defined by:

$\langle
Z_{\star}\rangle_{M}=\displaystyle\sum^{N_{\star}}_{j=1}\mu_{j}Z_{j}.$ (7)

Note that both definitions are limited by the age and metallicities range used
in our elements base. They are presented under the Mage_L, Mage_M, MZ_L, and
MZ_M keywords.

Additional parameters:

we also present a set of additional fitting parameters that are included in
the megacubes, such as optical extinction (Av), the present mass in stars
(M*), masses processed in stars (M*in), normalisation flux (F_Norm), stellar
dispersion velocity (Sigma_star), stellar rotation velocity (vrot_star),
percentage mean deviation (Adev), reduced $\chi^{2}$ (ChiSqrt), and signal-to-
noise ratio on normalisation window (SNR).

The final product after the procedures described above is a complete set of
Flexible Image Transport System (fits) files for the entire MaNGA sample; this
constitutes what we refer to as megacubes. The results obtained after our
fitting are appended as multiple extensions into the original MaNGA datacubes.
The additional extensions added are as follows:

BaseAgeMetal:

The ages and metallicities of the SSPs used in the base of elements.

PopBins:

In this extension we store the synthesis parameters directly computed by
starlight as well as properties derived in the libCreateMegacube module (see §
3.1.3)

PoPVecsL (M):

Original (e.g. not binned) population vectors in light fractions (mass
fractions).

The starlight output observed flux (FLXOBS), synthetic flux (FLXSYN), and
weights (WEIGHT), as well as continuum signal-to-noise ratio masks, are also
stored on the megacubes.

###  3.2 Emission line fitting

Our megacubes, by construction, include absorption-free emission-line
datacubes. We have used these datacubes to fit the most common emission lines
in the optical region. To this purpose we use the
ifscube333https://github.com/danielrd6/ifscube Python package (Ruschel-Dutra,
2020; Ruschel-Dutra et al., 2021) to fit the profiles of the most prominent
emission-line, namely: H$\beta$, [O iii]$\lambda\lambda$4959,5007, He
i$\lambda$5876, [O i]$\lambda$6300, H$\alpha$ [N ii]$\lambda\lambda$6548,6583
and [S ii]$\lambda\lambda$6716,6731. The line profiles are fitted with
Gaussian curves by adopting the following constraints: (i) the width and
centroid velocities of emission lines from the same parent ion are constrained
to the same value, just He i$\lambda$5876 and [O i]$\lambda$6300 are kept free
during the fit; (ii) the [O iii]$\lambda$5007/$\lambda$4959 and [N
ii]$\lambda$6583/$\lambda$6548 flux ratios are fixed to their theoretical
values of 2.98 and 3.06, respectively; (iii) the centroid velocity is allowed
to vary from –300 to 300 km s-1 for [S ii] lines and –350 to 350 km s-1 for
the other lines relative to the velocity obtained from the redshift of each
galaxy (listed in the MaNGA Data Analysis Pipeline - DAP); and (iv) the
observed velocity dispersion of all lines is limited to the range 40–300 km
s-1. In a few sources better fits of the observed profiles are obtained when
including a broad component for H$\alpha$ and H$\beta$444No changes in the
activity classification are found when a broad component is included since,
even in cases where a broad component is evident, the line fluxes are well
represented by a single component. For completeness, we have made available
the megacubes for these sources with and without the broad component. . In
addition, we include a first-order polynomial to reproduce the local
continuum.

Instead of relying on the minimization of a residual function, kinematic
constraints were enforced by construction, as each emission line in the same
kinematic group inherits the same exact parameter for line width and centroid
velocity, see Ruschel-Dutra et al. (2021) for additional details.
Additionally, all the lines have been fitted with a single Gaussian. This
approach has the advantage of reducing the number of free parameters that are
perceived by the fitting algorithm.

The output of IFSCube is in the form of a Multi-Extension FITS file (MEF),
with each extension storing a different result from the fitting process, a
copy of the input, or software-specific parameters for later reference. The
first four extensions are in the same shape of the input data, "FITSPEC"
representing the observed spectra (restricted to the requested wavelength
window), the result of the pseudo continuum fit is in "FITCONT", "STELLAR"
stores the given stellar spectra and "MODEL" stores the modelled emission
spectrum. If no stellar spectra were given as input, the corresponding
extension is filled with zeros.

Next there is the "SOLUTION" extension which stores the best parameters for
all the emission lines considered, and also the reduced chi-squared of the
model as a whole. The shape is that of the input data for the spatial part,
with each parameter occupying a different plane. For instance, if three
Gaussian curves were fit to a 50x50 data cube, then there should be 9
independent parameters for the whole model, and another plane for the chi-
squared, resulting in a 50x50x10 array.

The initial guess for each parameter is stored in the "INIGUESS" extension,
also in the form of a three-dimensional array, with a set of parameters for
each spaxel. This is useful only when using the option to update the initial
guess based on results from previous spaxels. Extensions 7 through 10
("FLUX_D", "FLUX_M", "EQW_M" and "EQW_D") store the fluxes and equivalent
widths of each line, using two different approaches. The first one is a direct
trapezoidal integration of the observed spectra, minus the stellar component
and pseudo-continuum, while the second is an integration over the modelled
emission line. The final status of each spaxel is specified by an integer code
and stored in the "STATUS" extension. A bi-dimensional mask, which combines
both the input mask and IFSCube’s internal assessment of the spaxel quality,
is stored in the extension "MASK2D".

The last three extensions are tables, as opposed to all the previous ones
which consist of images. Extension "SPECIDX" stores the sequence in which the
spaxels were processed. Parameter names in the exact order that they apper in
the "SOLUTION" extension are specified by the table in the "PARNAMES"
extension. Finally, the "FITCONFIG" extensions keeps a copy of the input
configuration.

All these extensions regarding the emission line fitting have been appended to
the megacubes.

##  4 Results and discussion

###  4.1 megacubes

As described in the previous sections we have fitted the stellar population
and derived their properties for $\sim$ 10 000 galaxies, being thus the main
purpose of this paper to make available for the community, in an easy-to-use
way, the maps of the stellar population and emission lines of all spaxels on
the $\sim$10 thousand MaNGA datacubes. This final product is what we call
megacubes.

In order to facilitate the inspection of the large number of properties that
can be derived from the analysis of the spectra available in the IFU data
cube, a customized viewer was developed in collaboration with LIneA’s IT team.
All the megacubes are available through a web interface
(https://manga.linea.org.br/ or
http://www.if.ufrgs.br/~riffel/software/megacubes/), where each one of them
can be downloaded and/or inspected via a number of different plots, which can
be interacted with. Additionally, we do also provide a table with the mean or
integrated values (integrated for $M\star$ and emission-line fluxes) of the
circularised maps for many properties for all the galaxies for different
radial values as follows555We call attention to the equivalent width values
since they are the mean value of the different strengths measured over the
field. They should be taken as an upper limit: spaxels containing emission
lines below the detectability limit contribute only to the continuum, thus
reducing the total line flux, while in an integrated spectrum over the same
field these “spaxels” contribute also to the line flux.: r_int_0 =
R$<0.5\,R_{e}$; r_int_1 = $0.5\,R_{e}\leq$ R$<1.0\,R_{e}$; r_int_2 =
$1.0\,R_{e}\leq$ R$<1.5\,R_{e}$, r_int_3 = $1.5\,R_{e}\leq$ R$<2.0\,R_{e}$;
r_int_4 = R$<0.5\,{\rm kpc}$; r_int_5 = $0.5\,{\rm kpc}\leq$ R$<1.0\,{\rm
kpc}$; r_int_6 = $1.0\,{\rm kpc}\leq$ R$<2.0\,{\rm kpc}$; r_int_7 = $2.0\,{\rm
kpc}\leq$ R$<5.0\,{\rm kpc}$, and r_int_8 = $5.0\,{\rm kpc}\leq$ R$<10.0\,{\rm
kpc}$. These mean/integrated properties have been derived considering the
values where the continuum SNR$\geq$10 (SN_MASKS_10) and, for the emission-
line related properties we have considered an additional threshold of $\geq
3\sigma$ detection.

As a highlight of the science that can be done with these megacubes, we
analyze here the stellar populations and the spatially resolved emission-line
diagnostic diagrams of the final set of AGNs on the MaNGA survey and compare
them with the results obtained for the control sample we have described in §
2.1.

###  4.2 Mapping the stellar populations in MaNGA AGNs

The megacubes have a large number of output parameters, but here we are mostly
interested in $x_{j}$ – the fractional contribution of each SSP to the total
light at $\lambda_{norm}$ – as they represent the star formation history of
the galaxy. Following Mallmann et al. (2018), we show example maps of these
parameters, together with the mean age and the reddening maps in Fig. 2 (late-
type AGN) and Fig. 3 (early-type AGN). It is worth mentioning that we have
changed the methodology applied in Mallmann et al. (2018) for the computation
of the profiles using a circularized deprojection over the entire FoV, instead
of only a region along the major axis of the galaxies as in Mallmann et al.
(2018).

Figure 2:  Comparison of the stellar population properties between a late-type
AGN and its controls. Left side panels - Top set of panels: SDSS image (the
MaNGA field is indicated in magenta). Second row: RGB image using a
composition of the binned population vectors [blue: young (xyy+xyo = xy);
green: intermediate age (xiy+xii+xio = xi); red: old (xo)]. Bottom set of
panels: From top to bottom: visual extinction ($A_{\rm V}$), $\rm X_{Y}$, $\rm
X_{I}$, $\rm X_{O}$ and mean age ($<t>$) maps. For display purposes, we used
tick marks separated by 5$"$. The solid horizontal line in the $A_{\rm V}$
maps represents 1 $R_{e}$. Right side panels - Top: summary table with the
mean gradient values for each property in 3 different $R_{e}$ ranges. Bottom:
average radial profiles for AGN hosts (red) and control galaxies (blue and
green). The units of these profiles are the same as in the maps on the left
side. The shaded area represents $1\,\sigma$ standard deviation. This figure
is similar to those of Mallmann et al. (2018), but the profiles presented here
have been calculated using new circularised deprojected maps.  Figure 3:  Same
as Fig. 2 but for an early-type AGN and its control galaxies.

####  4.2.1 2D maps analysis

In order to show in a qualitative way how the different stellar population
components are distributed along the FoV we have constructed RGB images (Figs.
2 and 3) of the galaxies that were created by assigning three colors (Red,
Green, and Blue) to the binned population vectors: Red represents the old (xo:
5 Gyr $<t\leq$ 13 Gyr), green the intermediate age (xiy+xii+xio = xi: 100 Myr
$<t\leq$ 2 Gyr), and blue the young (xyy+xyo = xy: $t\leq$ 56Myr) stellar
populations (see § 3.1.3 for the age bin definitions). We also show (left
side) the maps for the mean properties and the radial profiles (right side) of
these properties. In addition, we show the mean gradient values in 3 different
radial steps (0.0 to 0.5Re, 0.5 to 1.0Re, and 0.0 to 1.0 Re). These gradients
have been computed by linear regression over the points within the radial
steps and the values quoted are the angular coefficients for the AGN and for
the two controls galaxies.

From this exercise, one can see that the old population is more centrally
concentrated in the late-type sources, while the gradient is shallower in the
early-type ones, but in both cases, a decreasing value is observed with
increasing radius. In addition, both types show significant contributions of
the intermediate age with an outwards increasing gradient, while the young
component is distributed along the full FoV. This behavior is seen for almost
all sources in our sample (see supplementary material for the figures for the
other galaxies).

####  4.2.2 Gradients

The quantitative comparison of individual sources is very difficult, and
therefore we show the general behaviour of the stellar population parameters
along the galaxies and the comparison between AGN and controls. We show their
variations with radii and with [O iii] luminosity from Fig. 4 to Fig. 8. The
L([O iii]) bins where taken from Rembold et al. (2017). We have also separated
the sample into early- and late-type galaxies. The Hubble types were taken
from Vázquez-Mata et al. (2022) and are listed in Tab. 1. We have classified
the sources with the parameter T-Type $\leq$ 2 as early-type and with 2$<$
T-Type $<$ 10 as late-type, while objects with T-Type $\geq$ 10 were
classified as Irregular and are not used in our analysis.

What clearly emerges from this choice is that for the younger ages ($t<$56
Myr) no differences are detected when comparing AGNs hosts and control
galaxies. However, when focusing on R $<$ 0.5 Re the values for this component
seem to be biased towards a slight increase on the mean values for AGN hosts
with higher [O iii] luminosity, when compared to their corresponding control
galaxy counterparts. In addition, this component’s contribution is generally
low ($\lesssim$ 10 percent) with the possible difference between AGNs and
controls being around 2 percent and within the uncertainties. For the less
luminous AGNs a positive gradient is seen, while a slightly negative gradient
is seen for the higher luminosity ones. This suggests that the more luminous
AGNs may be facing a rejuvenation when compared with the less luminous ones.
However, we call attention to the fact that a reddened young SP can mimic an
AGN featureless continuum component (see Cid Fernandes et al., 2004; Cid
Fernandes et al., 2005; Riffel et al., 2009; Riffel et al., 2022, for more
details), thus, this young component has to be analyzed together whit the FC.
In Fig. 5 we show the behavior of the FC component as a function of R and L([O
iii]). In general, the values are very small ($\lesssim$5 percent), and
slightly increase outwards666We attribute the increase in the value of FC with
R, for both AGNs and controls, as due to an increase of the noise in the
spaxels, such that the absorption features are washed away and the FC
component becomes hardly distinguishable from the underlying stellar continuum
(see Cid Fernandes et al., 2014; Cid Fernandes, 2018, and references).. For
the two higher luminosity bins a higher value for FC (of $\sim$10 percent) is
seen for R$<$0.5 Re , and it becomes quite different than the contributions
found for the control sample ($\sim$2 percent), which we interpret as due to a
true AGN FC contribution, while for the remaining luminosity bins and for
larger radii we interpret it as due to a reddened young stellar population.

From Fig. 6 it is clear that there is a significant difference for the
intermediate age (100 Myr $<t\leq$ 2 Gyr) population between AGN hosts and
control galaxies. This difference increases with luminosity and is seen for
both early and late-type galaxies. Also, a positive gradient is seen with the
contribution of the intermediate population increasing outwards.

When looking at the old component ($t\geq$ 5 Gyr), a clear difference between
the AGN hosts and their controls is seen, becoming more evident for higher
luminosities. A slightly steeper gradient, with the fraction of the old
population decreasing with R, is seen in the case of AGN hosts when compared
with controls.

The above results can be summarised when we looking how the light-weighed mean
age ($<t>_{L}$, Fig. 8) changes with R. What becomes clear when looking for
R$\lesssim$0.5 Re, is that AGN hosts become younger as the AGN luminosity
increases (for both, Early and Late-type sources), but, the mean ages are
smaller for AGN hosts over R values up to 1.75 Re, indicating that the stellar
population of the host galaxies is affected by the nuclear activity. In
addition, a negative gradient of $<t>_{L}$ with radius is also found, with a
flattening for higher AGN [O iii] luminosities.

As can be seen from Figs.4 to 8, in general, we observe that the fraction of
young and intermediate-age stellar populations increases with the radius,
while in the case of the old population, it decreases. These findings are in
agreement with previous results (e.g. Sánchez et al., 2013; Ibarra-Medel et
al., 2016; Goddard et al., 2017; Mallmann et al., 2018; Sánchez et al., 2021,
and references therein). Our results are similar to the results we have
obtained for the first 62 AGNs observed by MaNGA (Mallmann et al., 2018). When
looking at controls and AGNs, separated between early- and late-type sources,
the sense of our gradients are opposed to those found by Goddard et al.
(2017). For early-types we derive negative gradients while theirs are slightly
positive. The results presented here are in full agreement with those by
Ibarra-Medel et al. (2016), who found that the radial stellar mass growth
histories of early-type galaxies increase outward, though with a trend much
less pronounced than that of their late-type galaxies. The decreasing gradient
we observed for the old component suggests that the relative contribution of
the more recent star formation increases going outward. Both, early and late-
type sources present a significant contribution of the intermediate
($\sim$20/40 percent for Early/Late, respectively, for R$<$0.5 Re, in light
fractions) and young populations ($\sim$5/7 percent for Early/Late,
respectively, for R$<$0.5 Re, in light fractions). This indicates that even in
early-type sources residual star-formation is taking place, suggesting that
the galaxies are facing a rejuvenation process (e.g. Mallmann et al., 2018;
Martín-Navarro et al., 2022) or still forming stars with the gas that they
have still available (e.g. they have not finished their SF over cosmic time,
see Eales et al., 2017; Dimauro et al., 2022; Paspaliaris et al., 2023, and
references), thus they are in fact (predominantly) red and not  dead sources
(Salvador-Rusiñol et al., 2020; Salvador-Rusiñol et al., 2021; de Lorenzo-
Cáceres et al., 2020; Benedetti et al., 2023).

Figure 4: Comparison of the percent contribution to the light at
$\lambda_{0}$=5700Å between AGN (blue) and controls (orange) of the young
stellar population (xyy+xyo; $t\leq$ 56 Myr) percent contribution (in light
fractions) for five different luminosity bins (right side: $39$-$39.75$,
$39.75$-$40.25$, $40.25$-$40.75$, $40.75$-$41.25$, $41.25$-$42.0$) as a
function of the radius expressed in units of $R_{e}$. The second and third
columns show also the grouping of the galaxies in early and late-type hosts.
Figure 5: The same as in Fig. 4 but for the featureless continuum component.
Figure 6: The same as in Fig. 4 but for the intermediate age stellar
population (xiy+xii+xio; 100 Myr $<t\leq$ 2 Gyr) component. Figure 7: The same
as in Fig. 4 but for the xo (5 Gyr $<t\leq$ 13 Gyr) stellar population
component. Figure 8: Light-weighted mean age versus radius. The labels are the
same as in Fig. 4. Figure 9: Light-weighted mean metallicity versus radius.
The labels are the same as in Fig. 4.

####  4.2.3 Dependence on the AGN luminosity

In the case of the most luminous AGNs [log(L[O iii]:41.25 - 42.00)] the young
population contribution seems to rise to higher values for lower radii values.
We interpret this as the fact that these AGNs are receiving an extra supply of
gas and thus are more luminous. In fact, as shown in Riffel et al. (2022)
using high angular resolution observations, found that the inner region of the
AGNs is dominated by an intermediate age population ($<t>_{L}\lesssim$ 1.5
Gyr) and that a correlation between the bolometric luminosity of AGNs with the
mean age of their stellar populations is observed in the sense that the more
luminous AGNs have larger amounts of intermediate-age stars. This correlation
was interpreted as due to a delay between the formation of new stars and the
triggering/feeding of the AGN. These intermediate-age stars do provide (via
mass loss through stellar evolution) an extra amount of gas that will reach
the supermassive black hole (SMBH). Such gas has a low velocity (a few hundred
$\rm km\,s^{-1}$) and is accreted together with the gas that is already
flowing towards the central region of the host galaxy (e.g. Cuadra et al.,
2006; Hopkins, 2012; Storchi-Bergmann & Schnorr-Müller, 2019). In fact, young
to intermediate-age populations are detected in the inner region of galaxies
(e.g. Dottori et al., 2005; Davies et al., 2005; Riffel et al., 2007; Riffel
et al., 2015; Salvador-Rusiñol et al., 2020; Salvador-Rusiñol et al., 2021; de
Lorenzo-Cáceres et al., 2020). Such population is dominated by short-lived
stars ($t\simeq$0.2 – 2 Gyr; $\rm M\simeq 2-6\,M_{\odot}$) who do eject a
significant amount of material to the nuclear environment. This recycled
material can cool down forming new stars (e.g. Salvador-Rusiñol et al., 2020;
Salvador-Rusiñol et al., 2021; de Lorenzo-Cáceres et al., 2020; Benedetti et
al., 2023) and/or fuel the SMBH with an extra amount of gas, making the AGN
brighter or triggering it. This would explain the fact that the most luminous
AGNs in our sample do show larger fractions of this intermediate-age
population when compared with their control sources.

####  4.2.4 The stellar Metallicity

In Fig. 9 we show the mean stellar metalicity. Overall, the trend for both the
AGN hosts and control galaxies shows a decreasing gradient with increasing
radius. A similar behaviour is found by Goddard et al. (2017) and Sánchez
(2020), who found a negative gradient for the stellar metallicity for high
mass galaxies and positive for low mass ones. For our sample, when all sources
are taken together (e.g. left side pannel) the most luminous AGN hosts show a
slightly shallower gradient when compared with the controls777This is in full
agreement with the findings of Sánchez (2020), since the AGN luminosity can be
taken as a proxy for the galaxy mass (thus the control galaxies for the most
luminous AGNs would be more massive).. Additionally, for regions R $<$ 0.5 Re
the AGN hosts metallicity is biased towards lower values when compared with
the control galaxies and larger radii values. In fact, this is in agreement
with the finding of do Nascimento et al. (2022) who derive the gas metallicity
(traced by the O/H abundance) of 108 Seyfert galaxies from MaNGA, and found
that the inner regions of these galaxies display lower abundances than their
outer regions. Additionally, Armah et al. (2023) have shown that the more
luminous Seyfert galaxies have lower gas metalicities. However, these results
can not be straightforward compared since the gas metallicity is a recent
picture of the chemical evolution of the galaxies, while the mean stellar
metalicity is a mean over the galaxies’ lifetime (e.g. the mean over all the
stellar generations). Nevertheless, our stellar metalicity estimates are in
agreement with those obtained through the gas phase abundances.

###  4.3 Spatially resolved diagnostic diagrams: BPT and WHAN

It is known that AGN produces a much harder radiation field than main sequence
stars. A widely used tool to identify and classify emission-line sources
according to their dominant radiation field are the diagrams proposed by
Baldwin et al. (1981, hereafter BPT) which are based on line ratios between
high and low ionization potential species (e.g. [N ii]/H$\alpha$ $\times$ [O
iii]/H$\beta$). However, the BPT diagrams cannot discriminate between genuine
low-ionization AGN and emission-line galaxies whose ionizing photons are
produced in the atmospheres of hot low-mass evolved stars (HOLMES). To
overcome this Cid Fernandes et al. (2010) have proposed a diagram that uses as
a discriminator the equivalent width (EW) of the H$\alpha$ emission line. They
have shown that galaxies with EW(H$\rm{}_{\alpha})<$ 3Å, may not be ionized by
an AGN but by the HOLMES. This diagram is known as the WHAN diagram since it
involves the [N ii]/H$\alpha$ and EW(H$\alpha$).

In Fig. 10 and Fig. 11 we present the BPT and WHAN diagrams, respectively for
AGNs and controls. These diagrams have been built with all the spaxels where
the SNR in the continuum is $\geq 10$ and the [O iii]$\lambda$ 5007Å emission
line is detected with a minimum of 3$\sigma$ confidence level, and for four
different radial regions in the galaxies.

As can be seen in Fig. 10, the BPT diagrams reveal that most of the AGN
emission is concentrated in the inner 0.5 $R_{e}$ region of the galaxies (with
Sy/LINER classification), as well as that the bulk of the emission for the
control galaxies is located in the SF-dominated region. In addition, for
regions outside the inner 0.5 $R_{e}$, most of the AGN hosts and control
galaxies become SF-dominated and present very similar distributions. In
summary, when moving radially across the galaxy (increasing $R_{e}$) what is
seen is that the ionization pattern moves from the AGN-dominated region to the
SF-dominated one, crossing the transition region.

On the other hand, the same behavior is not clearly seen in the WHAN diagram
(Fig. 11). While the bulk of the spaxels in the inner 0.5 $R_{e}$ of AGN hosts
is located at consistently larger EW(H$\alpha$) values ($\sim 0.5$ dex) than
the “retired” wing of the control galaxies, spaxels in both samples also
distribute along a diagonal path crossing the wAGN/sAGN regions all the way to
the SF region. This extended path in the WHAN diagram corresponds to the
displacement from AGN/LI(N)ER to SF-ionization in the BPT diagram across the
transition region.

It is important to note that the WHAN diagram seems effective at separating
the “forgotten" population of retired galaxies from true AGN-ionized ones;
however, the transition region (e.g. with photons from HOLMES/SF+AGN) is not
explicitly defined in this diagram. The distinction between AGN and SF objects
is much more diffuse. At radial distances larger than 0.5 $R_{e}$, spaxels
tend to be located in, or close to, the SF region, both for AGN hosts and
control galaxies, but a significant fraction of spaxels is still located in
the sAGN/wAGN region. This may be due to the fact that, as we move toward
higher radii values, we observe regions where the role played by SF on the gas
excitation increases, while that of the AGN decreases (Sánchez, 2020), and so
the EW(H$\alpha$) increases, making the locus of the region move towards the
weak-AGN/SF dominated region. In the studies of Deconto-Machado et al. (2022)
as in Gatto et al. (in preparation), on the same sample of AGN and control
galaxies discussed here, we have found continuity in the kinematic properties
of AGN and controls, in terms of the line widths of the [O iii]$\lambda$5007Å
emission lines vs. L([O iii]). This can be interpreted as due to the fact that
even in the regions with EW(H$\alpha$) $<3\AA$ the gas may be still ionized by
a mix of photons coming both from the AGN and from HOLMES. This becomes even
more clear when BPT and WHAN are considered together.

In order to try to clarify the behavior described above, we have considered
the BPT and WHAN together in Fig. 12: as can be seen in this figure the
spaxels with larger EW(H$\alpha$) when moving to larger radii move to the SF
dominated region, clearly indicating that to do a proper activity
classification, besides the line ratios and EW(H$\alpha$), aperture effects
also need to be considered and that the bulk of the emission outside the
central region is dominated by star-forming processes (Sánchez, 2020). In
fact, this aperture-dependent (mis)classification has been reported by Albán &
Wylezalek (2023). They found that the number of fake-AGN increases
significantly for values larger than 1.0$R_{e}$ and they attribute it to
increased contamination from diffuse ionized gas. Sánchez (2020) shows that
the ionization in the overall extension of galaxies is dominated by stellar
processes (hot young stars or HOLMES), with the AGN emission being dominant in
the central region of active galaxies.

Figure 10: BPT diagnostic diagram for our AGN and control samples. In these
diagrams, we show all the spaxels where SNR in the continuum is $\geq 10$ and
the [O iii]$\lambda$ 5007Å emission line is detected with a 3$\sigma$
confidence level. Different radial values are shown, from top to bottom: 0 –
0.5 $R_{e}$ (red), 0.5 – 1.0 $R_{e}$ (green), 1.0 – 1.5 $R_{e}$ (blue), and
1.5 – 2.0 $R_{e}$ (yellow). Black and red lines correspond to the empirical
and theoretical criteria to separate AGN-like and H ii-like objects proposed
by Kewley et al. (2001) and Kauffmann et al. (2003b), respectively. The blue
line represents the separation between AGN-like and LINERS-like sources
proposed by Cid Fernandes et al. (2010).  Figure 11:  WHAN diagnostic diagram
for our AGN and control samples for the same data-points as in Fig. 10 Figure
12: BPT diagnostic diagram for our AGN and control samples for distinct radial
distances (from top to bottom: 0 – 0.5 $R_{e}$, 0.5 – 1.0 $R_{e}$, 1.0 – 1.5
$R_{e}$, and 1.5 – 2.0 $R_{e}$) with each spaxel colored according to the
equivalent width (EW) of the H$\alpha$ emission line (using the limits of the
WHAN for EWH$\alpha$). The lines are the same as in Fig. 10.

##  5 Final Remarks

We present here spaxel-by-spaxel stellar population fits for the $\sim$10
thousand MaNGA datacubes. We provide multiple extension fits files, nominated
as megacubes, with maps of light- and mass-fraction contribution of each
stellar population component, light-weighted and mass-weighted mean age and
mean metallicity, reddening, star formation rates via stellar population (over
the last 1 Myr, 5.6 Myr, 10 Myr, 14 Myr, 20 Myr, 32 Myr, 56 Myr, 100 Myr, and
200 Myr), binned population vectors in light- and mass-fraction in different
age ranges (xyy$\leq$10 Myr; 14 Myr$\rm<xyo\leq$56 Myr; 100
Myr$\rm<xiy\leq$500 Myr; 630 Myr $\rm<xii\leq$800 Myr; 890 Myr$\rm<xio\leq$2.0
Gyr; 5.0 Gyr$\rm<xo\leq$13 Gyr). Parameters characterising the emission-line
profiles of H$\beta$, [O iii]$\lambda\lambda$4959,5007, He i$\lambda$5876, [O
i]$\lambda$6300, H$\alpha$ [N ii]$\lambda\lambda$6548,6583 and [S
ii]$\lambda\lambda$6716,6731, are also provided for each spaxel. All the
megacubes are available through a web interface (https://manga.linea.org.br/
or https://www.if.ufrgs.br/~riffel/software/megacubes/), where each one of
them can be inspected, downloaded, and interacted with via different plots.

We analysed the megacubes for the final AGN sample (293) and control galaxies
(586) in the MaNGA survey selected according to the criteria from Rembold et
al. (2017), in which the control galaxies are matched to the AGN according to
stellar mass, distance, inclination, and galaxy type.

We have also presented a global analysis of the stellar population of the
final set of AGNs and compared them with the control galaxies sample. We found
that the young and intermediate-age populations show an outwards increasing
gradient for AGNs and controls, while the old component decreases outwards,
indicating that the galaxies are facing a rejuvenation process. We find that
the fraction of intermediate-age stellar population is higher in AGN hosts
than that found in the control sample, and this difference becomes larger for
higher-luminosity AGNs. This has been interpreted by us as the fact that an
extra amount of gas is available in these more luminous sources and that most
likely it originates from mass loss from the intermediate-age stellar
population. We have also found that the mean metalicities show a similar trend
for AGN hosts and control galaxies showing a decreasing gradient with
increasing radius, with AGN hosts showing a slightly shallower gradient.
Additionally, for regions R $<$ 0.5 Re the AGN hosts metallicity is biased
towards lower values when compared with the control galaxies, as well as when
compared with larger radii values. This is in agreement with the behavior
found when studying the gas phase abundances.

Analysis of the global properties of the gas excitation via BPT and WHAN
diagrams have also been presented. The spatially resolved BPT diagrams reveal
that the AGN emission is concentrated in the inner 0.5 $R_{e}$ region of the
galaxies (with Sy/LINER classification), as well as that the bulk of the
distribution for the control galaxies is located in the SF-dominated region.
Additionally, besides the dependence on aperture size for proper activity type
classification, we show that the BPT diagram has to be used together with the
strength of the H$\alpha$ equivalent width. We thus present a “composite"
BPT+WHAN diagram to be used for as a more comprehensive diagnostic of the
emitting gas excitation in galaxies.

## Acknowledgments

We thank the anonymous referee for useful suggestions which helped to improve
the paper. RR acknowledges support from the Fundación Jesús Serra and the
Instituto de Astrofísica de Canarias under the Visiting Researcher Programme
2023-2025 agreed between both institutions. RR, also acknowledges support from
the ACIISI, Consejería de Economía, Conocimiento y Empleo del Gobierno de
Canarias and the European Regional Development Fund (ERDF) under grant with
reference ProID2021010079, and the support through the RAVET project by the
grant PID2019-107427GB-C32 from the Spanish Ministry of Science, Innovation
and Universities MCIU. This work has also been supported through the IAC
project TRACES, which is partially supported through the state budget and the
regional budget of the Consejería de Economía, Industria, Comercio y
Conocimiento of the Canary Islands Autonomous Community. RR also thanks to
Conselho Nacional de Desenvolvimento Científico e Tecnológico ( CNPq, Proj.
311223/2020-6, 304927/2017-1, 400352/2016-8, and 404238/2021-1), Fundação de
amparo à pesquisa do Rio Grande do Sul (FAPERGS, Proj. 16/2551-0000251-7 and
19/1750-2), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
(CAPES, Proj. 0001). RAR acknowledges support from CNPq and FAPERGS. GSI
acknowledges support from CNPq (142514/2018-7) and FAPESP (Fundação de Amparo
à Pesquisa do Estado de São Paulo, Proj. 2022/11799-9). We are also grateful
to LIneA’s IT team which has supported the creation of the web portal and the
INCT e-Universo. CRA acknowledges support from the projects “Feeding and
feedback in active galaxies”, with reference PID2019-106027GB-C42, funded by
MICINN-AEI/10.13039/501100011033, and “Quantifying the impact of quasar
feedback on galaxy evolution”, with reference EUR2020-112266, funded by
MICINN-AEI/10.13039/501100011033 and the European Union NextGenerationEU/PRTR.

SDSS is managed by the Astrophysical Research Consortium for the Participating
Institutions of the SDSS Collaboration including the Brazilian Participation
Group, the Carnegie Institution for Science, Carnegie Mellon University, the
Chilean Participation Group, the French Participation Group, Harvard-
Smithsonian Center for Astrophysics, Instituto de Astrofisica de Canarias, The
Johns Hopkins University, Kavli Institute for the Physics and Mathematics of
the Universe (IPMU) / University of Tokyo, the Korean Participation Group,
Lawrence Berkeley National Laboratory, Leibniz Institut für Astrophysik
Potsdam (AIP), Max-Planck-Institut für Astronomie (MPIA Heidelberg), Max-
Planck-Institut für Astrophysik (MPA Garching), Max-Planck-Institut für
Extraterrestrische Physik (MPE), National Astronomical Observatories of China,
New Mexico State University, New York University, University of Notre Dame,
Observatório Nacional / MCTI, The Ohio State University, Pennsylvania State
University, Shanghai Astronomical Observatory, United Kingdom Participation
Group, Universidad Nacional Autónoma de México, University of Arizona,
University of Colorado Boulder, University of Oxford, University of
Portsmouth, University of Utah, University of Virginia, University of
Washington, University of Wisconsin, Vanderbilt University, and Yale
University.

## Data Availability

The data underlying this article are available under SDSS collaboration rules,
and the by-products are available at: https://manga.linea.org.br/ or
https://www.if.ufrgs.br/~riffel/software/megacubes/ following the SDSS
collaboration rules.

## References

  * Abdurro’uf et al. (2022) Abdurro’uf et al., 2022, The Seventeenth Data Release of the Sloan Digital Sky Surveys: Complete Release of MaNGA, MaStar and APOGEE-2 Data (arxiv:2112.02026), doi:10.3847/1538-4365/ac4414
  * Agostino et al. (2021) Agostino C. J., et al., 2021, The Astrophysical Journal, 922, 156
  * Aguado et al. (2019) Aguado D. S., et al., 2019, The Astrophysical Journal Supplement Series, 240, 23
  * Alam et al. (2015) Alam S., et al., 2015, The Astrophysical Journal Supplement Series, 219, 12
  * Albán & Wylezalek (2023) Albán M., Wylezalek D., 2023, Classifying the Full SDSS-IV MaNGA Survey Using Optical Diagnostic Diagrams: Presentation of AGN Catalogs in Flexible Apertures, doi:10.48550/arXiv.2302.08519
  * Armah et al. (2023) Armah M., et al., 2023, Monthly Notices of the Royal Astronomical Society, 520, 1687
  * Baldry et al. (2004) Baldry I. K., Glazebrook K., Brinkmann J., Ivezić Ž., Lupton R. H., Nichol R. C., Szalay A. S., 2004, The Astrophysical Journal, 600, 681
  * Baldwin et al. (1981) Baldwin J. A., Phillips M. M., Terlevich R., 1981, Publications of the Astronomical Society of the Pacific, 93, 5
  * Belfiore et al. (2019) Belfiore F., et al., 2019, The Astronomical Journal, 158, 160
  * Benedetti et al. (2023) Benedetti J. P. V., et al., 2023, Monthly Notices of the Royal Astronomical Society
  * Bessiere & Ramos Almeida (2022) Bessiere P. S., Ramos Almeida C., 2022, Monthly Notices of the Royal Astronomical Society, 512, L54
  * Bieri et al. (2016) Bieri R., Dubois Y., Silk J., Mamon G. A., Gaibler V., 2016, Monthly Notices of the Royal Astronomical Society, 455, 4166
  * Blanton et al. (2017) Blanton M. R., et al., 2017, The Astronomical Journal, 154, 28
  * Brammer et al. (2009) Brammer G. B., et al., 2009, The Astrophysical Journal, 706, L173
  * Bundy et al. (2015) Bundy K., et al., 2015, Astrophysical Journal, 798, 7
  * Cardelli et al. (1989) Cardelli J. A., Clayton G. C., Mathis J. S., 1989, The Astrophysical Journal, 345, 245
  * Cherinka et al. (2019) Cherinka B., et al., 2019, The Astronomical Journal, 158, 74
  * Cid Fernandes (2018) Cid Fernandes R., 2018, Monthly Notices of the Royal Astronomical Society, 480, 4480
  * Cid Fernandes et al. (2004) Cid Fernandes R., Gu Q., Melnick J., Terlevich E., Terlevich R., Kunth D., Rodrigues Lacerda R., Joguet B., 2004, Monthly Notices of the RAS, 355, 273
  * Cid Fernandes et al. (2005) Cid Fernandes R., Mateus A., Sodré L., Stasińska G., Gomes J. M., 2005, Monthly Notices of the RAS, 358, 363
  * Cid Fernandes et al. (2010) Cid Fernandes R., Stasińska G., Schlickmann M. S., Mateus A., Vale Asari N., Schoenell W., Sodré L., 2010, Monthly Notices of the Royal Astronomical Society, 403, 1036
  * Cid Fernandes et al. (2013) Cid Fernandes R., et al., 2013, Astronomy and Astrophysics, 557, A86
  * Cid Fernandes et al. (2014) Cid Fernandes R., et al., 2014, Astronomy & Astrophysics, Volume 561, id.A130, $<$NUMPAGES$>$19$<$/NUMPAGES$>$ pp., 561, A130
  * Conselice (2014) Conselice C. J., 2014, Annual Review of Astronomy and Astrophysics, 52, 291
  * Crain et al. (2015) Crain R. A., et al., 2015, Monthly Notices of The Royal Astronomical Society, 450, 1937
  * Croton et al. (2006) Croton D. J., et al., 2006, Monthly Notices of The Royal Astronomical Society, 365, 11
  * Cuadra et al. (2006) Cuadra J., Nayakshin S., Springel V., Di Matteo T., 2006, Monthly Notices of the Royal Astronomical Society, 366, 358
  * Davies et al. (2005) Davies R. I., Sternberg A., Lehnert M. D., Tacconi-Garman L. E., 2005, The Astrophysical Journal, 633, 105
  * Deconto-Machado et al. (2022) Deconto-Machado A., et al., 2022, Astronomy and Astrophysics, 659, A131
  * Dimauro et al. (2022) Dimauro P., et al., 2022, Monthly Notices of the Royal Astronomical Society, 513, 256
  * Dottori et al. (2005) Dottori H., Díaz R. J., Carranza G., Lípari S., Santos Jr. J., 2005, The Astrophysical Journal, 628, L85
  * Drory et al. (2015) Drory N., et al., 2015, The Astronomical Journal, 149, 77
  * Eales et al. (2017) Eales S., de Vis P., Smith M. W. L., Appah K., Ciesla L., Duffield C., Schofield S., 2017, Monthly Notices of the Royal Astronomical Society, 465, 3125
  * El-Badry et al. (2016) El-Badry K., Wetzel A., Geha M., Hopkins P. F., Kereš D., Chan T. K., Faucher-Giguère C.-A., 2016, The Astrophysical Journal, 820, 131
  * Fabian (2012) Fabian A. C., 2012, Annual Review of Astronomy and Astrophysics, 50, 455
  * Gallagher et al. (2019) Gallagher R., Maiolino R., Belfiore F., Drory N., Riffel R., Riffel R. A., 2019, Monthly Notices of the Royal Astronomical Society, 485, 3409
  * Goddard et al. (2017) Goddard D., et al., 2017, Monthly Notices of the Royal Astronomical Society, 466, 4731
  * González Delgado et al. (2005) González Delgado R. M., Cerviño M., Martins L. P., Leitherer C., Hauschildt P. H., 2005, Monthly Notices of the Royal Astronomical Society, 357, 945
  * Gunn et al. (2006) Gunn J. E., et al., 2006, The Astronomical Journal, 131, 2332
  * Hopkins (2012) Hopkins P. F., 2012, Monthly Notices of the RAS, 420, L8
  * Ibarra-Medel et al. (2016) Ibarra-Medel H. J., et al., 2016, Monthly Notices of the Royal Astronomical Society, 463, 2799
  * Ilha et al. (2022) Ilha G. S., et al., 2022, Monthly Notices of the Royal Astronomical Society, 516, 1442
  * Ishibashi & Fabian (2012) Ishibashi W., Fabian A. C., 2012, Monthly Notices of the Royal Astronomical Society, 427, 2998
  * Kauffmann et al. (2003a) Kauffmann G., et al., 2003a, Monthly Notices of the Royal Astronomical Society, 341, 54
  * Kauffmann et al. (2003b) Kauffmann G., et al., 2003b, Monthly Notices of the RAS, 346, 1055
  * Kewley et al. (2001) Kewley L. J., Dopita M. A., Sutherland R. S., Heisler C. A., Trevena J., 2001, The Astrophysical Journal, 556, 121
  * King & Pounds (2015) King A., Pounds K., 2015, Annual Review of Astronomy and Astrophysics, 53, 115
  * Koski (1978) Koski A. T., 1978, The Astrophysical Journal, 223, 56
  * Law et al. (2015) Law D. R., et al., 2015, The Astronomical Journal, 150, 19
  * Law et al. (2016) Law D. R., et al., 2016, The Astronomical Journal, 152, 83
  * Law et al. (2021) Law D. R., et al., 2021, The Astronomical Journal, 161, 52
  * Lintott et al. (2011) Lintott C., et al., 2011, Monthly Notices of the Royal Astronomical Society, 410, 166
  * Mallmann et al. (2018) Mallmann N. D., et al., 2018, Monthly Notices of the Royal Astronomical Society, 478, 5491
  * Martín-Navarro et al. (2022) Martín-Navarro I., Shankar F., Mezcua M., 2022, Monthly Notices of the Royal Astronomical Society, 513, L10
  * Martins et al. (2013) Martins L. P., Rodríguez-Ardila A., Diniz S., Gruenwald R., de Souza R., 2013, Monthly Notices of the RAS, 431, 1823
  * Muzzin et al. (2013) Muzzin A., et al., 2013, The Astrophysical Journal, 777, 18
  * Nayakshin & Zubovas (2012) Nayakshin S., Zubovas K., 2012, Monthly Notices of the Royal Astronomical Society, 427, 372
  * Noeske et al. (2007) Noeske K. G., et al., 2007, The Astrophysical Journal Letters, 660, L47
  * Paspaliaris et al. (2023) Paspaliaris E. D., Xilouris E. M., Nersesian A., Bianchi S., Georgantopoulos I., Masoura V. A., Magdis G. E., Plionis M., 2023, Astronomy and Astrophysics, 669, A11
  * Rees (1989) Rees M. J., 1989, Monthly Notices of the Royal Astronomical Society, 239, 1P
  * Rembold et al. (2017) Rembold S. B., et al., 2017, Monthly Notices of the Royal Astronomical Society, 472, 4382
  * Riess et al. (2022) Riess A. G., et al., 2022, The Astrophysical Journal, 934, L7
  * Riffel et al. (2007) Riffel R., Pastoriza M. G., Rodríguez-Ardila A., Maraston C., 2007, Astrophysical Journal, Letters, 659, L103
  * Riffel et al. (2009) Riffel R., Pastoriza M. G., Rodríguez-Ardila A., Bonatto C., 2009, Monthly Notices of the RAS, 400, 273
  * Riffel et al. (2011) Riffel R., Ruschel-Dutra D., Pastoriza M. G., Rodríguez-Ardila A., Santos Jr. J. F. C., Bonatto C. J., Ducati J. R., 2011, Monthly Notices of the RAS, 410, 2714
  * Riffel et al. (2015) Riffel R., et al., 2015, Monthly Notices of the RAS, 450, 3069
  * Riffel et al. (2016) Riffel R. A., et al., 2016, Monthly Notices of the Royal Astronomical Society, 461, 4192
  * Riffel et al. (2017) Riffel R. A., Storchi-Bergmann T., Riffel R., Dahmer-Hahn L. G., Diniz M. R., Schönell A. J., Dametto N. Z., 2017, Monthly Notices of the Royal Astronomical Society, 470, 992
  * Riffel et al. (2018) Riffel R. A., et al., 2018, Monthly Notices of the Royal Astronomical Society, 474, 1373
  * Riffel et al. (2021a) Riffel R., et al., 2021a, Monthly Notices of the Royal Astronomical Society, 501, 4064
  * Riffel et al. (2021b) Riffel R. A., Bianchin M., Riffel R., Storchi-Bergmann T., Schönell A. J., Dahmer-Hahn L. G., Dametto N. Z., Diniz M. R., 2021b, Monthly Notices of the Royal Astronomical Society, 503, 5161
  * Riffel et al. (2021c) Riffel R. A., et al., 2021c, Monthly Notices of the Royal Astronomical Society, 504, 3265
  * Riffel et al. (2022) Riffel R., et al., 2022, Monthly Notices of the Royal Astronomical Society, 512, 3906
  * Ruschel-Dutra (2020) Ruschel-Dutra 2020, Danielrd6/Ifscube v1.0, Zenodo, doi:10.5281/zenodo.3945237
  * Ruschel-Dutra et al. (2021) Ruschel-Dutra D., et al., 2021, Monthly Notices of the Royal Astronomical Society, 507, 74
  * Salvador-Rusiñol et al. (2020) Salvador-Rusiñol N., Vazdekis A., La Barbera F., Beasley M. A., Ferreras I., Negri A., Dalla Vecchia C., 2020, Nature Astronomy, 4, 252
  * Salvador-Rusiñol et al. (2021) Salvador-Rusiñol N., Beasley M. A., Vazdekis A., Barbera F. L., 2021, Monthly Notices of the Royal Astronomical Society, 500, 3368
  * Sánchez (2020) Sánchez S. F., 2020, Annual Review of Astronomy and Astrophysics, vol. 58, p.99-155, 58, 99
  * Sánchez et al. (2013) Sánchez S. F., et al., 2013, Astronomy and Astrophysics, 554, A58
  * Sánchez et al. (2021) Sánchez S. F., Walcher C. J., Lopez-Cobá C., Barrera-Ballesteros J. K., Mejía-Narváez A., Espinosa-Ponce C., Camps-Fariña A., 2021, Revista Mexicana de Astronomia y Astrofisica, 57, 3
  * Schaye et al. (2015) Schaye J., et al., 2015, Monthly Notices of the Royal Astronomical Society, 446, 521
  * Schlegel et al. (1998) Schlegel D. J., Finkbeiner D. P., Davis M., 1998, The Astrophysical Journal, 500, 525
  * Schnorr-Müller et al. (2021) Schnorr-Müller A., et al., 2021, Monthly Notices of the Royal Astronomical Society, 507, 300
  * Smee et al. (2013) Smee S. A., et al., 2013, The Astronomical Journal, 146, 32
  * Springel et al. (2005) Springel V., et al., 2005, Nature, 435, 629
  * Steiner et al. (2009) Steiner J. E., Menezes R. B., Ricci T. V., Oliveira A. S., 2009, Monthly Notices of the Royal Astronomical Society, 395, 64
  * Storchi-Bergmann & Schnorr-Müller (2019) Storchi-Bergmann T., Schnorr-Müller A., 2019, Nature Astronomy, 3, 48
  * Storchi-Bergmann et al. (2009) Storchi-Bergmann T., McGregor P. J., Riffel R. A., Simões Lopes R., Beck T., Dopita M., 2009, Monthly Notices of the RAS, 394, 1148
  * Storchi-Bergmann et al. (2012) Storchi-Bergmann T., Riffel R. A., Riffel R., Diniz M. R., Borges Vale T., McGregor P. J., 2012, Astrophysical Journal, 755, 87
  * Tacconi et al. (2020) Tacconi L. J., Genzel R., Sternberg A., 2020, Annual Review of Astronomy and Astrophysics, vol. 58, p.157-203, 58, 157
  * Thomas et al. (2013) Thomas D., et al., 2013, Monthly Notices of the Royal Astronomical Society, 431, 1383
  * Trussler et al. (2020) Trussler J., Maiolino R., Maraston C., Peng Y., Thomas D., Goddard D., Lian J., 2020, Monthly Notices of the Royal Astronomical Society, 491, 5406
  * Vazdekis et al. (2010) Vazdekis A., Sánchez-Blázquez P., Falcón-Barroso J., Cenarro A. J., Beasley M. A., Cardiel N., Gorgas J., Peletier R. F., 2010, Monthly Notices of the Royal Astronomical Society, 404, 1639
  * Vazdekis et al. (2016) Vazdekis A., Koleva M., Ricciardelli E., Röck B., Falcón-Barroso J., 2016, Monthly Notices of the RAS, 463, 3409
  * Vázquez-Mata et al. (2022) Vázquez-Mata J. A., et al., 2022, Monthly Notices of the Royal Astronomical Society, 512, 2222
  * Vogelsberger et al. (2014) Vogelsberger M., et al., 2014, Nature, 509, 177
  * Wake et al. (2017) Wake D. A., et al., 2017, The Astronomical Journal, 154, 86
  * Wang & Loeb (2018) Wang X., Loeb A., 2018, New Astronomy, 61, 95
  * Westfall et al. (2019) Westfall K. B., et al., 2019, The Astronomical Journal, 158, 231
  * Wetzel et al. (2012) Wetzel A. R., Tinker J. L., Conroy C., 2012, Monthly Notices of the Royal Astronomical Society, 424, 232
  * Yan et al. (2016a) Yan R., et al., 2016a, The Astronomical Journal, 151, 8
  * Yan et al. (2016b) Yan R., et al., 2016b, The Astronomical Journal, 152, 197
  * York et al. (2000) York D. G., et al., 2000, The Astronomical Journal, 120, 1579
  * Zubovas & Bourne (2017) Zubovas K., Bourne M. A., 2017, Monthly Notices of the RAS, 468, 4956
  * Zubovas et al. (2013) Zubovas K., Nayakshin S., King A., Wilkinson M., 2013, Monthly Notices of the Royal Astronomical Society, 433, 3079
  * de Lorenzo-Cáceres et al. (2020) de Lorenzo-Cáceres A., Vazdekis A., Falcón-Barroso J., Beasley M. A., 2020, Monthly Notices of the Royal Astronomical Society, 498, 1002
  * do Nascimento et al. (2022) do Nascimento J. C., et al., 2022, Monthly Notices of the Royal Astronomical Society, 513, 807
  * van der Wel et al. (2014) van der Wel A., et al., 2014, The Astrophysical Journal, 788, 28

See pages - of ./figures/onlineFigures_comp.pdf