11institutetext: Zentrum für Astronomie der Universität Heidelberg,
Astronomisches Rechen-Institut, Mönchhofstr, 12-14 69120 Heidelberg, Germany

# Classifying the full SDSS-IV MaNGA Survey using optical diagnostic diagrams:
presentation of AGN catalogs in flexible apertures

M. Albán 11 D. Wylezalek 11

(Received November 11, 2022; accepted February 16, 2023)

Accurate active galactic nucleus (AGN) identifications in large galaxy samples
are crucial to assess the role of AGN and AGN feedback in the coevolution of
galaxies and their central supermassive black holes. Emission line flux ratio
diagnostics are the most common technique for identifying AGN in optical
spectra. New large samples of integral field unit observations allow The
exploration of the role of aperture size used for the classification. In this
paper, we present galaxy classifications for all 10,010 galaxies observed
within the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey.
We use Baldwin-Philips-Terlevich line flux ratio diagnostics combined with an
H$\alpha$ equivalent threshold in 60 apertures of varying size for the
classification and provide the corresponding catalogs. MaNGA-selected AGN
primarily lie below the main sequence of star-forming galaxies, reside with
massive galaxies with stellar masses of $\sim 10^{11}$ M⊙ and a median
H$\alpha$-derived star formation rate of $\sim 1.44$M⊙ yr-1. We find that the
number of ‘fake’ AGN increases significantly beyond selection apertures of $>$
1.0 Reff due to increased contamination from diffuse ionized gas (DIG). A
comparison with previous works shows that the treatment of the underlying
stellar continuum and flux measurements can significantly impact galaxy
classification. Our work provides the community with AGN catalogs and galaxy
classifications for the full MaNGA.

###### Key Words.:

Catalogs – galaxies: active

##  1 Introduction

Mounting observational evidence has shown that supermassive black holes
(SMBHs) are ubiquitous in most, or even all, centers of massive galaxies
(Decarli et al., 2007; Kormendy & Ho, 2013; Graham, 2016). Many properties of
these galaxies show tight relations (e.g., the bulge’s velocity dispersion,
Ferrarese & Merritt, 2000; Gültekin et al., 2009), in particular with the mass
of their SMBHs (Beifiori et al., 2011), suggesting that the presence of a SMBH
impacts the evolution of their host galaxies and vice versa. How this happens
over cosmic time is an active field of study (e.g. Heckman & Best, 2014; Park
et al., 2015; Volonteri et al., 2016; Buchner et al., 2019; Smith & Bromm,
2019; Singh et al., 2021). Standard evolution models inkove the active growth
phases of SMBH, i.e. their active galactic nucleus (AGN, Rees, 1984; Alexander
& Hickox, 2012; Padovani et al., 2017) phase.

During the SMBH activity, the injection of energy into the interstellar medium
(ISM) generated by accretion onto the BH can have a relevant impact on the
host galaxy, leading, for example, to quench star formation (negative
feedback, Springel et al., 2005; Cano-Díaz et al., 2012), enhance it (positive
feedback, Mahoro et al., 2017; Nesvadba et al., 2020; Bessiere & Almeida,
2022), or even both (e.g. Wagner et al., 2016; Dugan et al., 2017). This has
motivated cosmological simulations (see Somerville & Davé, 2015, for a
detailed review) to include AGN feedback (Fabian, 2012) in their models.

To constrain and accurately quantify the effect that AGN have in their host
galaxies, AGN selection algorithms are required to be as complete as possible.
AGN galaxies span a very wide peculiar observational characteristics (Maoz,
2007), and it is therefore not surprising that all AGN selection methods come
with some caveats. Over the past decades, multiple AGN selection methods have
been invoked inspired by the peculiar multi-wavelength radiation from the
energetic release of the accretion onto SMBHs (for a convenient summary of
these methods, check section 1.4 from Harrison, 2014). However, these
techniques can be heavily biased towards, for example, finding AGN
preferentially in luminous host galaxies or may not be sensitive to find
obscured AGN (e.g., Azadi et al., 2017; Yi et al., 2022).

A commonly used AGN selection method is based on strong optical emission-line
ratios (Baldwin et al., 1981; Veilleux & Osterbrock, 1987), which are
frequently referred to as BPT diagrams in the literature. We also adopt that
nomenclature in this paper. The used line complexes are close in wavelength
space which reduces the effects of dust reddening that could affect the
measurement of the emission line ratios (Kennicutt, 1992). This technique,
together with empirical demarcation lines (for the demarcation line equations
and a summary of this methods, see Kewley et al., 2006), allows distinguishing
between different ionisation sources. For example, star-forming galaxies and
HII regions exhibit specific line ratios that tend to occupy a well-defined
area in the BPT diagrams. Other celestial objects, such as planetary nebula,
and most importantly (for our study, to identify AGN activity) Seyfert and
LINER (low ionisation nuclear emission-line region, Halpern & Steiner, 1983)
galaxies gather in different positions on the BPT diagrams. A frequently used
BPT diagnostic diagram includes [O III]$\lambda$5008/H$\beta$ versus [N
II]$\lambda$6583/H$\alpha$ that distinguishes between AGN-like galaxies, star-
forming galaxies, and an overlap region classifying objects as composite
galaxies (which show characteristics consistent with both AGN and star-forming
galaxies, Kewley et al., 2001). However, this diagram does not allow one to
distinguish between LINER and Seyfert galaxies by itself.

LINER galaxies have been originally proposed to be driven by weak accretion-
powered AGN (Halpern & Steiner, 1983; Kauffmann et al., 2003a; Ho, 2008;
Masegosa, J. et al., 2011), presenting lower ionization levels (Ferland &
Netzer, 1983) compared to Seyferts. Therefore, Additional BPT diagrams use [O
III]/H$\beta$ versus [S II] $\lambda\lambda$6717,6731/H$\alpha$ (Kewley et
al., 2001) and [O III]/H$\beta$ versus [O I] $\lambda$6302/H$\alpha$
(Kauffmann et al., 2003a) to further distinguish between LINER and Seyfert
galaxies. However, some LINERs have also been reported to be compatible with
galaxies whose spectral contribution is dominated by ionizing photons from
post asymptotic giant branch stars (e.g. Binette et al., 1994; Yan & Blanton,
2012; Singh et al., 2013) or to be related to poststarburst galaxies
(Taniguchi et al., 2000). This situation has motivated alternative diagnostics
to select true AGN from the LINER galaxy population. For example, (Cid
Fernandes et al., 2010) proposed to use an additional cut in H$\alpha$
equivalent width (EW) motivated by the fact that Seyfert galaxies have higher
EW(H$\alpha$) values than LINER galaxies without AGN.

Some difficulties in using BPT diagnostics (see section 5 of Kewley et al.,
2019, for a review) also arise due to the fact that the emission line ratios
can be affected by shocks (e.g., Dopita et al., 2002; Allen et al., 2008),
obscuration by dust, metallicity (as the BPT diagram correlates with
metallicity; Groves et al., 2004), diffuse ionized gas regions (Zhang et al.,
2016; Mannucci et al., 2021), morphology, and cosmic-time (Kewley et al.,
2013; Hirschmann et al., 2017). Furthermore, observational effects, such as
using different aperture sizes affect measurements of integrated galaxy
properties, such as integrated star formation rates, emission line fluxes or
EW(H$\alpha$) measurements (Hopkins et al., 2003; Gómez et al., 2003; Kewley
et al., 2005; Iglesias-Páramo et al., 2016). Naturally, galaxy classification
using BPT diagnostics is therefore highly dependent on aperture size
(Maragkoudakis et al., 2014). If the galaxies to be classified span a range in
redshifts, a constant aperture probes different physical sizes in the observed
galaxies. This directly affects the observed emission-line flux ratios in
single fiber spectral observations, possibly leading to miss-classifications
(Veilleux et al., 1995; Maragkoudakis et al., 2014).

Integral field spectroscopy (IFS) (Bershady et al., 2010; Drory et al., 2015)
can help mitigate this effect by allowing to map the 2D spectral properties of
a target (e.g. Westfall et al., 2019). In particular, The SDSS-IV survey
Mapping Nearby Galaxies at APO (MaNGA) provides optical IFU observations of
10,010 galaxies at $0.03<z<0.1$. The final data release DR17 has just recently
been released to the public (DR17, Abdurro’uf et al., 2021). In this paper, we
investigate the impact of aperture effects for BPT-based AGN classifications
as well as provide a suite of AGN catalogs based on three sets of apertures
with differing units (kpc, effective radius, and arcsecond). We classify
galaxies as star-forming, Seyfert, Composite, LINER, or Ambiguous.

The paper is organized as follows: In Section 2 we describe the data and some
available AGN catalogs from early and recent MaNGA product launches and data
releases. The details for the study of the sample and the AGN selection
algorithm are described in Section 2. In Section 4 we present our aperture
based catalog and discuss the impact of the aperture selection. We compare our
AGN candidates with other AGN catalogs in Section 5. Lastly, we present our
conclusions in Section 6. Throughout the paper we use H${}_{0}=$72 km s-1
Mpc-1, $\Omega_{\rm{M}}$=0.3 and $\Omega_{\Lambda}=0.7$.

##  2 Data and AGN catalogs

Figure 1: In the left plot we show (target with plate-ifu: 8725-9102) the flux
of the [OIII]$\lambda$5007 emission line. Pixels with $S/N<3$ are masked. The
black circle indicates the 2 kpc circular diameter aperture where we average
the flux for the emission line ratios. In the right hand plot we show the
zoom-in into the aperture region, showing the percentage of the pixel area
captured by the aperture shown in black. We show the value in each pixel that
corresponds to the weight of the pixel when we compute the average fluxes.

###  2.1 MaNGA DR17

Mapping Nearby Galaxies at Apache Point Observatory (MaNGA, Bundy et al.,
2015) is one of the surveys of the fourth generation of the Sloan Digital Sky
Survey (SDSS-IV). It is an integral field unit (IFU) survey providing
spatially resolved spectra (Drory et al., 2015; Law et al., 2015) covering a
spectral range from 3622 to 10354 Å at a resolution R$\sim$2000 for each
target, using the 2.5 Sloan Telescope (Gunn et al., 2006). The field of view
of the IFU ranges from 12 to 32 arcseconds in diameter, ensuring that at least
80% of the targets are covered out to 1.5 $R_{e}$ and 2.5 $R_{e}$,
respectively. The 17th (and last) data release (Abdurro’uf et al., 2021)
includes data for 10,010 unique galaxies at $0.01<z<0.15$ and stellar masses ¿
109 M⊙.

For MaNGA, a Data Reduction Pipeline (DRP) is provided in Law et al. (2016),
whose output is processed by the Data Analysis Pipeline (DAP, Westfall et al.,
2019; Belfiore et al., 2019). In the DRP, the raw data for each target is
calibrated, sky subtracted, and stored in individual cube data and row-stacked
spectra. The DAP then analyzes the latter to create cubes with the binned
spectra together with models for the best fit spectra for different components
(e.g. stellar continuum, emission lines). The DAP also provides maps of
physical properties of the galaxies (e.g. sky coordinates, kinematics such as
stellar and gas velocities, emission line fluxes, equivalent widths, and
more). Through this paper, we use the emission line measurements (see Section
2) from the DAP for our analysis.

A set of AGN catalogs (e.g. (Rembold et al., 2017; Wylezalek et al., 2018;
Sánchez et al., 2018; Comerford et al., 2020)) from previous SDSS-IV releases
have provided AGN candidates since the early stages of MaNGA (e.g. MaNGA
Product Launch 5, hereby MPL-5). These catalogs are described in the coming
subsections and will be used for comparison purposes (see Section 5) to
examine possible differences with our selection technique 3.2. We will provide
a detailed comparison of the ionized gas dynamics in AGN selected through
different methods in an upcoming paper (Albán et al., in prep.).

###  2.2 MaNGA-MPL-5 AGN Catalog of Sánchez et al.

By June 2016, MaNGA had observed around 2700 targets (MaNGA Product Launch 5
or MPL-5; the MPL5 sample is identical to the 14th data release of MaNGA,
Abolfathi et al., 2018). For this sample, Sánchez et al. (2018) found 98 AGN
candidates. In this study, they used optical diagnostics (BPT diagrams),
following the guidelines from Kewley et al. (2006) on the three BPT
classification diagrams ([N II]/H$\alpha$, [S II]/H$\alpha$ and [O
I]/H$\alpha$). They focus on the emission-line fluxes inside a $3\arcsec$
aperture, derived using the PIPE3D (Sánchez et al., 2016) data analysis
pipeline. In addition to the BPT diagnostics, they also include a cut in
EW(H$\alpha$) of ¿ 1.5Å (Cid Fernandes et al., 2010). A classification between
type-I and type-II AGN (see Antonucci, 1993; Netzer, 2015, for a detailed
overview on AGN types) is also provided based on a multi-Gaussian emission-
line fitting procedure in the spectral region containing H$\alpha$ and [N II].
They classify an AGN as type-I if the broad component satisfies a $S/N>5$ and
$1000<\textnormal{FWHM}<10000$ km/s. They identify 35 type-I AGN in their
sample.

###  2.3 MaNGA-MPL-5 AGN Catalog of Rembold et al.

An additional study by Rembold et al. (2017) identifies 62 AGN candidates in
the MaNGA MPL-5/DR14 galaxy sample. They follow a similar approach as in
Section 2.2, using optical-BPT diagnostics. However, the analysis is carried
out using only one, namely the [N II]-based BPT diagnostic. They use emission-
line fluxes from a $3\arcsec\times 3\arcsec$ aperture, but do not use the
MaNGA data directly for their analysis. Instead, they utilise measurements
from the SDSS-III single-fibre observations. The emission line fluxes and
EW(H$\alpha$) were taken from the spectral analysis of Thomas et al. (2013),
which requires an amplitude-over-noise (AoN) greater than two to calculate an
emission line flux. They apply an EW(H$\alpha)>3\r{A}$ cut, as in Cid
Fernandes et al. (2010). The latter criterion is also used in our AGN
selection.

###  2.4 MaNGA-MPL-5 AGN Catalog of Wylezalek et al.

A different approach was used in Wylezalek et al. (2018). Ionization radiation
due to AGN can sometimes be found far away from the center of galaxies.
Possible reasons for this effect are central obscuration, recent mergers,
relic AGN (e.g., Keel et al., 2015). Therefore, Wylezalek et al. (2018)
develop a selection procedure based on spatially resolved BPT maps taking full
advantage of IFU spectra and do not require AGN-like BPT diagnostics in the
galaxy centers.

In addition to the classical BPT line ratio diagnostics, they impose a suite
of additional criteria to circumvent potential contamination of their sample
through diffuse ionized gas, extraplanar gas, and photoionization by hot
stars. They detect 303 AGN candidates in the same MaNGA Product Launch
(MPL-5). They find 173 galaxies that would not have been selected as AGN
candidates using the standard selection algorithms based on single-fibre
spectral observations.

###  2.5 MaNGA-MPL8 multi-wavelength AGN catalog of Comerford et al.

Comerford et al. (2020) utilizes mid-IR, X-ray, and radio observations as well
as broad emission lines in SDSS spectra to identify 406 AGN in the MaNGA MPL-8
(6261 galaxies) catalog. Their AGN catalog is thus independent of any BPT
diagnostics. They find 67 AGN through mid-infrared selection criteria using
data from the Wide-field Infrared Survey Explorer (Wright et al., 2010, WISE),
17 using hard X-ray selection criteria from Burst Alert Telescope (Barthelmy
et al., 2005, BAT) observations, and 325 AGN candidates through radio
selection criteria using data from the NRAO Very Large Array Sky Survey
(Condon et al., 1998, NVSS) and the Faint Images of the Radio Sky at Twenty
centimeters (Becker et al., 1995, FIRST) survey. In this catalog, they also
sub-divide radio-selected AGN into radio-loud and radio-quiet AGN.
Additionally, they present 55 type-1 AGN candidates based on broad Balmer
emission lines in SDSS spectra. Some galaxies are classified as AGN by several
of these criteria (e.g., 11 galaxies were classified as AGN simultaneously by
radio and mid-infrared selection criteria; see Table 2 in Comerford et al.
(2020)).

##  3 AGN Selection

In this section, we present how IFU spectroscopy can be utilised in a variety
of ways to classify MaNGA galaxies according to their BPT diagnostics. We
extract the emission line properties of the targets in different aperture
sizes and investigate how optical diagnostics vary and depend on the chosen
aperture size. We finally present a suite of BPT-based MaNGA AGN catalogs with
varying apertures for the full MaNGA sample (DR17).

Figure 2: The y-axis in the top plots and the bottom left plot shows the
number of galaxies classified as AGN (solid blue line) selected based on
different aperture sizes and using different units. The selection is done
following the algorithm described in Section 3.2, excluding the [O
I]/H$\alpha$ BPT diagram. In the top left plot, the kpc aperture increases in
steps of 0.5 kpc (in diameter). In the top right plot, the arcsecond aperture
increases in steps of 0.5$\arcsec$ (in diameter). In the bottom left plot, the
effective radius aperture increases in steps of 0.1 Reff (in radius). The red
(vertical-dashed) line in the top panels corresponds to the apertures used for
the comparisons in Section 5. In the bottom right plot, we show how the flux
ratios in one BPT diagram change as the aperture does. This is done for three
individual galaxies illustrated with different symbols (check the legend for
their corresponding MaNGA-IDs). Each symbol is filled with a specific color
that corresponds to the size of its aperture. In the bottom right plot, we
also include the empirical division lines that will give each target a
specific classification (e.g., AGN-like galaxy, Composite object, or HII-star
forming galaxies): red lines correspond to Kewley et al. (2001), and the blue
dashed line on the left plot corresponds to Kauffmann et al. (2003a).

###  3.1 Sample Definition

To obtain the emission line fluxes, as required by a BPT classification, we
extract the emission line maps for [O III]$\lambda$5008, [N II]$\lambda$6584,
[S II]$\lambda\lambda$6717,6731 (hereafter, we will refer to the sum of [S
II]$\lambda\lambda$6717,6731 simply as [S II]), [O I]$\lambda$6300 and the
hydrogen Balmer lines H$\alpha$ and H$\beta$ from the DAP. We use their non-
parametric emission line measurements. The latter are derived using bands of
20 Å centered on each emission line, and a narrower passband is used for
H$\alpha$ and [N II], due to their small separation (see Westfall et al.,
2019). No special treatment is done for possible complexities in emission
lines such as galactic outflows (e.g., Wylezalek et al., 2020) or emission
from the broad line region (BLR, Peterson, 2006; Netzer, 2015). We use the
MANGA_DAPPIXMASK111You can find information on how to use these masks in the
following link: https://www.sdss.org/dr17/algorithms/bitmasks/ bitmap to mask
pixels flagged as DONOTUSE or UNRELIABLE to exclude biased and/or unreliable
measurements.

Furthermore, in each map, we mask the pixels where the emission line flux does
not satisfy a signal-to-noise ($S/N$) greater than three (the $S/N$ is also
extracted from the DAP). Emission lines with a $S/N<3$ lead to unreliable flux
measurements and thus unreliable emission line ratios which would increase the
bias in the classification (e.g., Brinchmann et al., 2004). We measure the
pixel-weighted flux before computing the emission-line ratios. When a pixel
(at a certain position) in a map of a specific parameter (e.g., the [N II]
flux) has a $S/N<3$, we do not use that pixel for computing the emission line
flux of that aperture (see Section 3.2). However, if that pixel (same position
as before) has enough signal-to-noise ($S/N>3$) in another map (e.g., the [S
II] flux) of the same galaxy, it is included for computing that emission line
flux.

This $S/N$ cut inevitably decreases the size of our galaxy sample. For
example, when using a 2 kpc-sized aperture (see Section 3.3), in $\sim$25% of
the MaNGA galaxies no pixels are left available for measuring emission line
fluxes due to the signal-to-noise cut. Therefore, our sample is biased towards
line emitting galaxies, which are preferentially gas-rich, star-forming
galaxies. We explore this bias further in Section 4.3 and in Section 4.4.

###  3.2 Optical Classification

Due to the IFU nature of the MaNGA observations, we can perform an aperture-
dependent optical classification. We perform weighted averages in a way that
if a pixel partially contributes to a circular (or squared) aperture, we weigh
the pixel according to the fraction of its enclosed area. The latter is
measured as follows:

${\displaystyle{\bar{x}}={\frac{\sum\limits_{i=1}^{N}x_{i}w_{i}}{\sum\limits_{i=1}^{N}w_{i}}},}$
(1)

where xi is the total contribution of the pixel and wi is the weight of the
pixel measured as the fraction of the enclosed area by the corresponding
aperture for the $N$ enclosed pixels. We construct a squared grid to represent
the area and positions of each pixel. Each pixel is placed in the center of
the squares of the grid, evenly distributed according to the average distance
between the original coordinates (as in the coordinate map known as
SPX_SKYCOO, from the DAP maps).

We compare the coordinate of the center of each square (from the grid) to the
original center (from the DAP map SPX_SKYCOO) of the pixel. The typical
offsets between the coordinates of our synthetic grid and the ones from the
DAP are of the order of 10-8$\arcsec$, and the distances from each pixel are
of the order of 10-2$\arcsec$, demonstrating that the deviations from the
original position of the pixels are negligible. Thus, we do not take into
account this effect when calculating average emission line fluxes inside the
respective apertures. An example of this interpolation is shown in Figure 1,
where in the left plot we display an example of the [O III]$\lambda$5008
emission-line flux map (masked as described in Section 3.1) with the aperture
shown with a black circle (whose size corresponds to 2 kpc). In the right plot
we show a zoom-in version of this map using a different color map based on the
fraction of the pixel enclosed by the aperture (again shown in black); note
that pixels whose area enclosed by the aperture is zero are masked as NaN
values and are excluded from the plot. To create a specific aperture, the
digital size of a pixel is converted to physical quantities using data from
the DAP (e.g., effective radius, pixel per arcsecond and kiloparsec steps) and
the DRP (for individual redshifts using the NSA redshift measurements).

We measure average emission line fluxes in a set of apertures following the
quality criteria outlined in Section 3.1. We then compute the following
averaged flux ratios: [O III]/H$\beta$, [N II]/H$\alpha$, [S II]/H$\alpha$ and
[O I]/H$\alpha$ (also with a $S/N>3$ cut). For a specific aperture (e.g. 2 kpc
or $3\arcsec\times 3\arcsec$), we perform two separate classifications (Kewley
et al., 2006; Kauffmann et al., 2003a; Kewley et al., 2001): one using all
three BPT diagrams and one excluding the [O I]6300/H$\alpha$ BPT diagram. The
[O I]6300 emission line is relatively weaker compared to the other lines in
the BPT diagrams and therefore exhibits lower S/N leading. This would
therefore lead to the exclusion of many more galaxies from the analysis (see
Table 2 and Table 3). If a galaxy is classified as one type in one diagram and
as a different type in the remaining diagrams (whether we are using two or
three of the diagrams), it will be referred to as Ambiguous. Additionally, we
also apply the diagnostic criteria outlined by Cid Fernandes et al. (2010)
which allows the differentiation between two very distinct classes of galaxies
that overlap in the LINER region of the BPT diagrams, namely galaxies hosting
a weak AGN and ”retired galaxies”. Retired galaxies have stopped forming stars
and are ionized by their hot evolved low-mass stars, i.e. post-AGB stars. This
differentiation can be achieved by using an equivalent width (EW) cut of
EW($H\alpha)>3$ ÅA. We use the EW(H$\alpha$) from the DAP and also measure its
average (see equation 1) in the various apertures. We apply this EW($H\alpha$)
cut to all galaxies above star-forming demarcation lines (Kewley et al.,
2006). This step concludes the classification procedure. As mentioned in
Section 3.1., not all galaxies could be classified due to the quality cuts. We
will refer to these low $S/N$ galaxies as the unclassified ones (see Section
4.4).

We point out that we have not taken into account the inclination angles of the
individual galaxies, meaning that all the circular or squared apertures are
performed on the line-of-sight projected galaxy.

Figure 3: Number of galaxies (y-axis) identified by a specific BPT
classification (see the legend on the top right of the plot) using different
selection aperture sizes (in kpc, x-axis). Each value is colored by the
average H$\alpha$ surface brightness (color-bar) within a 2 kpc aperture. This
classification is done excluding the [O I] BPT diagram. Figure 4: Average
radial profiles of the H$\alpha$ surface brightness stacked according to
specific AGN candidates. The shaded regions correspond to the 16th and 84th
percentiles of each stacked radial profile. The solid green line corresponds
to the AGN selected using a 2 kpc circular aperture, and hollow circles hatch
its shaded area. The black dash-dot line corresponds to the AGN selected by an
aperture of 10 kpc, and dots hatch its shaded area. Finally, the blue dashed
line corresponds to the AGN selected by the 10 kpc aperture but excludes the
AGN selected by the 2 kpc aperture. The last line has a shaded area hatched by
hollow squares.

###  3.3 Aperture effects on BPT AGN Selection

In this Section, we explore the behavior of an aperture-dependent
classification. To do so, we investigate the BPT classification as described
in Section 3.2 in 60 different apertures of different sizes, defined as
follows:

  * •

20 circular-shaped apertures with sizes ranging from 0.5 kpc to 10 kpc in
diameter in steps of 0.5 kpc .

  * •

20 squared-shaped aperture with sizes ranging from 0.5$\arcsec$ to 10$\arcsec$
on the side in steps of 0.5 $\arcsec$.

  * •

20 circular-shaped apertures with sizes ranging from 0.1 Reff to 2 Reff in
diameter in steps of 0.1 Reff.

In Figure 2 (bottom right plot), we show an example of the behavior of the
emission line ratios from three different targets when using 20 circular-
shaped apertures. This reveals the dependence of the aperture on our
classification. In Figure 2 (see the two upper plots and the one on the bottom
left) we show how the number of galaxies classified as AGN changes as a
function of an increasing kpc-, arcsecond- and effective radius (Reff)-based
aperture, respectively. For simplicity, we use the term ‘AGN’ to refer to
‘galaxies classified as AGN’ hereafter.

As expected, in the smallest apertures, independent of the specific unit, the
number of galaxies classified as AGN is similar. The number of AGN in Reff-
based apertures, though, drops steeply (compared to the other apertures) and
reaches its minimum at $\sim$0.7-1 Reff. This happens because the step that we
use based on effective radius aperture reaches the outerskirts of the galaxies
faster, leading to fewer galaxies classified as AGN. In the $\sim$7500
galaxies that we were able to classify (see Table 2), the average Reff
corresponds to $\sim$4.63 kpc, meaning that an aperture of 0.7 Reff will have
an average size of $\sim$6.5 kpc in diameter.

We also observe an initial decrease in the total number of classified AGN in
the kpc- and arcsec-based apertures. We then observe an increase in classified
AGN at larger aperture sizes in all panels. The increasing number of AGN at
larger aperture sizes is most notably seen in the kpc- and arcsec-based
apertures (see the top panels of Figure 2) beyond $\sim$6 kpc and $\sim$7′′,
respectively (see Section 4.1). In the Reff-based apertures, the number of AGN
increases beyond $\sim$1.2 Reff.

For all the targets in MaNGA DR17, we provide FITS tables containing all of
our relevant measurements (emission line ratios and H$\alpha$ EWs, if
available) in all here investigated apertures. We report the BPT class of each
galaxy (before applying the EW cut and excluding the [O I]6300/H$\alpha$
diagram) and the resulting AGN catalog for each specific aperture. We provide
these catalogs as part of the supplementary material. We show an example of
one of our catalogs in Table 1, specifically for the 2 kpc-based
classification. Note that (as discussed in Section 3.2) for a galaxy to be
classified as an AGN, our procedure not only requires the emission-line ratios
to be located above the star-forming demarcation lines (see Kewley et al.,
2006) but also to have an EW(H$\alpha$) greater than 3 (whether they are
selected as Seyfert or LINER). For example, in row number 8528 the target is
classified as Seyfert initially, but because of a low EW(H$\alpha$) this
target fails to meet our final AGN criteria. The same is true for the LINER
galaxy in row number 8529. Due to our $S/N$ criteria, some values are stored
as NaN, and thus we do not assign any classification (e.g. 8531, note that
these targets do not even enter the Ambiguous BPT classification). In row
number 8535, a Seyfert did meet the EW(H$\alpha$) cut and is selected as an
AGN candidate. In that case, the [O I]/H$\alpha$ column is NaN, but this does
not impact the classification as [O I]/H$\alpha$ is not used here. The
classifications and measurements in these catalogs can be used according to
the science goals. The most straightforward advantage is being able to choose
parameters and classifications from a specific aperture. For example, one can
alternatively decide to relax on the EW(H$\alpha$) criterion when selecting
AGN (if needed), and select AGN from the Seyfert and LINER population with
EW(H$\alpha$)$>1.5$ ÅA. Therefore, it is possible to easily extract modified
classifications from these catalogs.

Table 1: BPT classification (without using [O I]/H$\alpha$), AGN selection, emission line-ratios, and equivalent widths values for the MaNGA galaxies. This is one out of 60 tables, each corresponding to a different aperture (see Section 3.3). We show our selection considering a 2 kpc aperture. From the first to the last column, the table is shown as it is stored in the supplementary data. The first column reports the ID of the row. The second column reports the Plate and IFU of the corresponding target. In the third column, a boolean array is stored to flag a galaxy as an AGN if the value is 1 and 0 if it is not an AGN (i.e., Seyferts and LINERs with EW(H$\alpha)>3$ ÅA). From the fourth to the eighth column, the BPT class is shown. We abbreviate Seyferts, LINERs, Star-forming, Composite and ambiguous classification as SY, LI, SF, CM, and AM, respectively (however, in the fits table, they are tagged as their full name). From the ninth to the twelfth column, the emission line flux ratios are given as logarithmic bases of 10. Lastly, the last column stores the information of the equivalent width in Å. If a value is tagged as $nan$, the emission line did not pass our S/N criteria (see Section 3.2.) ROW | MANGAID | AGN | SY | LI | SF | CM | AM | [O III]/H$\beta$ | [N II]/H$\alpha$ | [S II]/H$\alpha$ | [O I]/H$\alpha$ | EW_H$\alpha$
---|---|---|---|---|---|---|---|---|---|---|---|---
| Plate-IFU | Boolean array: $0=$ False $\ldots$ $1=$ True | log10 | log10 | log10 | log10 | Å
0 | 10001-12701 | 0 | 0 | 0 | 1 | 0 | 0 | -0.03 | -0.56 | -0.37 | -1.32 | 22.25
1 | 10001-12702 | 0 | 0 | 0 | 0 | 1 | 0 | -0.18 | -0.3 | -0.23 | -0.63 | 5.03
⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮
8526 | 9040-3704 | 0 | 0 | 0 | 0 | 0 | 1 | 0.12 | -0.22 | -0.28 | -1.03 | 7.35
8527 | 9040-6101 | 0 | 1 | 0 | 0 | 0 | 0 | 0.65 | 0.17 | -0.07 | nan | 1.07
8528 | 9040-6102 | 0 | 1 | 0 | 0 | 0 | 0 | 0.36 | -0.05 | -0.28 | nan | 2.22
8529 | 9040-6103 | 0 | 0 | 1 | 0 | 0 | 0 | 0.28 | 0.02 | 0.2 | 0.08 | 0.3
8530 | 9040-6104 | 0 | 0 | 0 | 1 | 0 | 0 | -0.48 | -0.34 | -0.69 | -1.51 | 9.8
8531 | 9040-9101 | 0 | 0 | 0 | 0 | 0 | 0 | nan | nan | nan | nan | nan
8532 | 9040-9102 | 1 | 0 | 1 | 0 | 0 | 0 | 0.09 | 0.09 | -0.17 | -0.78 | 6.17
8533 | 9041-12701 | 0 | 0 | 0 | 1 | 0 | 0 | -0.49 | -0.39 | -0.59 | -1.61 | 14.0
8534 | 9041-12702 | 0 | 0 | 0 | 0 | 1 | 0 | -0.31 | -0.32 | -0.52 | -1.33 | 6.3
8535 | 9041-12703 | 1 | 1 | 0 | 0 | 0 | 0 | 0.49 | -0.07 | -0.27 | nan | 3.47
⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮
10241 | 9894-9102 | 0 | 0 | 0 | 1 | 0 | 0 | 0.51 | -1.32 | -0.71 | -1.54 | 114.6
Figure 5: Filled blue circles correspond to a comparison between measurements
of EW(H$\alpha$) (right-hand plot) and [OIII]/H$\beta$ ratio (left-hand plot)
from our 2 kpc aperture (y-axis) and $3\arcsec\times 3\arcsec$ aperture
(x-axis). The measurements shown here correspond to the full MaNGA sample. The
solid black line in both plots shows the one-to-one ratio that the values
should follow if they had the same value.

###  3.4 Aperture effects on BPT type classification

In Figure 3 we show how the classification in individual BPT sub-types (i.e.,
star-forming, Seyfert, LINER, Composites, Ambiguous) changes as a function of
aperture size. For this analysis, we choose the BPT classification only using
the [N II] and [S II] BPT diagrams along derived from a kpc-based aperture
(the color coding corresponds to the H$\alpha$ surface brightness, which we
will discuss in detail in Section 4.1). Specifically, at an aperture size of
$\sim$ 2 kpc, the number of star-forming galaxies reaches its maximum value,
while the number of Ambiguous objects and the number of Seyfert galaxies
decreases beyond this aperture. At very large aperture sizes, we see a drastic
decrease in the galaxies classified as star-forming as the number of galaxies
classified as LINER and Ambiguous galaxies increases. This effect is explored
in Section 4.1 and in Section 4.3.

##  4 Analysis

###  4.1 The role of diffuse ionized gas

Early studies of radio observations and optical emission lines have provided
evidence for the existence of diffuse ionized components outside bright H II
regions and in kpc-scale layers extending above 1-2 kpc of the Galactic Plane
as well as in nearby galaxies (Hoyle & Ellis, 1963; Reynolds, 1984; Rand et
al., 1990). The diffuse ionized gas (DIG; see Haffner et al., 2009, for a
review) is an important element of the ISM (and currently an active field of
study; e.g., Belfiore et al., 2016; Zhang et al., 2016; Jones et al., 2017;
Wylezalek et al., 2018; Vale Asari et al., 2019; Krishnarao et al., 2020;
Mannucci et al., 2021), corresponding to a widespread (low density $\sim$ 0.1
cm-3), warm gas (10 000 K). Zhang et al. (2016) has shown that DIG has
typically low H$\alpha$ surface brightness and can significantly affect
emission line ratios and thus the typical optical diagnostics, suggesting that
choosing galaxies / spaxels with $\Sigma_{H\alpha}$¿1039 erg s-1 kpc-2 will
result in more reliable H ${II}$ dominated regions. DIG not only can drive
true star-forming galaxies to the Ambiguous and LINER-like BPT classification
regime but also can imitate AGN-like emission line ratios.

Although integral field unit surveys provide an excellent observational tool
for studying the spatially resolved properties of galaxies, individual MaNGA
spaxels resolve properties down to the kpc scales. Thus, it is inevitable that
multiple ionizing sources contribute to individual spaxels, which include
contamination from DIG-dominated regions. In Figure 2 we see that apertures
exceeding 6 kpc, 7′′, and 1.2 $R_{eff}$ start to detect ‘AGN candidates’ at
larger radii. As discussed in Section 4.2, we expect reliable AGN signatures
from the inner kpc regions of true AGN candidates. Therefore, we explore the
potential role of the DIG in affecting the increasing AGN detection rate at
larger apertures.

To do so, we measure the cumulative222We checked the stacking of cumulative
and non-cumulative radial profiles of H$\alpha$ surface brightness (for AGN
candidates). The negligible differences do not impact the discussion in this
section. We focus on the cumulative contribution of this parameter motivated
by taking into account the effects of an increasing aperture-based selection.
radial profiles of the H$\alpha$ surface brightness for all MaNGA targets
following the procedure in Section 3.2. We stack the cumulative radial
profiles of the AGN-selected galaxies from a 2 kpc (diameter) aperture (419
galaxies) and a 10 kpc aperture (381 galaxies). Additionally, we compute the
stacked radial profile using the AGN candidates that were selected by the 10
kpc aperture, excluding the AGN that were selected in both the 2 kpc and 10
kpc aperture (158 galaxies). The latter was done only using the [N II] and [S
II] BPT diagrams. This is shown in Figure 4. All the AGN selected galaxies
from a 2 kpc (diameter) aperture (green solid line) show a high central
H$\alpha$ surface brightness ($\Sigma_{H\alpha}\sim$ 1039.5 erg s-1 kpc-2) and
a higher cumulative radial profile when compared to the AGN selected only
based on the 10 kpc aperture (i.e., excluding the ones selected from the 2 kpc
aperture). These 10 kpc AGN candidates display a lower central surface
brightness ($\Sigma_{H\alpha}$¡1039 erg s-1 kpc-2). Both populations (2 kpc
and 10 kpc selected) show significantly different levels and distributions of
H$\alpha$ surface brightness at every radius, with almost a dex of difference.

We relate this systematic decrease of the H$\alpha$ surface brightness to be
consistent with an increasing contribution from DIG regions. This explains the
increase of galaxies classified as AGN in larger apertures observed in Figure
2, which suggests that an increasing aperture would lead to the inclusion of a
DIG-biased population of AGN candidates. We further study this in Figure 3,
where we show the number of AGN candidates with a specific classification
(each represented with a different shape) when using different selection
apertures. The color map in this Figure shows the average H$\alpha$ surface
brightness measured in a 2 kpc aperture for each data point. Here, the number
of targets classified as LINER (circles) and Ambiguous (triangles) galaxies
increases at larger apertures while the average H$\alpha$ surface brightness
(measured at a 2 kpc aperture) decreases and is generally at a low level. This
is consistent with the results from Belfiore et al. (2016), where Low
ionization emission-line regions (LIER) dominate around $\Sigma_{H\alpha}\sim$
1038 (see Figure 7 in Belfiore et al., 2016), suggesting that these objects
may be strongly contaminated by DIG regions.

Objects classified as Composite, AGN, Seyferts or star forming galaxies have
higher central H$\alpha$ surface brightness at all apertures
($\Sigma_{H\alpha}\gtrsim 10^{38}$). Galaxies classified as Composites have
roughly a constant central H$\alpha$ surface brightness at all apertures. We
observe that objects that are still classified as Seyfert galaxies when
selected from very large kpc apertures are the ones who show the highest
central H$\alpha$ surface brightness. This is very likely due to the fact that
the contamination from DIG at larger radii must be compensated by higher
central H$\alpha$ surface brightness at the center for a galaxy to still make
it into the Seyfert regime of the BPT classification.

Figure 6: For all MaNGA galaxies, we show the stellar mass versus star
formation rate (derived from H$\alpha$), extracted from the Pipe3D (Sánchez et
al., 2016) catalog. We color each target in orange or purple respectively on
the scatterplot to determine whether or not that specific galaxy was
considered in the BPT classification scheme (meaning that it met our $S/N$
criteria specified in Section 3.1). This plot is based on the classification
using a 2 kpc aperture without considering the [O I] BPT diagram. The
histograms show the distributions of stellar mass (top) and star formation
rate (right). Distributions for the BPT classified and unclassified galaxies
correspond to the dashed and solid histograms, respectively. The distribution
of the whole MaNGA sample is shown in the black bold histogram. The
distributions of the histograms are normalized, and the y-axis in the
histograms corresponds to the normalized counts. Figure 7: Similar to Figure
6, we focus here on the BPT classified sample (orange), highlighting the AGN
candidates (dark red) selected from a 2 kpc aperture (see Section 3.2). We
show the main sequence for star-forming galaxies (SFMS) and the Retired
Galaxies Sequence (RGS) from Cano-Díaz et al. (2016) using blue and red dashed
lines, respectively.

###  4.2 Classifications based on a 3$\arcsec$ and 2 kpc aperture

Many current and early surveys have studied the general properties of galaxies
based on a single fibre 2$\arcsec$ (e.g., Dawson et al., 2013) or 3$\arcsec$
apertures (e.g., Gunn et al., 2006). Consequently, AGN identifications have
been frequently done, especially for galaxies observed within SDSS surveys,
based on measurements in these apertures. For MaNGA, for example, key AGN work
has been done by classifying AGN using optical diagnostics on a 3$\arcsec$
aperture (Rembold et al., 2017; Sánchez et al., 2018). We, therefore, select
the same aperture (3$\arcsec$) to study the impact of low redshift optically-
selected AGN candidates when compared to a kpc-based aperture catalog. Our
kpc-based aperture is used with the intention of reducing aperture effects
(given by the redshift range of MaNGA galaxies) on selected AGN candidates and
to focus on the compact AGN region.

AGN can ionize the gas in host galaxies up to kpc scales. This is commonly
referred to as the narrow line region (NLR, see Netzer, 2015). The typical
sizes of the NLR have been studied over the past decades (e.g., Bennert et
al., 2006; Chen et al., 2011; Netzer, 2015; Padovani et al., 2017), with a
consensus of a lower limit between hundreds of pc up to $\sim$1 kpc of radius.
Therefore, the ionizing source of some AGN can be easily diluted even at small
apertures.

We note that the smaller the aperture, the more galaxies are classified as AGN
(see Figure 2). Specifically, we find that 59 AGN candidates from the 0.5 kpc
catalog are not classified as AGN in the 2 kpc catalog. The median redshift of
these 59 galaxies is 0.032, while the median redshift of the AGN population at
2 kpc is 0.045. This corresponds to a median resolution of 1.05 kpc and 1.58
kpc, respectively333We can estimate the median spatial resolution of these
AGN-candidate population using the spatial element relation $\delta\approx
35z$ kpc (see Bundy et al., 2014).. Indeed, this suggests that the NLR sizes
of these 59 AGN candidates (if proven to be true AGN at all) are small enough
to not be resolved by the MaNGA survey and therefore get diluted when using a
2 kpc aperture. If we perform the same comparison between the 2 kpc and 5 kpc
catalog, we find that 122 galaxies are not classified as AGN in the 5 kpc
catalog. The median resolution of these 122 galaxies is 1.12 kpc and the
median resolution of the AGN population in the 5 kpc catalog is 1.60 kpc. When
we carry out such comparisons now using the arcsecond or effective radius-
based apertures, we find that the galaxies that are not classified as AGN in
larger apertures still have a similar median kpc resolution compared to the
ones at smaller apertures. We interpret this as a clear suggestion that the
kpc-based aperture is more consistent with the physical properties of AGN.

Furthermore, MaNGA samples can achieve a median spatial resolution of about
1.37 kpc (Wake et al., 2017). This spatial resolution and the suggested
physical sizes of the NLR motivate us to use an aperture of 2 kpc in diameter.
To carry out a comparison between catalogs based on different apertures (see
Section 5), we choose the following four catalogs:

  * •

circular-shaped aperture with a 2 kpc diameter using full BPT diagnostics
([NII]/H$\alpha$, [SII]/H$\alpha$, [OI]/H$\alpha$).

  * •

circular-shaped aperture with a 2 kpc diameter excluding the [OI]/H$\alpha$
BPT diagnostic.

  * •

square-shaped aperture with a 3$\arcsec$ size on the side using full BPT
diagrams ([NII]/H$\alpha$, [SII]/H$\alpha$, [OI]/H$\alpha$).

  * •

square-shaped aperture with a 3$\arcsec$ size excluding the [OI]/H$\alpha$ BPT
diagnostic.

We compute each AGN catalog following the procedure described in Section 3.2
including the EW(H$\alpha$) cut. The full classification and the number of
galaxies corresponding to each class (Star-forming, Seyfert, LINER, Composite
and Ambiguous, and the final AGN candidates) for these apertures is shown in
Table 2 and 3, as well as the $Total$ number of galaxies that were used in
that BPT classification. In these tables, we also report the number of
galaxies that the different selections have in common.

###  4.3 Hosts of emission line galaxies

The number of successfully BPT-classified galaxies is an increasing number of
decreasing $S/N$ cuts. The BPT classification becomes more biased towards less
massive galaxies (Brinchmann et al., 2004) as a function of increasing $S/N$
cut (see below). To present the impact of using our $S/N$ limit ($S/N>3$), in
Figure 6 we show the mass distribution (top histogram) and star formation rate
(right-hand histogram) of the classified and unclassified galaxies. We use the
stellar masses and star formation rates derived from the PIPE3D. In
particular, the star formation rate in each galaxy is estimated with the dust-
corrected H$\alpha$ luminosity using the relationship proposed by Robert C.
Kennicutt (1998). This measurement should be treated as an upper limit since
the PIPE3D uses the integrated H$\alpha$ flux, which could include
contamination of regions where the ionization source is not related to recent
star formation events (see Sánchez et al., 2022).. We note that our
classification is biased towards higher star formation rates and lower stellar
masses. This is an expected (and known) caveat given that optical emission
lines are sensitive to the ongoing star formation in galaxies (Kewley &
Ellison, 2008). High-energy photons produced by young and hot stars are able
to produce powerful emission lines through ionization (Kewley et al., 2001).
However, galaxies whose stellar population are dominated by old stars are not
generally able to produce the strong emission lines needed for typical optical
classifications (e.g., satisfying S/N specific criteria). Indeed, Kauffmann et
al. (2003b) finds that SDSS galaxies with higher masses present older stellar
populations and that the fraction of high-mass galaxies with recent star
formation is lower than in less massive galaxies. We further discuss how this
impacts our AGN selection in Section 4.4.

We found very little or no change in the stellar mass distribution of the AGN
candidates when selecting them by the different aperture steps, regardless of
the used unit (kpc, arcsec, or Reff). The typical stellar mass distribution
has a median of $\sim$1011 M⊙. Despite the similarity in their stellar mass
distributions, we do not expect an accurate AGN BPT selection when using
larger apertures. This similarity can occur due to the presence of a
population of galaxies with DIG-dominated regions, whose properties can mimic
AGN-like signatures (these galaxies are also massive, but older; see Section
4.1).

The star formation rate (derived from the integrated H$\alpha$ flux)
distribution from AGN selected using small apertures is also in agreement for
AGN selected at large apertures, selecting very low SFR(H$\alpha$) populations
at very large apertures. Our AGN candidates (using our 2 kpc catalog) follow
an offset lying mostly below the main sequence of star-forming galaxies (SFMS,
see Figure 7) and above the Retired Galaxies Sequence (RGS). Our AGN
candidates are located mostly in the green valley region (Salim, 2014). Leslie
et al. (2016) used optical diagnostics classification and found that Seyfert-
classified galaxies (with $z<0.1$) lie mostly below the SFMS. Schawinski et
al. (2009) find similar results using a sample of obscured and unobscured AGN,
concluding that their AGN host galaxies ($0.01<z<0.07$) are more likely to be
found in the green valley. Similar results have been discussed in a sample
with higher redshifts ($z<2.0$, Pović et al., 2012), suggesting that these AGN
could be part of a transitional phase, driving star-forming galaxies into a
more quiescent phase. Indeed, further evidence has been found by Le Fèvre, O.
et al. (2019) in even higher redshift ranges ($2<z<3.8$), where strong C III]
emitters (consistent with AGN sources) are more likely to be found below the
SFMS (with an increasing outflow velocity in the strongest C III] emitters),
suggesting that negative feedback quenches star-forming galaxies into a
quiescent population. We will further discuss the outflow properties in MaNGA-
AGN selected galaxies in Albán in prep.

###  4.4 Hosts with very low S/N or no emission lines

We find that the unclassified galaxies (with $S/N<3$ criteria) are typically
more massive and have lower star formation rates (see Figure 6) compared to
galaxies with higher $S/N$. This is an expected behavior (e.g., Brinchmann et
al., 2004), that happens since these galaxies are dominated by old stellar
populations and have low cold gas fractions (Wylezalek et al., 2022). For
example, Brinchmann et al. (2004) found (in a sample of SDSS galaxies) that
the flux of the [O III]$\lambda$5008 emission line decreases with stellar
mass, which thus typically has lower $S/N$ (consistent with the discussion in
Section 4.3). This trend reverses for the very high mass galaxies due to the
prevalence of AGN at these stellar masses (seen in Kauffmann et al., 2003a).

Using our 2 kpc aperture classification, we take the 4000 ÅA (D4000) break as
a proxy of stellar age (Kauffmann et al., 2003a, b). While the MaNGA sample
and our classified targets have a median $D_{4000}$ value of $\sim 1.36$ and
$\sim 1.46$ respectively, our unclassified sample of galaxies is typically
older, with a median of $D_{4000}\sim 1.71$ and with very low EW(H$\alpha$)
with a median of $\sim$0.22 ÅA. We also find that these galaxies typically
occupy the Green Valley and Quiescent region on the D4000 vs. stellar mass
diagram. Thus, we typically exclude old and high stellar mass galaxies with
low star formation rates. A fraction of these discarded galaxies are
classified as AGN through radio-selection methods in Comerford et al. (2020),
where they discuss the radio-mode AGN as a possible final phase in the AGN
evolution.

In a very early release of the MaNGA survey, Belfiore et al. (2016) used 646
galaxies to study the spatially resolved BPT diagrams focusing on low
ionization emission-line region (LIER) galaxies. They propose a classification
scheme where 151 galaxies had spaxels with very low signal-to-noise in the
emission lines or no emission lines at all. They use the spaxels of all the
galaxies that were not able to be classified and compute their EW(H$\alpha$),
finding a median of 0.5 ÅA. For our unclassified sources, we also compute the
median EW(H$\alpha$) from the central spaxel as well as the entire footprint,
finding that the values do not exceed 0.4 ÅA. This median EW(H$\alpha$) is
consistent with Belfiore et al., who classify line-less galaxies as the ones
with EW(H$\alpha)<1.0\r{AA}$ within one effective radius. Our AGN selection
excludes, by default, the galaxies whose aperture-averaged EW(H$\alpha$) is
lower than 3 ÅA. Therefore, we do not expect a significant contribution of AGN
candidates (based on our selection algorithm) coming from galaxies with no
emission lines due to our $S/N$ cut.

Table 2: MaNGA-DR17 catalog for optical diagnostics classification excluding
[O I] BPT diagram using a 2 kpc and a $3\arcsec\times 3\arcsec$ aperture.

$\begin{array}[]{llll}\hline\cr\vskip 3.0pt plus 1.0pt minus 1.0pt\cr
Class&2\leavevmode\nobreak\ kpc&3\arcsec\times 3\arcsec&Crossmatch\\\ \vskip
3.0pt plus 1.0pt minus 1.0pt\cr\hline\cr\vskip 3.0pt plus 1.0pt minus 1.0pt\cr
Star-forming&3574&3623&3538\\\ Seyfert&396&343&313\\\ LINER&1959&2235&1824\\\
Composite&1038&1085&972\\\ Ambiguous&526&538&416\\\ Total&7493&7824&7404\\\
\hline\cr AGN&419&399&378\\\ \hline\cr\vskip 3.0pt plus 1.0pt minus
1.0pt\cr\end{array}$

444Only around 7500 galaxies from the total MaNGA sample had available (enough
$S/N$, see Section 2) emission-line ratios when excluding the [O I]/H$\alpha$
BPT diagram to conduct this classification.

Table 3: MaNGA-DR17 catalog for optical diagnostics classification including
the [O I] BPT diagram using a 2 kpc and a $3\arcsec\times 3\arcsec$ aperture.
$\begin{array}[]{llll}\hline\cr\vskip 3.0pt plus 1.0pt minus 1.0pt\cr
Class&2\leavevmode\nobreak\
kpc&3^{\prime\prime}x3^{\prime\prime}&Crossmatch\\\ \vskip 3.0pt plus 1.0pt
minus 1.0pt\cr\hline\cr\vskip 3.0pt plus 1.0pt minus 1.0pt\cr Star-
forming&2769&2850&2679\\\ Seyfert&308&278&255\\\ LINER&391&400&279\\\
Composite&591&589&528\\\ Ambiguous&2262&2582&1918\\\ Total&6321&6699&6124\\\
\vskip 3.0pt plus 1.0pt minus 1.0pt\cr\hline\cr AGN&407&395&371\\\
\hline\cr\end{array}$

##  5 Comparison between AGN catalogs

To test our classification catalogs, in this section we aim to study the
impact of aperture size (2 kpc and 3′′ specifically in this section) on the
final AGN classification. We also explore the reasons for the discrepancies
and/or cross-matches between reported catalogs from previous literature
(described in Section 2) and our classification described in Section 2. When
comparing our catalogs to those from the literature, we restrict our catalogs
to match the corresponding MaNGA sample (e.g., if an AGN selection was made
using the MPL5 sample, we only use our AGN selected from that specific
subsample).

###  5.1 Internal comparison of our catalogs

As seen in Table 2 and Table 3 (see also Figure 2), the total number of
classified galaxies (after the $S/N$ criteria) is greater when using a
$3\arcsec\times 3\arcsec$ aperture than the 2 kpc aperture. The latter is not
surprising, given that more than half of the sample has a redshift which leads
to the aperture of 3$\arcsec$ to correspond to physical sizes greater than 2
kpc. This means that for an aperture of $3\arcsec\times 3\arcsec$ in this
sample, more pixels are considered, increasing the chance of meeting our $S/N$
criteria.

Even though there are more classifiable targets in the $3\arcsec\times
3\arcsec$ aperture catalog, fewer targets are selected as AGN by the
$3\arcsec\times 3\arcsec$ aperture. Using only the [N II]/H$\alpha$ and [S
II]/H$\alpha$ BPT diagrams, we identify 41 galaxies that are selected to be
AGN based on the 2 kpc aperture but not by the $3\arcsec\times 3\arcsec$
aperture. We show these galaxies in Figure 8, revealing the impact on the BPT
distribution when using the kpc vs. the arcsecond based aperture. The most
common reason for this disagreement is that the (larger) $3\arcsec\times
3\arcsec$ aperture pushes more targets to the star-forming regime. We observe
that the lowest redshift galaxies are the ones that least change the position
in the diagrams as the difference in physical size becomes more relevant at
higher redshifts. We find that 11 out of these 41 galaxies do not meet the
EW(H$\alpha$) criterion (EW(H$\alpha$)¿3Å) anymore.

If we reverse the comparison, we find that 21 galaxies are selected as AGN
candidates based on the $3\arcsec\times 3\arcsec$ aperture but not by the 2
kpc aperture. Most of them were not selected as AGN because they did not
satisfy the EW(H$\alpha$) criteria: eleven of these did not satisfy the
EW(H$\alpha$) cut, and the remaining ones were very close to the AGN division
lines from the [N II] and [S II] BPT diagrams, and eight are classified as
Ambiguous, one as star-forming and one as composite object in the 2 kpc
aperture. Similar results are found when including the [O I]/H$\alpha$ diagram
in the selection algorithm, providing less AGN due to the typically low $S/N$
in the [O I] emission line. We conclude that a 3′′ based aperture is more
prone to shift optically selected AGN galaxies (using a 2 kpc aperture) from
the MaNGA survey towards a more star-forming appearance.

To quantify the redshift dependence on the offset of the BPT classification,
we focus on the [N II] BPT diagram and measure the distance between the BPT
position of both kpc and 3$\arcsec$ apertures. We name this offset parameter
$\Delta(NII)$, measured as follows:

$\Delta(NII)=\sqrt{(O3_{ap1}-O3_{ap2})^{2}+(N2_{ap1}-N2_{ap2})^{2}}$ (2)

Where $O3_{ap}=[OIII]/H\beta_{ap}$ and $N2_{ap}=[NII]/H\alpha_{ap}$, with $ap$
the type of aperture used.

Figure 8: BPT distribution for galaxies that were selected by the 2 kpc
aperture (circles) but not by the $3\arcsec\times 3\arcsec$ aperture
(squares); each pair is connected by a black line. Both catalogs are performed
as in Section 3.2 excluding the [OI]/H$\alpha$ diagram. Each target is colored
by its redshift. We plot the empirical division lines that will give each
target a specific classification (e.g., AGN-like galaxy, Composite object, or
HII-star forming galaxies): red lines correspond to Kewley et al. (2001), and
the blue dashed line on the left plot corresponds to Kauffmann et al. (2003a).

We display the distribution of $\Delta(NII)$, with $ap1=2\leavevmode\nobreak\
kpc$ and $ap2=3^{\prime\prime}$, as a function of redshift in Figure 9. We
highlight in purple squares the galaxies that are kicked in the direction of
the HII-BPT region of the NII BPT diagram. We use negative values (see the
orange circles) to show the galaxies that are shifted toward the AGN region.
The latter shows that the discrepancy between flux ratio measurements
(equation 2) from a 2 kpc and a 3′′ aperture in the NII-BPT diagram is more
prone to increase, showing greater scatter at higher redshift. We see a
greater concentration of galaxies moving towards a more HII-like region
(purple squares) than galaxies shifting towards an AGN-like classification
(orange circles) for these optically AGN-selected galaxies. The empty symbols
(squares or circles) show the AGN candidates selected in one aperture but not
in the other. The empty squares (AGN in the 2 kpc aperture but excluded by the
3′′ aperture) reveal that more AGN candidates are being excluded by the 3′′
aperture, supporting our findings in Figure 8. The discrepancies in this
comparison are more prominent for higher redshifted galaxies.

Figure 9: Magnitude of the offset between the position of a 3$\arcsec$ and 2
kpc aperture in the [NII]-BPT diagram as a function of the redshift of the
target. The data displayed in this plot corresponds to the $\Delta(NII)$ from
AGN-selected galaxies using a 2 kpc aperture (and excluding the [O I] BPT
diagram). Orange solid circles correspond to galaxies whose offset slope
points towards the AGN BPT region (outside the HII-BPT region). In contrast,
the solid purple circles correspond to the opposite, moving galaxies towards a
more star-forming appearance. Empty symbols (squares or circles) correspond to
galaxies that were selected as AGN in one aperture but not in the other (e.g.,
the empty squares are exactly the same objects that are shown in Figure 5,
meaning that they were selected as AGN by the 2 kpc aperture but not for the
3$\arcsec$ aperture). The dashed vertical lines correspond to the redshift
values where the squared arcsecond aperture corresponds to the circumscribed
and inscribed square of a circle of radius 1 kpc, respectively.

We also see that the impact of the aperture size remains almost negligible
when the physical size of the arcsecond-based aperture matches the kpc
aperture size, as expected. We show in dashed lines the circumscribed and
inscribed square of a circle of radius 1 kpc, displaying the redshift range
where the squared aperture mostly matches the circular-kpc based aperture.
Furthermore, we find no correlation between $\Delta(NII)$ and the axis ratio
(b/a) when looking to our AGN candidates.

###  5.2 Comparison to the AGN catalog from Sánchez et al. 2018

We compare the MaNGA AGN catalog published in Sánchez et al. (2018) (which is
done using the Pipe3D output; see Section 2.2) to our $3\arcsec\times
3\arcsec$ aperture catalog. To make a consistent comparison, we use the same
EW(H$\alpha$) criteria (i.e., adapting to $EW(H\alpha)>1.5$ Å) following the
procedure in Section 3.2 and using BPT diagnostics excluding the [O
I]/H$\alpha$ diagram. Mostly all of their selected AGN are also selected in
our catalog. We find two AGN candidates with differing classifications: one
due to a disagreement in position on the BPT diagram, and the other not
meeting the $EW(H\alpha)$ criteria (solid diamonds in Figure 10). When
considering the full BPT scheme, only one extra miss-match occurs, due to the
lack of classifiable pixels ($S/N>3$) in the [O I] flux (from the DAP). If we
make the same comparison with our 2 kpc aperture catalog and use the full BPT
scheme, five galaxies that are selected by Sánchez et al. are not selected by
our catalog. Two with no classifiable pixels in [O I], two not satisfying the
$EW(H\alpha)$ criteria, and one with an Ambiguous BPT position.

Figure 10: We display the BPT position of all the AGN candidates that are in
disagreement between our 3$\arcsec$ aperture catalog (without [OI]/H$\alpha$
BPT diagram) and the catalog from Sánchez et al. (2016). Solid circles
correspond to targets selected by our catalog, but not by theirs. Solid
diamonds correspond to the opposite (AGN selected by them, but not by our
catalog). The color on the scatter plots corresponds to the logarithmic
EW(H$\alpha$). The red lines and the blue dashed line on the left plot
correspond to Kewley et al. (2001) and Kauffmann et al. (2003a), respectively.

However, there are more than 40 AGN candidates (if we compare to any of our
four catalogs using $EW(H\alpha)>3$ Å; see Section 4.2) that were not selected
by Sánchez et al., and more than 100 when we relax to $EW(H\alpha)>1.5$ Å. The
latter is shown in Figure 10, where we show our emission line ratio
measurements and adapt to their $EW(H\alpha)>1.5$ Å criteria. A visual
inspection suggests the fact that many AGN candidates from our catalog that
are not selected by Sánchez et al. (2018) are relatively close to the Kewley
et al. (2001) demarcation line (see Figure 10). Discrepancies for these
targets can be related to the quality cut that we make at $S/N>3$ for each
emission line measured by the DAP, which excludes that specific pixel from our
procedure, having slight flux ratio changes from the ones measured by Sánchez
et al. However, Sánchez et al. (2018) reported 302 galaxies above the AGN
division line (Kewley et al., 2001; Kauffmann et al., 2003a), suggesting that
the discrepancies for the galaxies far away from the demarcation lines on the
AGN selection are mostly related to the $EW(H\alpha)$ measurements. These
discrepancies are a consequence of a different treatment of the stellar
continuum when measuring $EW(H\alpha)$ (already reported in Thomas et al.,
2013; Wylezalek et al., 2018) and possibly due to the difference in the $S/N$
cut criteria.

Figure 11: Blue circles correspond to a comparison between measurements of
EW(H$\alpha$) values (left-hand plot) and [OIII]/H$\beta$ ratios (right-hand
plot) from this work (y-axis; using a 3$\arcsec$ aperture) and Sánchez et al.
(x-axis) in a logarithmic scale. The measurements shown here correspond to the
AGN-candidate subsample from Sánchez et al. The solid black line in both plots
shows the one-to-one ratio that the values should follow if they had the same
value. The gray dashed line in the plots shows a 1-D polynomial fit to the
distribution.

In Figure 11, we contrast the $EW(H\alpha)$ measurements from both AGN
catalogs (left plot). A 1-D polynomial suggests that the $EW(H\alpha)$
measurements are mostly in agreement. However, it can be seen that the one-to-
one comparison of the $EW(H\alpha)$ values shows an important scatter. At
lower $EW(H\alpha)$s, our measurements (from our 3$\arcsec$ procedure) predict
typically larger values than those measured by Sánchez et al. This supports
the idea that the main discrepancy in the AGN selection comes from the
differences between the $EW(H\alpha)$ values. In the right-hand plot, we
compare the [O III]/H$\alpha$ ratio measurements. In our catalog, [O
III]/H$\alpha$ has typically greater values (also reported in Belfiore et al.,
2019, when comparing the DAP measurements to Pipe3D, due to a different choice
of stellar continuum treatments). The latter is expected since there is a
better agreement in the AGN selection before applying the $EW(H\alpha)$
criteria.

A minor extra consideration that could drive the discrepancy is that we are
weighting pixels according to their enclosed fraction with their selected
aperture (see Section 3.2), which we do not expect to have a significant
impact. We find similar results when using our 2 kpc aperture and the
$EW(H\alpha)=1.5$ Å criteria.

###  5.3 Comparison to the AGN catalog from Rembold et al. 2017

Using our $3\arcsec\times 3\arcsec$ aperture and excluding the [O I]/H$\alpha$
BPT diagram we find that only one target that does not crossmatch with the AGN
candidates from Rembold et al. (2017). However, 14 galaxies drop from the
crossmatch when we adapt to the $EW(H\alpha)>3$ Å criteria. We display the
miss-matched targets in Figure 12 and we force the contrast of the
$EW(H\alpha)$ color-bar to have its maximum value at $EW(H\alpha)=3$ (in
logarithmic scale) to better distinguish why such galaxy was not classified as
AGN in our procedure in terms of the EW(H$\alpha$). Furthermore, if we
consider the full BPT scheme (including the [O I]/H$\alpha$ BPT), the latter
behavior holds. When we perform the same comparison with our absolute 2 kpc
aperture, we find one more crossmatched target. When reversing the comparison,
around 36 targets were selected by our catalogs but not by the Rembold
catalog.

Figure 12: BPT distribution of all the AGN candidates that are in disagreement
between our 3$\arcsec$ aperture and BPT excluding [OI]/H$\alpha$ diagram
catalog and the catalog from Rembold et al. (2017). Solid circles correspond
to targets selected by our catalog, but not by theirs. Solid diamonds
correspond to the opposite (selected by them, but not by our catalog). The
color on the scatter plots corresponds to a specific EW(H$\alpha$) range width
whose contrast is forced between $1.0$ ÅA $>EW(H\alpha)>3.0$ ÅA to emphasize
which targets satisfied the $EW(H\alpha)>3.0$ ÅA condition. The red lines
correspond to Kewley et al. (2001) and the blue dashed line on the left plot
corresponds to Kauffmann et al. (2003a).

Including the [OI]-based BPT diagnostic or changing the aperture (between
$3\arcsec\times 3\arcsec$ arcsec or 2 kpc) does not seem to impact the number
of AGN-selected sources, as also found in the comparison with the Sánchez et
al. classification. Five targets are not classified as AGN candidates (but as
Ambiguous) since they fall into the star-forming regime in the [S
II/H$\alpha$] diagram. Most of the extra AGN candidates from (Rembold et al.,
2017) do not satisfy the EW(H$\alpha$) ¿ 3 ÅA criteria. Specifically, the 14
and the further 36 discrepancies (see Figure 12) suggest that a $S/N$ cut
plays an important role for the EW(H$\alpha$) criteria since we use a $S/N>3$.
Low $S/N$ spaxels could be biased by low EW(H$\alpha$) values as this quantity
is measured by the ratio between the emission-line and continuum fluxes
(Thomas et al., 2013). An important discrepancy comes from the difference in
the stellar subtraction from MaStar (MaNGA Stellar Library) when measuring
emission lines (Yan et al., 2019). A smaller impact might be related to the
way in which we measure the flux (see Figure 1).

###  5.4 Comparison to the AGN catalog from Wylezalek et al. 2018

An interesting result from the comparisons conducted above is that as we
increase the aperture (either using a kpc or arcsecond aperture), our catalogs
have fewer AGN in common compared to the catalogs of (Sánchez et al., 2018)
and Rembold et al. (2017). However, the agreement between our catalogs and the
one from Wylezalek et al. (2018) remains almost constant as we increase the
aperture for the selection, with a small increase at greater apertures.

Figure 13: AGN candidates that are in the Wylezalek et al. (2018) catalog but
are excluded by our AGN selection (solid diamonds) using a 2 kpc aperture
without the [O I]/H$\alpha$ BPT criteria with $EW(H\alpha)>3$ Å (with the
contrast forced as in Figure 12). The ones that were selected by our procedure
but not by Wylezalek et al. are shown in filled circles. The dashed and solid
lines have the same interpretation as described in Figure 8.

In contrast to the previous comparisons, we find a greater disagreement
between our AGN candidates and the ones from (Wylezalek et al., 2018). In
Wylezalek et al. (2018), the authors search for AGN signatures at all
galactocentric distances. If we use our 2 kpc diameter aperture catalog
(excluding [O I]/H$\alpha$), we find only 56 targets in agreement (Figure 13).
Changing between a 2 kpc or a 3′′ aperture and the full BPT scheme has no
relevant impact for this comparison. The discrepancies reported in this
comparison are mostly related to the significant differences in the selection
methods. Their method does not use defined apertures, but pixel fractions
(e.g., if a relevant fraction of pixels is classified as AGN); every pixel is
used, whether it is near or far from the center of the galaxy. Wylezalek et
al. (2018) argue that using the full power of spatially resolved spectra
reveals possibly hidden and/or weaker AGN candidates that may have been missed
due to obscuring effects, recently turned-off AGN or AGN that are not located
in the reported center of the MaNGA NSA catalog (due to mergers or off-set
AGN).

###  5.5 Comparison to the multi-wavelength AGN catalog from Comerford et al.
2020

From their 406 AGN-selected galaxies, we find only 73 and 69 galaxies in
agreement from our 2 kpc and 3′′ (excluding [O I]/H$\alpha$) AGN catalog,
respectively when compared to the multi-wavelength AGN catalog from Comerford
et al. (2020) (for a description of this catalog, see Section 2.5). These
numbers decrease as we increase the size of our apertures. A better agreement
is found when we relax the $EW(H\alpha)$ criteria. Around 100 galaxies that
were classified as AGN in Comerford et al. (2020) were not able to be
classified by our 2 kpc aperture catalog due to our $S/N$ cut. In Figure 14 we
display the BPT diagrams highlighting the non-optical classified AGN from the
Comerford et al. (2020) catalog. The AGN sample of Comerford et al. (2020)
seems to not show any preference along with our BPT classification regime.
This highlights the fact that not all AGN can be classified using optical
emission line diagnostics, revealing the known caveats of BPT selection.

Figure 14: We show our MaNGA BPT classification using a 2 kpc aperture. We
highlight the multi-wavelength (based on radio, infrared, broad-line, and
X-ray selection methods, see Section 2.5) selected AGN candidates from
Comerford et al. (2020) with pink-colored star-like symbols.

##  6 Conclusions

We have used the final data release (DR17) of the SDSS-MaNGA survey (10,010
galaxies) to classify galaxies based on their BPT diagnostics. We have created
catalogs using a range of apertures (in units of kpc, arcsecond and effective
radius) that we base our classification on. In each aperture, we measure the
BPT diagnostics and include an $EW(H\alpha)$ cut for the final AGN
classification. We have studied how galaxies can change their classification
as we increase the aperture used for their characterization. Our main results
are as follows:

  * •

MaNGA AGN candidates for small ($<6$ kpc) kpc-based apertures lie below the
main sequence of star-forming galaxies (see Figure 7), in agreement with
previous results (e.g. Sánchez et al., 2018; Comerford et al., 2020). These
AGN candidates are massive with stellar of around $\sim 10^{11}M_{\odot}$ and
a median H$\alpha$-derived star formation rate of $\sim 1.44$
$M_{\odot}\leavevmode\nobreak\ y^{-1}$.

  * •

We show that the number of galaxies classified as AGN decreases with
increasing aperture size up to $\sim$ 6 kpc, 6′′ or $1.2\leavevmode\nobreak\
R_{eff}$, depending on the units used, respectively. We argue that this is due
to the compact nature of the AGN phenomenon. As the aperture size increases,
one gets closer to capturing the entire spectrum of a galaxy, potentially
reducing the dominance of AGN emission and positioning the observed galaxies
towards the Composite or Star-forming region.

  * •

Intriguingly, at apertures greater than 6 kpc, 6$\arcsec$ or
$1.2\leavevmode\nobreak\ R_{eff}$), the trend reverses and the number of
galaxies classified as AGN starts to increase (see Figure 2). The stacked
radial cumulative surface brightness profiles for the AGN candidates that are
only found in very large apertures (but not in the smaller ones) show very low
H$\alpha$ surface brightness (see Figure 4). This is consistent with AGN
selection being strongly contaminated by the effect of DIG-dominated regions.
This implies that using a very large aperture selects fewer true AGN.

  * •

When we compare our catalog with MaNGA AGN aperture-based catalogs from the
literature (using previous data releases) we find the following important
parameter. In addition to the classical BPT line ratios, the measurement of
the $EW(H\alpha)$ is a commonly used and important additional diagnostic. In
our comparisons, we show that different treatments to fit the stellar
continuum have a strong impact on the faint AGN population. This is because
EW(H$\alpha$) measurements are sensitive to small changes of the continuum
model. An AGN selection based on optical diagnostics can therefore vary
greatly between different analyses (see Figures 11, 10, and 12). Additional
discrepancies on the selection of AGN candidates are related to the strong
differences in the selection criteria (see Section 2.4 and Figure 13) and the
choice of the observed wavelength range when classifying galaxies (see Section
2.5 and Figure 14).

With this work, we show that the choice of aperture size impacts optical AGN
selection and galaxy classification. In comparison to single fibre optical
galaxy classification, IFU observations offer a more complete characterization
of the origin of ionizing sources in galaxies (e.g., identifying DIG regions)
and allow one to minimize aperture effects (e.g., using a redshift-independent
aperture), therefore, reducing the bias in AGN selection techniques.

###### Acknowledgements.

D.W. acknowledges support through an Emmy Noether Grant of the German Research
Foundation, a stipend by the Daimler and Benz Foundation and a
Verbundforschung grant by the German Space Agency.
Funding for the Sloan Digital Sky Survey IV has been provided by the Alfred P.
Sloan Foundation, the U.S. Department of Energy Office of Science, and the
Participating Institutions. SDSS-IV acknowledges support and resources from
the Center for HighPerformance Computing at the University of Utah. The SDSS
web site is www.sdss.org.
SDSS-IV 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 Astrofísica 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, Observatario Nacional / MCTI, The Ohio State
University, Pennsylvania State University, Shanghai Astronomical Observatory,
United Kingdom Participation Group, Universidad Nacional Autonoma de México,
University of Arizona, University of Colorado Boulder, University of Oxford,
University of Portsmouth, University of Utah, University of Virginia,
University

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