# Exploring the AGN-ram pressure stripping connection in local clusters

Giorgia Peluso INAF-Padova Astronomical Observatory, Vicolo dell’Osservatorio
5, I-35122 Padova, Italy  Dipartimento di Fisica e Astronomia ”G. Galilei”,
Università di Padova, Vicolo dell’Osservatorio 3, 35122 Padova, Italy
Benedetta Vulcani INAF-Padova Astronomical Observatory, Vicolo
dell’Osservatorio 5, I-35122 Padova, Italy  Bianca M. Poggianti INAF-Padova
Astronomical Observatory, Vicolo dell’Osservatorio 5, I-35122 Padova, Italy
Alessia Moretti INAF-Padova Astronomical Observatory, Vicolo dell’Osservatorio
5, I-35122 Padova, Italy  Mario Radovich INAF-Padova Astronomical Observatory,
Vicolo dell’Osservatorio 5, I-35122 Padova, Italy  Rory Smith Korea Astronomy
and Space Science Institute (KASI), 776 Daedeokdae-ro, Yuseong-gu, Daejeon
34055, Republic of Korea  Yara L. Jaffé Instituto de Física y Astronomía,
Facultad de Ciencias, Universidad de Valparaíso, Avda. Gran Bretaña 1111,
Casilla 5030, Valparaíso, Chile  Jacob Crossett  Instituto de Física y
Astronomía, Facultad de Ciencias, Universidad de Valparaíso, Avda. Gran
Bretaña 1111, Casilla 5030, Valparaíso, Chile  Marco Gullieuszik INAF-Padova
Astronomical Observatory, Vicolo dell’Osservatorio 5, I-35122 Padova, Italy
Jacopo Fritz Instituto de Radioastronomía y Astrofísica, UNAM, Campus Morelia,
A.P. 3-72, C.P. 58089, Mexico  Alessandro Ignesti INAF-Padova Astronomical
Observatory, Vicolo dell’Osservatorio 5, I-35122 Padova, Italy

###### Abstract

Ram-pressure stripping by the intracluster medium (ICM) is one of the most
advocated mechanisms that affect the properties of cluster galaxies. A recent
study based on a small sample has found that many galaxies showing strong
signatures of ram-pressure stripping also possess an active galactic nucleus
(AGN), suggesting a possible correlation between the two phenomena. This
result has not been confirmed by a subsequent study. Building upon previous
findings, here we combine MUSE observations conducted within the GASP program
and a general survey of the literature to robustly measure the AGN fraction in
cluster’s ram pressure stripped galaxies using BPT emission line diagrams.
Considering a sample of 115 ram pressure stripped galaxies with stellar masses
$\geq 10^{9}\,M_{\odot}$, we find an AGN fraction of $\sim 27\%$. This
fraction strongly depends on stellar mass: it raises to 51% when only ram-
pressure stripped galaxies of masses $M_{*}\geq 10^{10}\,M_{\odot}$ are
considered. We then investigate whether the AGN incidence is in excess in ram
pressure stripped galaxies compared to non-stripped galaxies, using as
comparison a sample of non-cluster galaxies observed by the survey MaNGA.
Considering mass-matched samples, we find that the incidence of AGN activity
is significantly higher (at a confidence level $>99.95\%$) when ram-pressure
stripping is on act, supporting the hypothesis of an AGN-ram pressure
connection.

Galaxy environments — Galaxy cluster — Active Galactive Nuclei — Galaxy
properties

##  1 Introduction

Both theoretical and observational studies concur that there is a strong
connection between the presence of an Active Galactic Nucleus (AGN) and the
host galaxy properties (see Kormendy et al., 2013, and references therein),
suggesting that internal processes might regulate the AGN activity and,
conversely, the AGN activity might be relevant for shaping galaxy properties.
AGN are preferentially found in more massive galaxies
($M_{*}>10^{9}M_{\odot}$, see e.g. Juneau et al., 2011; Sabater et al., 2013;
Lopes et al., 2017; Pimbblet et al., 2013; Kauffmann et al., 2003; Sánchez et
al., 2018; Decarli et al., 2007; Rodríguez del Pino et al., 2017) and the mass
of the host galaxy is the main parameter driving the level of AGN activity
(Magliocchetti et al., 2020). However, it is still debated if other factors,
such as dense galaxy environment or galaxy clusters (e.g. Pimbblet et al.,
2013), have an impact on the presence of AGN in galaxies.

Despite the vast literature on this topic (e.g. Kauffmann et al., 2004; Best
et al., 2007; Silverman et al., 2009; von der Linden et al., 2010; Hwang et
al., 2012; Sabater et al., 2013; Martini et al., 2013; Ehlert et al., 2014;
Silverman & David, 2015; Coldwell et al., 2017; Lopes et al., 2017; Marziani
et al., 2017; Gordon et al., 2018; Magliocchetti et al., 2018; Koulouridis et
al., 2018; Argudo-Fernández et al., 2018), different studies have reached
quite opposite results, most likely due to the different techniques adopted to
identify AGNs, select the samples and characterise the environment. Using a
spectroscopic sample, Dressler et al. (1985) first suggested that the fraction
of AGN in clusters ($\sim$ 1%) is significantly lower than in the field
($\sim$5%). Similarly, Lopes et al. (2017), identifying AGN in the SDSS using
optical emission lines and Baldwin et al. (1981) (BPT) diagrams, found that
AGN favor environments typical of the field, low mass groups or cluster
outskirts. Using the same dataset but a different cluster sample, von der
Linden et al. (2010) showed instead that the AGN fraction does not change as a
function of environment, nor of clustercentric distance (see also Miller et
al., 2003). Similar conclusions were obtained by Martini et al. (2007), Lehmer
et al. (2007), Sivakoff et al. (2008), Arnold et al. (2009), exploiting X-ray
data. Yet, the radio AGN fraction seems to be much higher in clusters than in
the field (Sabater et al., 2013; Best et al., 2007).

Considering local density as a proxy for environment, Kauffmann et al. (2004)
found that AGN host galaxies with strong [O III] emission are twice as
frequent in low density regions than in high density regions (see also Miller
et al., 2003; Montero-Dorta et al., 2009). In contrast, Amiri et al. (2019)
did not find any effect of the galaxy density on nuclear activity. Sabater et
al. (2013) found that (at fixed mass) the prevalence of optical AGN is a
factor of 2-3 lower in the densest environments (see also Man et al., 2019),
but increases by a factor of $\sim$2 in the presence of strong one-on-one
interactions. Gilmour et al. (2007) showed that X-ray selected AGN lie
predominantly in moderate dense regions.

The expected connection between AGN incidence and properties with the
environment has roots in the fact that the characteristics of AGN are strongly
linked to the conditions of the available gas, which in turn can be affected
by the galaxy environment. So any environmental specific physical mechanism
that has the potential to affect the galaxy gas can impact the AGN activity.
For example, mergers – which most frequently happen in the field – have
frequently been cited as a method to fuel AGN (e.g. Sanders et al., 1988) and
a number of morphological studies claim an excess of post-merger systems in
their AGN samples (Bahcall et al., 1997; Canalizo & Stockton, 2001; Urrutia et
al., 2008; Letawe et al., 2010; Smirnova et al., 2010).

Another process able to affect the gas supply in galaxies is ram pressure
stripping (RPS, Gunn & Gott 1972). This is a mechanisms happening most
efficiently in clusters and massive groups (Hester, 2006) and it is due to the
pressure exerted by the intracluster medium (ICM) on the galaxy interstellar
medium (ISM). This interaction can produce many visible effects on the galaxy,
such as alter its less bound gas, giving rise to wakes of stripped material
departing from the main galaxy body (van Gorkom, 2004; Kenney et al., 2004;
Poggianti et al., 2017a; Fumagalli et al., 2014) and inducing a quenching of
star formation (Vollmer et al., 2001; Tonnesen et al., 2007; Vulcani et al.,
2020). Prior to complete gas removal, it has been observed that ram pressure
can also increase the star formation rate in galaxies (Crowl & Kenney, 2006;
Merluzzi et al., 2013; Vulcani et al., 2018) and simulations support this
finding (Kronberger et al., 2008; Kapferer et al., 2009; Tonnesen et al.,
2009; Bekki, 2013), suggesting that the increased pressure initially helps
compress the gas and triggers increased star formation. The same mechanism
that initially promotes star formation can also fuel the AGN during the RPS
process: gas can be funnelled towards the galaxy centres, due to gravitational
instabilities and the spiraling towards the center of clumps that lose angular
momentum (Schulz & Struck, 2001; Tonnesen et al., 2009; Ramos-Martínez et al.,
2018). The funneling of gas towards the galaxy center can also ignite the
central super-massive black hole (SMBH). Theoretical models (Tonnesen et al.,
2009) have indeed demonstrated that gas inflows can fuel the central AGN in
ram-pressure stripped galaxies, possibly due to the presence of magnetic
fields (Ramos-Martínez et al., 2018). The enhanced accretion onto the black
hole can then produce heating and outflows due to AGN feedback (Ricarte et
al., 2020).

Therefore RPS might be simultaneously responsible for an enhanced AGN activity
and the appearance of tails of stripped material. This scenario has been first
proposed by Poggianti et al. (2017b, hereafter P17b) to explain the very high
incidence (6/7) of AGN detected in a sample of galaxies strongly affected by
RPS, also called jellyfish galaxies (see also Maier et al., 2021). That
analysis is based on Integral Field spectroscopic data coming from the GAs
stripping Phenomena in galaxies (GASP, Poggianti et al., 2017a). Subsequent
GASP studies have led to the identification of AGN-driven outflows (Radovich
et al., 2019) and a compelling case for AGN feedback in action (George et al.,
2019).

The P17b analysis is based on a small sample and importantly is only composed
of jellyfish galaxies with very striking tails, and all massive galaxies.
Thus, it leaves open the possibility that the AGN activity could be only
related to the (rather short) peak phase of stripping and/or only to the
galaxy mass regardless of RPS.

A subsequent study did not find a high incidence of AGN: Roman-Oliveira et al.
(2018) 111Note added at the proofs stage: see also Boselli et al. arxiv
2109.13614 which appeared unrefereed on astroph on September 28 2021. analyzed
a sample of ram pressure stripped galaxies in a supercluster at $z\sim 0.2$
and found only 5/70 AGN, according to optical line diagnostics. Their sample
span a wide galaxy stellar mass range (from 109 to 10${}^{11.5}M_{\odot}$) and
is based on visual identification of the candidates. At odds with GASP, none
of these candidates have IFS data to confirm they are indeed affected by RPS.
It also includes ram pressure stripped candidates with different degrees of
stripping, while as said above the P17b study includes only very dramatic
cases.

In this paper, we build on previous results and aim at estimating the
incidence of AGN on the largest possible sample of ram pressure stripped
galaxies up to date.

Throughout the paper, we adopt a Chabrier (2003) initial mass function (IMF)
in the mass range 0.1-100 M⊙. The cosmological constants assumed are
$\Omega_{m}=0.3$, $\Omega_{\Lambda}=0.7$ and H${}_{0}=70$ km s-1 Mpc-1.

##  2 DATASETS and galaxy samples

In this paper we aim at characterizing the incidence of AGN activity among ram
pressure stripped cluster galaxies. We start our analysis by considering a
sample of galaxies drawn from the GAs Stripping Phenomena in galaxies (GASP,
Poggianti et al. 2017a) survey, then, to increase the statistics and support
more robustly the results, we also gather a literature sample of all ram
pressure stripped galaxies identified by different authors in the last four
decades. Finally, we exploit the fifteenth data release of the MaNGA survey
(DR15; Aguado et al. 2019) to build a control sample of galaxies with
characteristics similar to the ram pressure stripped galaxies, but considering
only those not in clusters and therefore presumably not affected by strong
RPS.

###  2.1 The GASP sample and data

GASP is a project aimed at studying gas removal processes, mainly due to ICM-
ISM interaction, using a sample of 114 galaxies. More specifically, it
comprises both RPS candidates and undisturbed galaxies located in clusters,
groups and field, spanning a range in stellar masses from $10^{9}$ to
$3.2\times 10^{11}$ M⊙ and a redshift range of 0.04 $<$ z $<$ 0.07. All
galaxies were selected on the basis of B-band imaging coming from three
different surveys: WINGS (Fasano et al., 2006), OMEGAWINGS (Gullieuszik et
al., 2015), and PM2GC (Calvi et al., 2011).

GASP is based on an ESO Large Program carried out with the integral-field
spectrograph MUSE, mounted at the VLT, whose large field of view
($1^{\prime}\times 1^{\prime}$) and high, but seeing limited, spatial
resolution ($0.2^{\prime\prime}$/pixel, seeing of $1^{\prime\prime}$), allow
us to cover the galaxy outskirts and possible tails of gas departing from the
main body of the galaxies up to ten times the galaxy effective radius (i.e.
$\sim\ 10\ R_{e}$) with a resolution of $\sim$ 1 kpc at the galaxy redshifts.

For our analysis, we select only cluster members that have been confirmed to
be ram pressure stripped based on the MUSE data: in fact, they all have
extraplanar $\rm H\alpha$ emission in various stages of stripping (B.
Poggianti et al. in prep.), from weak/initial stripping (JStage=0.5) to
significant tails (JStage=1) to extreme tails longer than the stellar disk
diameter (JStage=2, so-called “jellyfish galaxies”) to truncated disks
corresponding to a late-stage of RPS (Jstage=3), for a total of 51 galaxies.
From now on we will call this sample GASP-RPS. All of these are
morphologically late-type and star-forming galaxies.

We make use of the fluxes of the emission-only component of the lines
H$\alpha$, H$\beta$, $\rm[OIII]\lambda$5007Å and $\rm[NII]\lambda$6583Å
measured with the KUBEVIZ code (Fossati et al., 2016) from the continuum-
subtracted MUSE cubes corrected for both Galactic and intrinsic extinction, as
described in detail in Poggianti et al. (2017a). Stellar masses are taken from
Vulcani et al. (2018) and are computed using the SINOPSIS spectrophotometric
code (Fritz et al., 2017) by summing up the masses of all the spaxels within
the galaxy disk (Gullieuszik et al., 2020).

To characterize the ionization mechanism acting on the gas and therefore
identify galaxies with AGN, we inspect the BPT diagnostic diagram
[NII]/H$\alpha$ vs. [OIII]/H$\beta$ ratios (BPT-NII, Baldwin et al. 1981). We
consider only spaxels with a signal-to-noise (S/N) ratio greater than $3$ for
all the lines used. We use the relation from Kauffmann et al. (2003) (K03) to
separate Star-forming from Composite regions, the Kewley et al. (2001) (K01)
line to identify AGN222The choice of the K01 demarcation line to identify AGN
is a conservative choice that minimizes the AGN spaxels. Note that recent
works (see Law et al. 2021 and references therein) find a demarcation line in
the NII-BPT diagram closer to the K03 separation, but we choose the most
conservative one to minimize the contamination from star-forming regions. and
the Sharp et al. (2010) (SB10) relation to discriminate between Seyferts and
LINERs. We classify a galaxy as AGN host if in its central (3′′) region there
are at least 20 spaxels333We adopted this number upon visual inspection of the
maps. This choice allows us to have enough spaxels to identify possible AGN
with high confidence, still focusing on the central part of the galaxy. that
have a Seyfert or LINER classification, otherwise we flag it as star forming.
In the galaxies with high values of extinction ($A_{V}$, as measured by the
Balmer decrement) in the central cores, that might prevent us from identifying
an AGN, we further inspect the LINER classified spaxels: a bi-conical shape of
their distribution suggests extended ionized regions and therefore indicates
the presence of the AGN.

For those galaxies with a central LINER/AGN classification we have carefully
checked the emission line fits, in particular the $\rm H\beta$ line given that
an underestimate of H$\beta$ flux would lead to an overestimate of the
$\rm[OIII]/H\beta$ ratio, mimicking line ratios typical of AGN.

###  2.2 Ram-pressure stripping candidates from the literature

We have performed a systematic literature search of all the ram pressure
stripped galaxies identified by December 2020. These galaxies were studied
exploiting a wide variety of observational techniques, including radio (e.g.,
Gavazzi et al., 1995); sub-mm (e.g. Scott et al., 2013; Jáchym et al., 2014,
2019); infrared (e.g., Sivanandam et al., 2010, 2014); optical (e.g., Gavazzi
et al., 1995, 2001; Sun et al., 2007, 2010; Yagi et al., 2010; Sivanandam et
al., 2010; Fumagalli et al., 2014; Gavazzi et al., 2017; Fossati et al., 2016;
Roberts et al., 2020); UV (e.g., Smith et al. 2010) and X-ray (e.g., Sun et
al., 2006, 2010). The assembled sample is therefore greatly heterogeneous and
while for some galaxies it has been confirmed that RPS is the only acting
mechanism, in some other cases galaxies are most likely undergoing both RPS
and tidal interactions. As our aim is to include all RPS galaxies and collect
a sample as large as possible, we consider also the latter cases. However, we
remove cases where a merger or tidal interaction is the main cause of the
galaxy transformation. 444 The candidate merging systems removed from the
sample are: F0237 (Owers et al., 2012), NGC4294, NGC4299 and NGC4302 (Vollmer
et al., 2013; Pappalardo et al., 2012).

We narrow down our search to galaxies for which we retrieve information at any
wavelength on the ionization mechanism of the central emission,555A list of
all RPS galaxies known in the literature, regardless of AGN information, will
be published in J. Crossett et al. (in prep.). obtaining a total sample of 80
galaxies (from now on LIT-RPS sample). All these turn out to have some active
star formation (in addition to the eventual AGN activity) in the available
literature.

The LIT-RPS sample is located in the redshift range $0.001\leq z\leq 0.34$,
plus a galaxy at $z=0.73$, and covers a stellar mass range of $1.3\times
10^{8}<M_{*}/M_{\odot}<2.0\times 10^{11}$. Stellar masses have been collected
from the literature and homogenized to the same Chabrier (2003) IMF (as in
GASP). When a stellar mass estimate was not available (4/80), we computed it
using the available photometric data following the Bell et al. (2001)
approach, as described in Appendix A.

To assess the strength of RPS signatures and compare with the GASP JStage
classification, four of us (GP, BMP, BV, AM) visually inspected the available
images in the literature for all the galaxies. Following the scheme described
in §2.1, we assign a flag indicating the extent of the tail (JStage) based on
the H$\alpha$ emission (if available) and also a general JStage based on any
wavelength observed (JStagegen). In the case of multiple images with different
resolutions or at different wavelengths showing a different extent of the
tail, we always consider the wavelength with the longest visible tail to
assign the JStagegen. The classifiers agreed in most of the cases. In the
discrepant cases, each galaxy was inspected together by the classifiers to
ensure homogeneity and to find a consensus. This visual inspection also
confirmed that the LIT-RPS sample is composed of morphologically late-type
galaxies (spirals or irregulars).

###  2.3 The MaNGA sample

MaNGA (Mapping Nearby Galaxies at Apache Point Observatory, Bundy et al. 2015)
is an integral-field spectroscopic survey observing galaxies at 0.01 $\leq$ z
$\leq$ 0.15 using the BOSS Spectrograph (Smee et al., 2013) mounted at the 2.5
m SDSS telescope (Gunn et al., 2006), which covers a spectral range from 3600
Å to 10300 Å, with a resolution of R$\sim$ 1400 at 4000 Å and R$\sim$ 2600 at
9000 Å.

We exploit the MaNGA DR15 release and use the outputs of the Pipe3D pipeline
(Sánchez et al., 2016a, b, 2018). More specifically, we use the
Pipe3D-v2_4_3666https://www.sdss.org/dr16/data_access/value-added-
catalogs/?vac_id=manga-pipe3d-value-added-catalog:-spatially-resolved-and-
integrated-properties-of-galaxies-for-dr15 catalog, which contains integrated
properties, characteristic and gradients of different quantities for 4656
galaxies. Of interest for our work are integrated stellar masses and star
formation rates obtained from the H$\alpha$ emission line, that we convert to
our adopted Chabrier (2003) IMF.

We first exclude from the sample 75 duplicate galaxies and then select only
galaxies with a specific Star Formation Rate (sSFR) $>10^{-11}\rm yr^{-1}$,
for a total of 2509 galaxies. The latter selection allows us to consider only
star forming galaxies, as are galaxies in both the GASP-RPS and LIT-RPS
samples.

As we aim at assembling a sample not affected by RPS, we crossmatch our sample
with the environmental catalog by Tempel et al. (2014), who provide halo mass
estimates based on Navarro et al. (1997) profiles. Using a searching radius of
5′′, we obtain a match for 2061 galaxies, 861 of which are located in
structures with halo masses $\log(\rm M_{h}/M_{\odot})<13.0$,777We verified
that results are insensitive to the exact choice of this threshold, exploring
log halo masses up to 13.6. We decided to use a conservative cut (13.0) to
avoid the possibility that ram-pressure stripped galaxies in groups
contaminate the sample. therefore are most likely isolated (Yang et al.,
2007).

Finally, to reduce the effect of a different morphological mix among the
different samples, we use the visual morphological classification from the
MaNGA Value Added Catalogs888https://www.sdss.org/dr16/data_access/value-
added-catalogs/?vac_id=manga-visual-morphologies-from-sdss-and-desi-images
that is based on inspection of image mosaics using a new re-processing of SDSS
and Dark Energy Legacy Survey (DESI) images, following the methods from
Hernández-Toledo et al. (2010) and exclude 70 early-type (Ellipticals, S0s and
S0as) and 2 unclassified galaxies.999For consistency with the other samples,
we have applied the morphological cut, but all the results remain unchanged if
no morphological criterion is applied.

To assemble the final MaNGA sample, we consider only galaxies that in a
circular aperture of 3′′ diameter (i.e. the SDSS fibre size) centered on the
galaxy have at least 20 spaxels with $\rm S/N>3$ for all lines that will be
used to detect the presence of an AGN. 782 galaxies pass this selection and
constitute our reference sample, called MaNGA-Ref. This sample covers a
redshift range 0.0024$<z<$0.1439. We note that the MaNGA fibre core diameter
(2′′) is similar to the typical seeing value (2′′.5). At the median redshift
of our MaNGA-Ref sample (z=0.0317), the MaNGA spatial resolution of 2′′
corresponds to 1.27 kpc and at the 75% redshift percentile (z=0.043) to 1.7
kpc. This is only slightly worse than the GASP spatial resolution of $\sim$1
kpc (median $z=0.05$), which is dominated by the seeing ($\sim$1”).101010 This
is why in GASP the datacubes have been filtered with a 5x5 kernel (Poggianti
et al., 2017a).

To identify AGN, we inspect the BPT-NII maps provided by the online tool
MARVIN111111https://www.sdss.org/dr15/manga/marvin/ and use the same
classification criteria as for the RPS samples. We count the number of spaxels
classified as AGN (i.e. Seyfert + LINER), Star-Forming or Composite. If the
number of spaxels classified as Seyfert or LINER is larger than 20 in a
circular aperture of 3′′ diameter, we classify the galaxy as AGN, otherwise as
star forming.

##  3 Results I: The incidence of AGN among ram pressure stripped galaxies

In this section we present the sample of AGN hosts in the GASP-RPS and in the
LIT-RPS samples separately and quantify the incidence of AGN among ram
pressure stripped galaxies. We also investigate if these fractions depend on
the properties of the ram pressure stripped galaxies, such as stellar mass and
Jstage. In the following section we will quantitatively compare these
fractions controlling for the different mass distribution and comparing them
to those of the MaNGA-Ref sample.

###  3.1 GASP-RPS

Figure 1: Left. BPT-NII diagnostic diagram for all spaxels with $S/N>3$. The
red dotted and continuous lines are defined as in Kewley et al. (2001) and
Kauffmann et al. (2003), respectively. The green lines are taken from Sharp et
al. (2010). Right. Galaxy map color-coded according to the BPT-NII
classification; red lines are the stellar emission isocontours corresponding
to the galactic disk edges.  Figure 1: (continued)

In the GASP-RPS sample, seven galaxies are already known to host an AGN; six
of them were presented in P17b and one (JO36), in Fritz et al. (2017). The
latter is an edge-on disk hosting an obscured AGN which is not directly
identified using BPT diagrams due to strong dust absorption. However, evidence
for the AGN presence comes from extra-nuclear LINER-like emission with a cone
morphology and the AGN is detected by _Chandra_ as a point-like X-ray source
(Fritz et al., 2017).

Among the P17b candidates, JO194 was classified as a LINER and its combined
line ratios are better reproduced by an AGN model (Radovich et al., 2019),
while JO201, JO206, JO204, JW100 and JO135 are classified as Seyfert galaxies
according to BPT diagrams and are all Seyfert2. JO204 and JO135 also have
extended emission line regions ionized by the AGN. Four of these galaxies
display AGN outflows (Radovich et al., 2019).

Having inspected all other GASP cluster-members ram-pressure stripped
galaxies, we find other 5 AGN candidates that are presented in Figure 1. The
stripping characteristics of these galaxies were discussed in previous works
and only summarized here, but the analysis of their central ionization
mechanism is shown here for the first time. JO49 has unwinding tails due to
RPS (Bellhouse et al., 2021). It presents a central LINER-like region
surrounded by a thin Composite-like ring, which in the BPT diagram correspond
to a long finger of points encompassing the Composite region extending well
beyond the K01’s line. We note that JO49 hosts also an X-ray source of
luminosity $1.2\times 10^{41}\rm\,erg\,s^{-1}$, detected by XMM-Newton (Webb
et al. 2020121212The 4XMM-DR10 catalog contains source detections covering an
energy interval from 0.2 keV to 12 keV. On the other hand, the _Chandra_
energy range goes from 0.5 to 7 keV.).

JO85, another unwinding ram pressure stripped galaxy (Bellhouse et al., 2021),
has fewer LINER-like points than JO49 embedded in a Composite-like region, but
it is highly obscured by dust ($A_{V}\sim 2.7$ in the central region as
measured by the Balmer decrement) and has a central _Chandra_ point source
with a luminosity of $5.0\times 10^{40}\,\rm erg\,s^{-1}$ (Evans et al.,
2020).

JO147 (first described by Merluzzi et al. 2013, see also Poggianti et al.
2019) is an inclined highly-extincted disk, and is stripped in the north-west
direction. We find that it has LINER-like opposite cones embedded in wider
Composite cones. Its luminosity in the X-ray band observed by XMM-Newton is
$2.4\times 10^{41}\rm\,erg\,s^{-1}$ (Webb et al., 2020).

JO171 is an Hoag-like ring galaxy with long tails stripped in the north
direction (Moretti et al., 2018). It has central AGN-powered spaxels
(Seyfert2) in the inner kpc.

Finally, JW39 has long tails originating from unwinding spiral arms (Bellhouse
et al., 2021). It has a LINER-like circular central region surrounded by a
larger circular area with Composite emission.

The latter two galaxies have no available central X-ray counterparts, from
neither XMM-Newton nor _Chandra_.

To summarize, with respect to the sample of AGN described in P17b and Radovich
et al. (2019), we find an additional Seyfert2 and 4 LINER-like galaxies,
yielding a total sample of 12 AGN hosts in the GASP-RPS sample. Their main
properties are summarized in Tab. 1.

The left panel of Figure 3 shows the mass distribution of galaxies hosting an
AGN compared to the entire GASP-RPS sample. While RPS galaxies cover a mass
range of 8.7 $\leq\log(M_{\ast}/M_{\odot})\leq$ 11.5, AGN hosts are among the
most massive galaxies in the sample, having all $\log(M_{\ast}/M_{\odot})\geq$
10.5.

We are now in the position of computing the fraction of AGN ($f_{AGN}$) over
the total (AGN+SF) number of galaxies, considering different subsamples, as
summarised in Table 2. The AGN fraction in the total GASP-RPS sample is
0.24${}^{+0.06}_{-0.05}$, with uncertainties computed as binonomial errors.
Restricting the sample to $\log(M_{\ast}/M_{\odot})\geq$ 10.5, this fraction
becomes 0.71${}^{+0.10}_{-0.12}$.

Considering the various stages of stripping (Fig. 4), the frequency of AGN
increases with the strength of RPS signatures: no galaxies with AGN activity
have Jstage $=0.5$, while the AGN incidence increases among moderate-stripping
galaxies (Jstage=1, 8%) and is particularly high among Jstage=2 galaxies,
where it reaches 56%. Still one out of 4 galaxies in a late stage of RPS
(truncated disks, Jstage=3) has an AGN. Interestingly, using the same methods
of the current analysis, only two AGNs are found in the GASP non-ram pressure
stripped sample of star-forming galaxies, which consists of 49 galaxies
(Vulcani et al., 2021, B. Poggianti et al. in prep.).

In the GASP-RPS sample, out of the 17 galaxies more massive than
$\log(M_{*}/M_{\odot})\geq 10.5$, 10 ($\sim 58\%$) have Jstage=2 and,
viceversa, $\sim 55\%$ (10/18) of the Jstage=2 galaxies are more massive than
$\log(M_{*}/M_{\odot})\geq 10.5$. All of them host an AGN. It is significant
that none of the massive galaxies have a Jstage=0.5. This result suggests a
tight correlation between stellar mass and Jstage. The correlation is probably
linked with the higher capability of massive galaxies to retain gas. While
low-mass galaxies are already completely stripped when they approach the
densest regions in clusters, high-mass galaxies more easily hold onto their
gas (Jaffé et al., 2018, Luber et al., submitted to ApJ) and experience RPS in
these dense regions, where the gas removal is the most intense. Since AGN are
preferentially located in the most stripped and massive galaxies, we cannot
state which of these two parameters is more connected to the presence of an
AGN.

Table 1: AGN candidates in the GASP sample. Columns are: 1) GASP ID; 2-3)
coordinates of the optical center; 4) galaxy redshift; 5) host cluster; 6)
galaxy stellar masses (Vulcani et al., 2018); 7) Jstage (Poggianti et al. in
prep.; 8) AGN classification; 9) work in which the source is presented.

ID | RA | DEC | z | cluster | $\log M_{\ast}/M_{\odot}$ | Jstage | AGN flag | refs
---|---|---|---|---|---|---|---|---
JO85 | 351.13068 | 16.86815 | 0.0355 | A2589 | 10.7 | 1 | 3 | this paper
JO36 | 18.247583 | 15.591488 | 0.0407 | A160 | 10.8 | 3 | 4 | Fritz et al. (2017)
JO194 | 359.25284 | -34.680588 | 0.042 | A4059 | 11.2 | 2 | 3 | P17b
JO204 | 153.44513 | -0.914182 | 0.0424 | A957 | 10.6 | 2 | 1 | P17b
JO201 | 10.376208 | -9.26275 | 0.0446 | A85 | 10.8 | 2 | 1 | P17b
JO49 | 18.682709 | 0.286136 | 0.0451 | A168 | 10.7 | 2 | 3 | this paper
JO147 | 201.70721 | -31.395975 | 0.0506 | A3558 | 11.0 | 2 | 3 | this paper
JO206 | 318.44754 | 2.476218 | 0.0511 | IIZW108 | 11.0 | 2 | 1 | P17b
JO171 | 302.56125 | -56.641823 | 0.0521 | A3667 | 10.6 | 2 | 1 | this paper
JO135 | 194.26791 | -30.375088 | 0.0544 | A3532 | 11.0 | 2 | 3 | P17b
JW100 | 354.10443 | 21.150702 | 0.0619 | A2626 | 11.4 | 2 | 3 | P17b
JW39 | 196.03212 | 19.210691 | 0.0663 | A1668 | 11.2 | 2 | 3 | this paper

Note. — The adopted AGN flag for both GASP-RPS and LIT-RPS galaxies ranges
from 0 to 6: 0 means that star formation is the dominant ionization process at
the galaxy center according to BPT-NII classification; 1, 2, 3 if the galaxy
hosts a Seyfert 1, Seyfert 2 or LINER-like nucleus, respectively, again
according to the BPT diagram; 4 if the AGN has been detected through the X-ray
signal, but not in the optical; 5 when the galaxy is classified as a radio
galaxy; 6 when the source is classified as AGN, without any specification on
the type.

Table 2: AGN fractions in the GASP-RPS sample, considering galaxies of different mass and characterized by different Jstages. Errors are binomial.  $N_{AGN}/N_{TOT}$ | $f_{AGN}$ | Jstage | $\log(M_{\ast}/M_{\odot})$
---|---|---|---
12/51 | $0.24_{-0.05}^{+0.06}$ |  $\geq$ 0.5 | all
12/17 | $0.71_{-0.12}^{+0.10}$ |  $\geq$ 0.5 |  $\geq$ 10.5
0/16 | $0.0_{-0.0}^{+0.06}$ | $=0.5$ | all
1/13 | $0.08_{-0.05}^{+0.11}$ | $=1$ | all
10/18 | $0.56_{-0.12}^{+0.11}$ | $=2$ | all
1/4 | $0.25_{-0.15}^{+0.25}$ |  =$3$ | all

Figure 3: Stellar mass distributions for all galaxies (black histogram) and
for galaxies hosting an AGN (red histogram). From left to right: the GASP-RPS,
LIT-RPS and MaNGA-Ref samples.

Figure 4: Left. Stacked Histograms for galaxies of different Jstages and
divided among centrally star-forming galaxies (light blue histogram) and AGN
(dark blue histogram) according to the BPT-NII classification for GASP-RPS
(left) and LIT-RPS (right). Percentages are AGN fractions in the corresponding
bin of Jstage and Jstagegen.

###  3.2 LIT-RPS

The catalog of the 82 literature ram-pressure stripped galaxies with SF/AGN
information is presented in Table 3, which gives galaxy name, coordinates,
redshift, host cluster name, Jstage, Jstagegen and alternative names. Stellar
masses and AGN classification, along with the source for those values are
given in Table 4.

One of these galaxies has broad optical lines typical of Seyfert1. For all the
other galaxies the AGN classification is based on the BPT-NII diagnostic. For
$\sim 68\%$ (55/81) of them the classification in published results is based
on spectroscopic observations published in dedicated papers, either from
integral-field unit (Merluzzi et al., 2013, 2016; Fossati et al., 2016;
Boselli et al., 2019; Stroe et al., 2020; Consolandi et al., 2017), long-slit
or fibre spectra (Ebeling et al. 2019; Cortese et al. 2007; Owers et al. 2012;
Owen et al. 2006; Mahajan et al. 2010131313We note that for 2 galaxies,
GMP3618 and D100, Mahajan et al. (2010) give different results with respect to
the classification reported in the DR7 (Abazajian et al., 2009) and DR8
(Aihara et al., 2011) analysis even though they used DR7 data to build up BPT-
NII., Veron-Cetty M.P. 2003). For the other 32$\%$ (26/81) of the galaxies we
instead use the online AGN classification based on the analysis of emission
line ratios extracted from an integrated spectra of the central circular
aperture ($r\sim 3^{\prime\prime}$) observed with the SDSS fibre (Data Release
8, from now on DR8, Aihara et al. 2011) as analyzed by Brinchmann et al.
(2004), Kauffmann et al. (2003), and Tremonti et al. (2004) in the Value Added
Catalog MPA/JHU. For 24 galaxies, we had both the DR8 automatic classification
and information about the central source from individual publications in the
literature. In these cases we favoured the latter.

We note that 4 of the 81 galaxies also have information coming from either
X-ray or radio data (Ebeling et al., 2019; Winkler et al., 1992; Owers et al.,
2012; Best et al., 2012; Kalita et al., 2019; Caglar et al., 2020). While
their position on a BPT-NII diagram suggests they are star forming, the
additional data instead classify them as AGN. In what follows we will
therefore discuss how results change if we include or exclude these 4 objects.

Overall, 24/82 galaxies host an AGN ($\sim$30%). If we disregard the AGN
classification based on X-ray or radio data and consistently consider only the
BPT-NII classification, the fraction above becomes 20/82 ($\sim 24\%$).

The central panel in Fig.3 shows the mass distribution of the galaxies with
and without AGN. The entire sample spans a mass range 8.1
$<\log(M_{\ast}/M_{\odot})<$11.4. Similarly to what found for GASP, most of
the AGN are massive galaxies, even though in this sample there are also a few
less massive AGN hosts. Above the GASP AGN mass limit
($\log(M_{\ast}/M_{\odot})>10.5$) the AGN fraction becomes
0.80${}^{+0.08}_{-0.12}$.

Table 5 reports the AGN fraction for the different subsamples considered,
including that for galaxies of different Jstagegen. The trend of the AGN
fraction with Jstagegen is weaker than in GASP-RPS (see also Fig. 4), with the
percentages ranging between 17% and 27% but being consistent within the large
errors in all Jstagesgen.141414The subsample of LIT-RPS galaxies with Jstage
is too small to study trends with the length of the H$\alpha$ tails.

We remind the reader that while the AGN classification and mass estimates
among GASP galaxies are homogeneous, for the LIT-RPS sample we based the
former on a number of different data and indicators. In addition, stellar
masses have been computed following many different approaches and so, even
though homogenized to the same IMF, there could be some systematics among the
different galaxies. Finally, we recall that the Jstagegen flag is based on a
very heterogeneous set of images in terms of wavelengths, depth, quality and
therefore results must be taken with caution.

Table 3: Colums are: 1) galaxy most common name 2) and 3) equatorial coordinates of the galaxy center from SIMBAD 4) galaxy redshift 5) host cluster 6) and 7) Jstage and general Jstage, defined in section §3.2 8) alternative names. Stellar masses flagged with the asterisk (*) are computed by means of photometric data as described in Sec.A.  name | RA | DEC | z | cluster | Jstage |  Jstagegen | alternative names
---|---|---|---|---|---|---|---
MIP001417-302303 | 3.5693 | -30.3843 | 0.2955 | A2744 | – | 2.0 | F1228
HLS001427-302344 | 3.61065 | -30.39581 | 0.3033 | A2744 | – | 2.0 | F0083
NGC1566 | 65.00175 | -54.93781 | 0.005 | Dorado | 0.0 | 0.0 | –
ID345 | 149.9191 | 2.5281 | 0.727 | CGr32 | 2.0 | 2.0 | –
LEDA36382 | 175.73523 | 19.96621 | 0.02427 | A1367 | 2.0 | 2.0 | CGCG97073
Table 4: Columns are 1) galaxy name 2) logarithm of the stellar masses, adopting Chabrier 2003 IMF (in parenthesis the reference from which the value has been taken is reported) 3) AGN classification (see Table 1) and relative reference 4) references which present a characterization of the galaxy as a RPS candidate. The four galaxies with classification equals to 4 and 5 (e.g. where the AGN is spotted observing them in X and radio) resulted to be Star-Forming in the optics. In the text we analyze the consequence to change their AGN flag to 0.  name | $\log(M_{\ast}/M_{\odot})$ | AGN | Refs
---|---|---|---
MIP001417-302303 |  9.6 [Rawle et al. (2014)] |  0 [Owers et al. (2012)] |  Owers et al. (2012), Rawle et al. (2014)
HLS001427-302344 |  10.9 [Rawle et al. (2014))] |  1 [Owers et al. (2012))] |  Owers et al. (2012), Rawle et al. (2014)
NGC1566 |  10.8 [Elagali et al. (2019)] |  1 [Veron-Cetty M.P. (2006)] | Elagali et al. (2019)
ID345 |  10.3 [Boselli et al. (2019)] |  1 [Boselli et al. (2019)] | Boselli et al. (2019)
LEDA36382 | 9.5 [Mendel et al. (2014)] | 0 [SDSS (2004)] |  Gavazzi et al. (1995), Gavazzi et al. (2001),
Sivanandam et al. (2014), Boselli et al. (2018), Yagi et al. (2017)
Table 5: AGN fractions for the LIT-RPS sample, considering galaxies of different mass ranges and characterized by different Jstagegen. Errors on fractions are binomial. Values outside/in brackets are the fractions computed ignoring/considering the 4 galaxies classified as AGN based on radio and X data.  $N_{AGN}/N_{TOT}$ | $f_{AGN}$ |  Jstagegen | $\log(M_{\ast}/M_{\odot})$
---|---|---|---
20/82 (24/82) |  $0.24_{-0.04}^{+0.05}$ ($0.29_{-0.05}^{+0.05}$) | all | all
12/15 (12/15) |  $0.80_{-0.12}^{+0.08}$ ($0.80_{-0.12}^{+0.08}$) | all |  $\geq$ 10.5
4/15 (5/15) |  $0.27_{-0.10}^{+0.13}$ ($0.33_{-0.11}^{+0.13}$) | =0 | all
2/10 (2/10) |  $0.20_{-0.10}^{+0.15}$ ($0.20_{-0.10}^{+0.15}$) | =0.5 | all
3/18 (4/18) |  $0.17_{-0.07}^{+0.11}$ ($0.22_{-0.08}^{+0.11}$) | =1 | all
8/30 (10/30) |  $0.27_{-0.07}^{+0.09}$ ($0.33_{-0.08}^{+0.09}$) | =2 | all

##  4 RESULTS II: Is the AGN fraction among ram-pressure stripped galaxies
higher than in non-ram-pressure stripped galaxies?

Figure 5: Normalized stellar mass distributions of the GASP (black histogram), MaNGA (purple-dotted histogram) and literature sample (light red histogram). For the Montecarlo, we have selected galaxies above the vertical-dotted line, i.e. with masses $\log(M_{\ast}/M_{\odot})>9$.  Table 6: AGN fractions and binomial errorbars for the GASP-RPS, LIT-RPS and ALL-RPS samples in two different mass bins. For the LIT-RPS and ALL-RPS sample, values in parenthesis are obtained considering also galaxies identified as AGN on the basis of X-ray or radio data.  | GASP-RPS |  | LIT-RPS |  | ALL-RPS
---|---|---|---|---|---
$\log(M_{\ast}/M_{\odot})$ | $N_{AGN}/N_{TOT}$ | $\rm f_{AGN}$ |  | $N_{AGN}/N_{TOT}$ | $\rm f_{AGN}$ |  | $N_{AGN}/N_{TOT}$ | $\rm f_{AGN}$
$\geq 9.0$ | 12/50 | $0.24^{+0.07}_{-0.06}$ |  | 19/65(23/65) | $0.29_{-0.05}^{+0.06}(0.35_{-0.06}^{+0.06})$ |  | 31/115(35/115) |  $0.27_{-0.04}^{+0.04}$ ($0.30_{-0.04}^{+0.04}$)
$\geq 10.0$ | 12/25 | $0.46^{+0.10}_{-0.09}$ |  | 17/31(19/31) |  $0.55_{-0.09}^{+0.09}$ ($0.61_{-0.09}^{+0.08}$) |  | 29/57(31/57) | $0.51_{-0.07}^{+0.07}(0.54_{-0.07}^{+0.07})$
Figure 6: Comparison of the AGN fraction in the different samples. Red and
orange lines refer to the ALL-RPS sample: the AGN fraction for galaxies with
$M_{\ast}>10^{9}M_{\odot}$ is shown by the thick orange line, that for
galaxies with $M_{\ast}>10^{10}M_{\odot}$ by the thick red line. The matched
shaded areas indicate by how much fractions change if we consider also the AGN
classified on the basis of X-ray or radio data (see text for details). Blue
and light blue violin plots refer to the MaNGA-Ref sample, for the two mass
bins as indicated in the labels. They show the probability density of the
bootstrap random extractions mass-matched to the ALL-RPS sample, at different
AGN fraction values, smoothed by a kernel density estimator. Grey horizontal
dashed and dotted lines represent median values and 25% and 75% percentiles of
the AGN fraction, respectively. Values of the pivotal confidence intervals of
the bootstrap distribution are also reported: the mass-matched MaNGA fractions
are lower than the ALL-RPS fractions at the 99.99% confidence level for
galaxies with $M\geq 10^{9}\,M_{\odot}$ and at the 99.96% level for $M\geq
10^{10}\,M_{\odot}$.

In the previous section we have quantified the incidence of AGN in ram
pressure stripped galaxies. We have seen that they represent 24% of the
overall, both for GASP-RPS and LIT-RPS samples. In the following we will
always exclude masses $<10^{9}\,M_{\odot}$, in all samples. Table 6 presents
the AGN fractions in GASP-RPS and LIT-RPS separately for stellar masses $\geq
10^{9}M_{\odot}$ and $\geq 10^{10}M_{\odot}$.151515From now on we exclude from
this analysis ID345 at z=0.73, considered a redshift outlier. The fractions in
the LIT-RPS sample are always higher than in GASP-RPS (0.29 vs 0.24 and 0.55
vs 0.46, respectively for the two mass bins), but are compatible within the
binomial errors. Including also the 4 X-ray/radio AGN in the literature,
fractions are slightly higher. We note that also the stellar mass
distributions of the two samples are similar (Fig.5), and indeed a Kolmogorov-
Smirnov (KS) test cannot exclude that they are drawn from the same parent
distribution.

It is therefore appropriate to join the two RPS samples, to obtain the largest
possible statistics161616Note that the galaxy JO147 appears in both samples,
from now on we will just consider it once., and derive the total AGN
fractions: $0.27^{+0.04}_{-0.04}$ at masses $\geq 10^{9}\,M_{\odot}$ and
$0.51^{+0.07}_{-0.07}$ for masses $\geq 10^{10}\,M_{\odot}$. These fractions
are high, but less extreme than the fraction that would have been inferred
from the P17b results, where 6/7 galaxies were AGN with a corresponding
fraction of 0.86${}^{+0.18}_{-0.09}$. This is due to the fact that the 2017
sample was composed of all massive Jstage=2 galaxies, and as we have seen in
the previous sections these are the most favorable conditions for AGN activity
in RPS galaxies.

We now aim at establishing whether the AGN frequency is connected to RPS and
therefore we compare the measured fractions to those obtained exploiting the
MaNGA-Ref sample, used as representative of non-ram pressure stripped field
galaxies.

As for the other samples, also in MaNGA-Ref AGN are located preferentially
among the most massive galaxies (right panel of Figure 3). The AGN fraction is
$0.15^{+0.01}_{-0.01}$171717Previous MaNGA works (e.g. Sánchez et al. 2018)
have found a significantly lower AGN incidence. However previous analysis has
not applied any cut in SSFR as we do, and have adopted much more stringent
definitions (emission line ratios above the Kewley demarcation lines
considering all the BPT diagrams simultaneously and H$\alpha$ equivalent width
$>1.5$Å in the central regions) for AGN, therefore results are not directly
comparable. above $\log(M_{\ast}/M_{\odot})\geq 9.0$ and
$0.28^{+0.02}_{-0.02}$ for $\log(M_{\ast}/M_{\odot})\geq 10.0$.

A KS test excludes that the MaNGA-Ref mass distribution is drawn from the same
parent distribution of the GASP+LIT sample (Fig. 5). Since the probability to
find an AGN increases with galaxy mass, to properly compare the fractions
obtained from MaNGA-Ref and ALL-RPS we need to control for the different mass
distributions. We perform a bootstrap random extraction of the MaNGA sample to
create 10000 subsamples with the same mass distribution of the ALL-RPS sample
matching the number of ALL-RPS galaxies in bins of 0.3 dex in stellar mass.
For each of the extracted samples we compute the AGN fraction $f_{AGN}$. We
repeat the random extraction considering separately two stellar mass ranges,
$\rm M_{\ast}\geq 10^{9}\,M_{\odot}$ and $\rm M_{\ast}\geq 10^{10}M_{\odot}$.
Violin plots with the $f_{AGN}$ distributions for the two mass ranges, their
medians and the 25th and 75th percentiles are shown in Fig.6. We find that the
median $f_{AGN}$ of the 10000 realizations of mass-matched MaNGA galaxies are
$f_{AGN}=$0.18 for $M\geq 10^{9}\,M_{\odot}$ and 0.35 for $M\geq
10^{10}\,M_{\odot}$. These values are lower than the corresponding values in
the ALL-RPS sample, which are 0.27 and 0.51, respectively. In order to assess
the significance of the difference between the RPS and non-RPS samples, we
compute the pivotal confidence intervals of the bootstrap distribution and
find that the mass-matched MaNGA fractions are lower than the ALL-RPS
fractions at the 99.99% confidence level for galaxies with $M\geq
10^{9}\,M_{\odot}$ ($>$ 99.99% if we include the 4 radio/X-ray AGNs), and at
the 99.96% level for $M\geq 10^{10}\,M_{\odot}$ ($>$ 99.99% if the 4 radio-X-
ray AGNs are included) (see Fig.6).

Since the three samples considered span slightly different redshift ranges, we
performed the bootstrap random extractions also limiting all samples to $z\leq
0.075$ (the GASP redshift limit). Results remained unchanged as fractions are
affected only at the 1% level at most. Finally, we also tried comparing mass-
matched MaNGA samples separately with GASP-RPS and LIT-RPS. Though clearly the
statistics decreases, we still find high probabilities that the mass-matched
MaNGA sample has lower AGN fractions than the RPS samples (81.4% and 95.4% for
GASP-RPS in the two galaxy mass ranges, and $>99.99$% for both LIT-RPS
samples).

From our analysis the incidence of AGN activity among ram pressure stripped
galaxies is significantly higher than that in the MaNGA field control sample.
A ram pressure stripped galaxy has a 1.5 times higher probability to host an
AGN than a similar non-ram pressure stripped galaxy. This effect is not driven
by different stellar mass distributions and points to a connection between RPS
and AGN activity.

A larger (of the order of hundreds), homogeneous sample of ram-pressure
stripped galaxies with integral-field spectroscopy would be needed to place
these results on more solid ground. Since this is currently unavailable, the
analysis presented here collects the best available datasets for addressing
the question of the AGN-RPS connection. There are however several caveats
which are worth stressing.

First of all, the AGN fraction depends strongly on the criteria adopted when
using the BPT diagram. In this paper, we are including both LINER-like and
Seyfert AGN, in order to capture also low luminosity AGN. This is done in all
samples considered in a similar manner, so it should not affect the relative
incidence and the main conclusions of this work, but the pure AGN fractions
will strongly depend on the initial choice.

Second, although great care has been taken to ensure the most homogeneous
analysis possible, the datasets are clearly dis-homogeneous. Even GASP and
MaNGA, that are both based on integral-field data for every galaxy, have been
observed with different instruments, thus have different resolutions, spaxel
size etc, and span a slightly different redshift range (see above for
invariance of AGN fractions with the redshift interval adopted). The
literature sample, obviously, is in itself very heterogeneous, with the
spectroscopic information coming from many different sources. The results
shown in this paper should therefore be taken with caution, and revisited once
large homogeneous samples will become available.

Third, in principle it is possible that the high AGN fraction we observe in
RPS galaxies is not a consequence of RPS itself. If the AGN incidence in
cluster star-forming galaxies was higher in general than in similar galaxies
in the field, the differences with respect to MaNGA would go in the same
direction of what we observe. However, as mentioned also above, the AGN
fraction in the GASP non-ram-pressure stripped sample is small (2/49) (Vulcani
et al., 2021). This sample is composed both of cluster and field “undisturbed”
galaxies. If we consider only the GASP cluster control sample (star-forming
and late-types), there are no AGN (B. Poggianti et al. in prep). So, this
caveat is unlikely to be responsible for our results.

Finally, we note that we are not studying the global AGN fraction in clusters,
but the occurrence of AGN activity in a very specific class of cluster
galaxies: those with clear signs of ram-pressure stripping, which are all
star-forming and late-type galaxies and thus represent a small fraction of the
total cluster galaxy population that are dominated by early-type galaxies.
Therefore, our results cannot be used to infer the total AGN fraction in
clusters and not necessarily show similar trends.

##  5 Summary

In this paper we have investigated the occurrence of AGN activity in ram-
pressure stripped galaxies in local clusters, comparing it with the AGN
frequency in a control sample of field galaxies. In all cases, we rely on BPT
diagnostic diagrams based on the [NII] line. All the galaxies analyzed in this
paper are star-forming and morphologically late-type galaxies.

First, we assembled two samples of ram pressure stripped galaxies. We have
used the MUSE data of 51 galaxies observed in the context of the GASP survey
(GASP-RPS) finding a Seyfert2 and 4 LINER-like AGN hosts previously unknown,
in addition to the 7 galaxies already discussed in P17b and Fritz et al.
(2017). We have then conducted a search in the literature assembling a sample
of 82 ram-pressure stripped galaxies for which it was possible to retrieve
information on their nuclear activity (either from IFU or slit/fibre) (LIT-
RPS).

We find similar fractions of AGN in GASP and in literature ram-pressure
stripped galaxies, with the AGN incidence being slightly higher in the
literature than in GASP, but consistent within the uncertainties. Overall, the
AGN fraction in the total GASP-RPS+LIT-RPS sample is $0.27^{+0.04}_{-0.04}$ at
masses $M_{*}\geq 10^{9}\,M_{\odot}$ and $0.51^{+0.07}_{-0.07}$ at $M_{*}\geq
10^{10}\,M_{\odot}$. Thus, more than half of the $\geq 10^{10}\,M_{\odot}$
ram-pressure stripped galaxies show AGN activity.

We then compare these findings with those for a sample of galaxies drawn from
the MaNGA survey and inhabiting dark matter haloes with masses $\leq
10^{13}M_{\odot}$. With this halo mass cut we ensure that rich groups and
clusters are excluded, hence these galaxies are not undergoing significant RPS
and this can serve as a control field sample. We perform a bootstrap random
extraction from the MaNGA sample to create 10000 realizations with the same
stellar mass distribution of the ram-pressure stripped sample.

Our two main results can be summarized as follows:

1) The great majority of galaxies hosting an AGN, in all three samples
considered, are high-mass galaxies. There are just very few galaxies with an
AGN at masses below $10^{10}\,M_{\odot}$ (no one below $10^{10.5}\,M_{\odot}$
in GASP). As a consequence, the AGN fractions are higher above these limits,
and very low below. Another factor that could be playing a role is the ram-
pressure strength or phase (Jstage): the highest AGN fractions are observed
among the most strongly ram-pressure stripped galaxies with the longest tails.
However, with the current samples it is hard to disentangle between mass and
Jstage effects.

2) Even after matching the galaxy mass distributions, the AGN incidence in the
field MaNGA sample is lower than in the ram-pressure stripped sample at the
$\geq 99.96\%$ confidence level. Overall, a ram pressure stripped galaxy has a
1.5 times higher probability to host an AGN than a similar non-ram pressure
stripped galaxy. This supports the hypothesis that ram-pressure can trigger
the AGN activity.

## Acknowledgements

We thank the Referee for the useful suggestions that improved the presentation
of the work. We warmly thank Jong-Ho Shinn from the Korea Astronomy and Space
Science Institute for useful discussion regarding the statistical analysis. GP
thanks A. Werle for the helpful discussion. Based on observations collected at
the European Organization for Astronomical Research in the Southern Hemisphere
under ESO programme 196.B-0578. This project has received funding from the
European Research Council (ERC) under the European Union’s Horizon 2020
research and innovation programme (grant agreement No. 833824). We acknowledge
financial contribution from the grant PRIN MIUR 2017 n.20173ML3WW_001 (PI
Cimatti), from the INAF main-stream funding programme (PI Vulcani) and from
the agreement ASI-INAF n.2017-14- H.0 (PI A. Moretti). Y.J. acknowledges
financial support from CONICYT PAI (Concurso Nacional de Inserción en la
Academia 2017) No. 79170132 and FONDECYT Iniciación 2018 No. 11180558. J.F.
acknowledges financial support from the UNAM- DGAPA-PAPIIT IN111620 grant,
México.

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##  Appendix A Mass estimates

To compute stellar masses for those galaxies for which they are missing in the
literature. we use the Bell et al. (2001) relation between the mass-to light
ratio of a galaxy and its color:

$\log\left(\frac{M}{L_{\lambda}}\right)=a_{\lambda}+b_{\lambda}\cdot\rm COL$
(A1)

where $L_{\lambda}$ is the luminosity in a band, indicated with $\lambda$, COL
is a photometric color and $a_{\lambda}$ and $b_{\lambda}$ are coefficients
depending on both $\lambda$ and COL. For our calculations, we used the Bell et
al. tables for a solar metallicity Z = 0.02 and a Bruzual & Charlot (2003) SSP
model, converting from a Salpeter (1955) to a Chabrier (2003) IMF subtracting
a factor -0.24. For one galaxy, 235144-260358 (Cortese et al., 2007), in order
to use the formula (A1) we first converted _HST_ magnitudes to an UBV
phometric system with the use of calibration equations for the Advanced Camera
for Surveys (ACS) presented in Sirianni et al. (2005). We assume a typical 0.3
dex uncertainty on the computed stellar masses, which we take as bin size of
the stellar mass distribution.