# Post-Starburst Properties of Post-Merger Galaxies

Wenhao Li,1 Preethi Nair,1 Kate Rowlands,2 Karen Masters,3 David Stark,2 Niv
Drory,4 Sara Ellison,5 Jimmy Irwin,1 Shobita Satyapal,6 Amy Jones,2 William
Keel,1 Kavya Mukundan1 and Zachary Tu7
1Dept. of Physics and Astronomy, The University of Alabama, Tuscaloosa, AL
35487, USA
2Space Telescope Science Institute, Baltimore, MD 21218, USA
3Dept. of Physics and Astronomy, Haverford College, Haverford, PA 19041, USA
4Dept. of Astronomy, The University of Texas at Austin, Austin, TX 78712, USA
5Dept. of Physics and Astronomy, The University of Victoria, Victoria, BC V8P
5C2, Canada
6Dept. of Physics and Astronomy, George Mason University, Fairfax, VA 22030,
USA
7Dept. of Astronomy, The University of Washington, Seattle, WA 98105, USA
E-mail<EMAIL_ADDRESS>
(Accepted May 12 2023. Received March 24 2023)

###### Abstract

Post-starburst galaxies (PSBs) are transition galaxies showing evidence of
recent rapid star formation quenching. To understand the role of galaxy
mergers in triggering quenching, we investigate the incidence of PSBs and
resolved PSB properties in post-merger galaxies using both SDSS single-fiber
spectra and MaNGA resolved IFU spectra. We find post-mergers have a PSB excess
of 10 – 20 times that relative to their control galaxies using single-fiber
PSB diagnostics. A similar excess of $\sim$ 19 times is also found in the
fraction of central (C)PSBs and ring-like (R)PSBs in post-mergers using the
resolved PSB diagnostic. However, 60% of the CPSBs + RPSBs in both post-
mergers and control galaxies are missed by the single-fiber data. By visually
inspecting the resolved PSB distribution, we find that the fraction of
outside-in quenching is 7 times higher than inside-out quenching in PSBs in
post-mergers while PSBs in control galaxies do not show large differences in
these quenching directions. In addition, we find a marginal deficit of HI gas
in PSBs relative to non-PSBs in post-mergers using the MaNGA-HI data. The
excesses of PSBs in post-mergers suggest that mergers play an important role
in triggering quenching. Resolved IFU spectra are important to recover the
PSBs missed by single-fiber spectra. The excess of outside-in quenching
relative to inside-out quenching in post-mergers suggests that AGN are not the
dominant quenching mechanism in these galaxies, but that processes from the
disk (gas inflows/consumption and stellar feedback) play a more important
role.

###### keywords:

galaxies: evolution – galaxies: interactions – galaxies: starburst

††pubyear: 2023††pagerange: Post-Starburst Properties of Post-Merger
Galaxies–Post-Starburst Properties of Post-Merger Galaxies

##  1 Introduction

The galaxy population in the local universe can be broadly divided into two
main categories; blue star-forming spirals and red quiescent ellipticals. This
bimodality is well characterized by the color vs magnitude diagram of the
galaxy population (Blanton et al., 2003; Baldry et al., 2004; Wyder et al.,
2007; Jin et al., 2014), which shows the star-forming ‘blue cloud’ separated
from the quiescent ‘red sequence’. This bimodal trend is built up over cosmic
time showing star-forming galaxies are quenching their star formation and are
evolving from the ‘blue cloud’ to the ‘red sequence’ (Bell et al., 2004;
Arnouts et al., 2007; Faber et al., 2007; Jannuzi et al., 2007). However, what
triggers this evolution and the truncation of star formation is not fully
understood.

Theoretically, galaxy mergers offer a scenario where star formation quenching
is expected to be seen. Galaxy mergers can trigger both enhanced star
formation (Barnes & Hernquist, 1996; Cox et al., 2006; Lin et al., 2007) and
active galactic nuclei (AGN, Di Matteo et al., 2005; Hopkins et al., 2006,
2008; Hickox et al., 2009). Both gas inflows and gas consumption by star
formation during mergers (Moreno et al., 2015) and the merger-driven AGN or
stellar feedback (Bower et al., 2006; Croton et al., 2006; Fabian, 2012; Lin
et al., 2017) can cause star formation quenching. However, star formation
quenching due to gas inflows/consumption and AGN/stellar feedback would lead
to different resolved star formation histories. On one hand, simulations have
shown that galaxy mergers can induce gas inflows towards the center, resulting
in star formation suppression in the outskirts and enhanced starburst in the
center, followed by a truncation of star formation due to gas consumption
(Naab & Burkert, 2001; Moreno et al., 2015). Alternatively, mergers can also
trigger widespread starbursts extending over large scales (Schweizer, 2005;
Kim et al., 2009; Saitoh et al., 2009; Hopkins et al., 2013), leading to rapid
consumption of the gas in the outskirts first. Both scenarios can cause star
formation quenching from the outskirts and progressively moving inward,
leading to an outside-in quenching direction. On the other hand, simulations
support that AGN feedback can drive kinetic winds, which lead to star
formation quenching from the center extending to the outskirts, resulting in
an inside-out quenching direction (Nelson et al., 2021). This was also seen in
observations (e.g., Sánchez et al., 2018), although Davies et al. (2022)
suggests that AGN feedback cannot fully quench star formation in short merger
timescales but can have long-term effects on the evolution of galaxies over a
few Gyrs. Winds driven by central stellar feedback could also be a potential
cause of inside-out quenching (Agertz et al., 2013).

Many previous works studying quenching have focused on post-starburst galaxies
(hereafter, PSBs). PSB galaxies are a rare population of galaxies spanning a
wide range of color from the ‘blue cloud’ through the ‘green valley’ (Wyder et
al., 2007; Martin et al., 2007; Yan et al., 2009) to the ‘red sequence’. They
have undergone a recent starburst followed by a rapid truncation of star
formation, which are currently transitioning from star-forming to quiescent.
PSB galaxies are classified by their spectra which show the presence of strong
Balmer absorption from intermediate-age (A-type) stars and an absence of
nebular emission from hot young (O- and B-type) stars (Dressler & Gunn, 1983;
Couch & Sharples, 1987; Zabludoff et al., 1996; Poggianti et al., 1999; Goto
et al., 2003; Goto, 2005; Young et al., 2014). As their spectra show a
superposition of quiescent galaxy spectra and spectral features of A-type
stars, PSB galaxies are also known as ‘E+A’ or ‘K+A’ galaxies. These spectral
features suggest that a recent starburst happened $<$1 Gyr ago and there is no
ongoing star formation currently, indicating a rapid quenching of star
formation. Hence, PSB galaxies are the ideal targets to investigate star
formation quenching.

Hydrodynamic simulations show that galaxy mergers can trigger a strong
starburst followed by a rapid truncation of star formation, and evolve into a
PSB phase and eventually the quiescent phase (Wild et al., 2009; Snyder et
al., 2011; Zheng et al., 2020). In addition to galaxy mergers, cosmological
simulations have also shown that a diversity of mechanisms such as ram
pressure stripping, shocks and stellar/AGN outflows can also quench star
formation in non-merging galaxies and trigger them into a PSB phase (Pawlik et
al., 2019; Davis et al., 2019). However, Davis et al. (2019) have found that
galaxy mergers at z $\sim$ 0 – 2 tend to be the most frequent cause of PSB
galaxies in simulations.

Observationally, nearly all previous studies on the merger-PSB relation began
with a PSB galaxy sample and then investigated their morphologies to constrain
the merger fractions. Numerous studies have found a significant fraction (13%
– 64%) of PSB galaxies showing disturbed morphologies, which indicates a
recent merger or interaction with a nearby companion (Zabludoff et al., 1996;
Blake et al., 2004; Goto, 2005; Yang et al., 2008; Pracy et al., 2009; Alatalo
et al., 2016; Pawlik et al., 2016). Studies comparing the merger fraction in
PSB galaxies relative to star-forming control galaxies have found a merger
excess of a factor of 2 – 4, suggesting mergers play an important role in
triggering the PSB phase of galaxies (Pawlik et al., 2018; Meusinger et al.,
2017; Sazonova et al., 2021; Wilkinson et al., 2022).

Studies investigating the PSB fraction in merging galaxies are lacking in the
literature. The first and only study so far is presented by Ellison et al.
(2022). They studied a sample of $\sim$ 500 post-merger galaxies with imaging
from the Canada-France Imaging Survey (CFIS111 https://www.cadc-ccda.hia-
iha.nrc-cnrc.gc.ca/en/community/unions/MegaPipe_CFIS_DR3.html) and single-
fiber spectra from the Sloan Digital Sky Survey (SDSS, York et al., 2000).
They used different PSB classification methods and found a PSB excess of a
factor of 30 – 60 in post-merger remnant galaxies compared to non-merging
controls, while there is no excess in close galaxy pairs (or early-stage
mergers). Their results highly suggest that galaxy mergers can rapidly quench
star formation and this process happens mainly after the coalescence of the
two nuclei.

However, most previous studies which identified PSB galaxies were based on
SDSS single-fiber spectra (e.g. Goto, 2005; Alatalo et al., 2016; Pawlik et
al., 2018; Wilkinson et al., 2022; Ellison et al., 2022) and would only
capture the fluxes within the 2.5′′ fiber pointing to the center of the
galaxies. The single-fiber diagnostic may only be capable of finding PSB
galaxies with star formation quenching in the center and will neglect those
with star formation quenching in the outskirts. In fact, PSB galaxies have
been found to have a variety of resolved quenching morphologies by integral-
field-unit (IFU) spectroscopy (Rowlands et al., 2018b; Quai et al., 2019; Chen
et al., 2019; Vulcani et al., 2020; French, 2021; Xu et al., 2022). Hence, IFU
observation will provide a more complete census of PSB galaxies, which also
enables us to study their resolved quenching properties.

Gas content is another way to study star formation quenching. As gas can be
consumed in star formation or ionized/expelled by stellar or AGN feedback
during mergers, gas suppression is expected to be seen when star-formation
quenching happens in late-merger systems (Hopkins et al., 2006; Fabian, 2012;
Hopkins et al., 2013; Park et al., 2018). However, previous studies on the gas
content in merger systems have found different results. Some have found a
deficit of neutral atomic hydrogen (HI) gas in mergers compared to non-merging
galaxies (Hibbard & van Gorkom, 1996; Georgakakis et al., 2000; Yu et al.,
2022), while others have found an enhancement of HI gas in mergers (Casasola
et al., 2004; Janowiecki et al., 2017; Ellison et al., 2018). In addition,
some studies have found no significant difference between the HI gas content
in galaxy mergers and non-merging galaxies (Ellison et al., 2015; Zuo et al.,
2018). One possible reason for these contradictory results is the differences
in merger stages in the various samples studied. Previous studies (Scudder et
al., 2012; Patton et al., 2013; Knapen et al., 2015; Pan et al., 2018, 2019)
have found the star formation rates in close pairs increase with decreasing
nuclear separations, suggesting star formation activities are more violent
near the coalescence phase. Early mergers with low star formation rates may
not have consumed much gas while late stage post-merger remnants may show a
gas deficit due to gas consumption, underscoring the importance of merger
stage in assessing gas content in mergers compared with non-mergers.

Gas depletion is expected to be seen in PSB galaxies where star formation is
being quenched or has been quenched. Surprisingly, previous studies have found
a significant amount of cold molecular gas in PSB galaxies regardless of
merger status (Rowlands et al., 2015; French et al., 2015, 2018; Alatalo et
al., 2016; Baron et al., 2022; Smercina et al., 2022; Sun & Egami, 2022; Otter
et al., 2022), which leads to a question of how PSB galaxies quench star
formation without suppressing the gas reservoir. French et al. (2023) studies
the dense molecular gas traced by HCN/HCO+/HNC (1-0) in six CO-detected PSB
galaxies, which are selected with low H$\alpha$ emission (H$\alpha$ $<$ 3Å)
and strong Lick H$\delta_{A}$ absorption (H$\delta_{A}-\sigma$H$\delta_{A}>$
4Å, French et al., 2015). Their results suggests that although PSB galaxies
still contain significant (CO-traced) lower density gas reservoirs, the lack
of (HCN/HCO+/HNC-traced) dense gas is the reason of their current quiescence.

In this work, we try to understand the relation between star formation
quenching and galaxy mergers by studying the post-starburst properties and HI
gas content of a sample of post-merger remnant galaxies. In Section 2, we
describe our post-merger and control samples and the observational data. In
Section 3, we introduce the PSB classification methods used in our work. In
Section 4, we present the results of PSB fractions and the resolved quenching
history of our sample and we relate them to the HI gas content. We discuss our
results in Section 5 and we summarize this work in Section 6.

Figure 1: Stellar mass and redshift distribution of the 1,051 post-merger
galaxies (red solid) and the 10,510 control galaxies (black dashed) from SDSS
DR14 parent sample. Each post-merger has 10 closest controls matched in both
stellar mass and redshift.

Figure 2: Star formation rates (SFRs) vs. stellar mass diagram of the DR14
post-merger and control sample. The contours represent the 90,234 galaxies
from the DR14 parent sample with the outer most contour including 99% of the
sample. The two division lines are drawn by eye based on the contours, which
divide galaxies into active star-forming (SF), transition and quiescence.

##  2 Sample and Data

###  2.1 SDSS Post-Merger Sample

The parent galaxy sample in this work is a volume-limited ($0.02\leq z\leq
0.06$ and 9 $\leq$ log M∗/M${}_{\odot}\leq$ 12) morphology catalog of
$\sim$113,000 galaxies visually classified from the Sloan Digital Sky Survey
(SDSS, York et al., 2000) Data Release 14 (Nair et al. 2023, in prep). In
short, the $\sim$ 113,000 galaxies in this catalog were visually classified by
co-author Nair following the methodology in Nair & Abraham (2010) using both
SDSS imaging and deeper data from the NOAO Legacy Survey (Schlegel et al.,
2021). Galaxies were classified into post-merger remnants, double nuclei,
close pairs, disturbed galaxies, and non-interacting galaxies. Features such
as bars, rings, and spiral arms were also identified.

In order to provide a robust PSB classification, we required the $r$-band
signal-to-noise ratio (S/N) $>$ 10 in the SDSS spectra (Goto, 2007; Wilkinson
et al., 2022) and the median S/N $>$ 10 per pixel of the whole spectrum (Chen
et al., 2019). This reduces the parent sample to 90,234 galaxies. There are
1,165 post-mergers identified in this reduced parent sample.

For these post-mergers, we built up a control sample of non-interacting
galaxies by searching for a unique closest match in stellar mass and redshift
within a search range of $\Delta$log M∗/M${}_{\odot}\pm 0.1$ and $\Delta z\pm
0.005$. We required 10 unique control galaxies for each post-merger to obtain
robust statistics. There are 1,051 post-mergers matched with 10 unique
controls while the other 114 have less than 10 controls. We only use the 1,051
post-mergers and 10,510 unique control galaxies in the following PSB analysis
(DR14 post-merger and control sample, hereafter). A Kolmogorov-Smirnov (KS)
test provides a p-value of 0.997 for the stellar mass distributions and 0.999
for the redshift distributions between the DR14 post-merger and control
sample, confirming that the two samples are indistinguishable in stellar mass
and redshift at a 3$\sigma$ confidence level. The stellar mass and redshift
distributions of the two samples are shown in Figure 1.

The nebular emission and absorption equivalent widths measured by the SDSS
single-fiber spectra are obtained from the value-added catalog compiled by the
Max Planck Institute for Astrophysics and the John Hopkins University (MPA-
JHU, Brinchmann et al., 2004; Kauffmann et al., 2003; Tremonti et al., 2004)
DR8. The MPA-JHU catalog also provides derived galaxy properties such as the
aperture corrected total stellar masses and star formation rates (SFRs), which
are used in this work. As described in the MPA-JHU catalog, SFRs are computed
within the galaxy fiber aperture using the nebular emission lines (Brinchmann
et al., 2004). SFRs outside of the fiber are estimated using the galaxy
photometry following Salim et al. (2007). For AGN and galaxies with weak
emission lines, SFRs are estimated from the photometry. All galaxies in our
post-merger sample and control sample have measurements in the MPA-JHU DR8
catalog. As shown in Figure 2, post-merger galaxies (red points) span a range
of star formation rates and stellar masses, migrating from the active star-
forming “blue cloud” to the quiescent “red sequence”. Despite the fact that
post-mergers have shown strong SFR enhancements (e.g. Ellison et al., 2013;
Bickley et al., 2022), there are many quenched post-mergers as well. The
environment density used in this work is given in Baldry et al. (2006), which
provides measurements for all SDSS DR7 galaxies. There are 827/1051 (78.7%)
post-mergers and 8,435/10,510 (80.3%) control galaxies that have environment
density measurements.

###  2.2 MaNGA Data

The Mapping Nearby Galaxies at Apache Point Observatory (MaNGA, Bundy et al.,
2015) is one of the three core programs in the fourth-generation Sloan Digital
Sky Survey (SDSS-IV, Blanton et al., 2017). MaNGA is an integral-field-unit
(IFU) spectroscopic survey of 10,010 galaxies using the 2.5-meter telescope at
the Apache Point Observatory (APO, Gunn et al., 2006). The IFUs employed in
MaNGA are hexagonal fiber bundles with sizes varying from 19 fibers to 127
fibers, which correspond to a diameter from 12′′ to 32′′ on the sky. Each
fiber has a diameter of 2′′. Dithered observations are used to cover the field
of view (Law et al., 2016) with a median spatial resolution of $\sim$ 2.5′′
FWHM. Spectra are taken using the Baryon Oscillation Spectroscopic Survey
(BOSS, Smee et al., 2013) spectrographs, which covers a wavelength range over
3600-10300 Å at a resolving power of R $\sim$ 2000\. MaNGA provides data cubes
with a spaxel size of $0.5^{\prime\prime}$ and reaches a target S/N of 4-8 Å-1
in the outskirts of each MaNGA galaxy (Yan et al., 2016). Two-thirds of the
full MaNGA galaxy sample has been observed out to 1.5 effective radius
($R_{e}$) while the other 1/3 has been observed out to 2.5 $R_{e}$ (Wake et
al., 2017).

The MaNGA Data Analysis Pipeline (DAP, Westfall et al., 2019; Belfiore et al.,
2019) uses stellar templates drawn from the MILESHC library to determine the
stellar kinematics and uses the MASTARSSP library to fit the stellar continuum
during the emission-line fitting module. We cross-matched the entire parent
sample of 90,234 galaxies with the latest MaNGA Product Launch 11 (MPL-11)
from SDSS DR17, which corresponds to the DAP version of v3_1_1-3.1.0. We found
5,854 galaxies in our sample are observed in MaNGA. Specifically, there are
156 post-mergers observed in MaNGA. For these MaNGA observed post-merger
galaxies, we also built up a control sample of MaNGA observed non-interacting
galaxies, similar to our single-fiber control sample. There are 136 MaNGA
observed post-mergers with 10 unique controls within our stellar mass and
redshift range (MaNGA observed post-merger and control sample, hereafter).

Nebular emission and absorption fluxes and equivalent widths are extracted
from the MaNGA “MAPS-SPX-MILESHC-MASTARSSP” data analysis pipelines, which
provide measurements for each individual spaxel. MaNGA also provides the
spatially Voronoi-binned (VOR10) data pipelines and the measurements on the
binned spectra. However, the measurements on individual spaxels (SPX) provide
a better insight of the spaxel distribution. It should be noted that using the
Voronoi-binned data pipelines would not affect our results qualitatively.

Figure 3: The equivalent width of H$\alpha$ emission vs. H$\delta_{A}$
absorption for the DR14 post-merger sample (left) and control sample (right)
based on SDSS single-fiber spectra. PSB galaxies (purple) are identified to be
located inside the lower right solid box region (Chen et al., 2019). Non-PSB
galaxies are classified into star-forming (blue dots), green valley (green
dots) and retired galaxies (red dots) based on their H$\alpha$ equivalent
width. The MaNGA resolved PSBs (Central PSBs, Ring-like PSBs and Irregular
PSBs) are over-plotted in different symbols, which will be discussed in
Section 4.2.

###  2.3 HI Data

MaNGA-HI (Masters et al., 2019) is an HI follow-up program of the SDSS-IV
MaNGA Survey galaxies using the Robert C. Byrd Green Bank Telescope (GBT) with
supplemented data from the Arecibo Legacy Fast ALFA (ALFALFA, Giovanelli et
al., 2005) Survey. The HI single-dish observations from GBT have a beam size
of FWHM of 9.1′ while the observations from ALFALFA have a beam size of FWHM
of 3.5′. The MaNGA HI program selects MaNGA galaxies with z$<$0.05 that lack
HI data from ALFALFA and is agnostic to morphological properties. All targets
were observed using standard position-switching to the same depth ($\sim$1.5
mJy, $\sim$15 minutes on-source). The detailed observation strategy is
described in Masters et al. (2019). The HI data used in this work are obtained
from the MaNGA-HI DR3 catalog (Stark et al., 2021), which provides HI mass
measurements for detections and HI mass upper limits for non-detections of
$\sim$ 6,000 MaNGA galaxies. Following Stark et al. (2021), we excluded
sources with contamination from OFF detections, baseline structure, or nearby
companions by placing a cut on the source confusion probability p $<$ 0.1. Out
of the 136 post-mergers and 1,360 control galaxies observed in MaNGA, 79 post-
mergers and 893 control galaxies have data in the MaNGA-HI DR3 catalog. Out of
the 79 post-mergers, 37 have HI detections above a 3$\sigma$ threshold and
have gas mass measurements. The other 42 are non-detections and only have
upper limits on gas mass. In the control sample, 330 control galaxies have HI
gas mass measurements while the other 563 have upper limits. We will discuss
the HI gas fraction in post-mergers and controls in Section 4.

##  3 Post-Starburst Identification

In this section, we introduce the methods adopted to identify PSB galaxies
using the SDSS single-fiber spectra and the MaNGA resolved spectra. Post-
starburst galaxies can be identified by the presence of strong Balmer
(H$\delta$) absorption which indicates an intermediate-age stellar population,
and weak or no (H$\alpha$ and/or [OII]) emission suggesting little or no on-
going star formation. There are multiple methods which can identify PSB
galaxies (see Wilkinson et al., 2022 and references therein). To compare with
previous studies in a consistent way, we first used the traditional ‘E+A’ PSB
identification criterion from Goto (2007) with single-fiber spectra. It
requires PSB galaxies to have:

  * •

EW(H$\delta_{A}$) $>$ 5Å

  * •

EW(H$\alpha$) $>$ -3Å

  * •

EW([OII]) $>$ -2.5Å

  * •

$r$-band S/N $>$ 10

where the negative equivalent widths indicate emission and positive values
indicate absorption. The H$\delta_{A}$ index is the equivalent width of
H$\delta$ absorption feature in the bandpass 4083-4122Å with continuum
bandpasses of 4041.6-4079.75Å and 4128.5-4161.0Å (Worthey et al., 1994;
Worthey & Ottaviani, 1997). It should be noted that Nielsen et al. (2012) show
that the PSBs selected by the Goto (2007) method are not starburst galaxies
that are obscured by dust. However, this method has strict limits on the EW of
H$\alpha$ and [OII] emission lines and may bias against PSB galaxies with
shocks or AGN, as well as PSB galaxies that are not yet completely quenched
(Kocevski et al., 2011; Alatalo et al., 2014; Yesuf et al., 2014).

The second method we used on the single-fiber spectra is the Chen et al.
(2019) PSB selection criteria, which can select PSB galaxies that have
quenched rapidly recently and also includes those which have not yet fully
quenched their star formation. It requires PSB galaxies to have (see Figure
3):

  * •

EW(H$\delta_{A}$) $>$ 3Å

  * •

EW(H$\alpha$) $>$ -10Å

  * •

log (-EW(H$\alpha$)) $<0.23\ \times$ EW(H$\delta_{A}$) - 0.46

  * •

median spectral S/N $>$ 10 per pixel

where the third equation in the Chen et al. (2019) selection criteria is
determined by an evolutionary track model of a starburst followed by a
truncation with an e-folding time of 300 Myr (see details in Chen et al.,
2019). For galaxies that are classified as non-PSBs by the Chen et al. (2019)
method, we classified them into star-forming (EW(H$\alpha$) $<$ -10Å), green
valley (-10Å $<$ EW(H$\alpha$) $<$ -3Å) and retired galaxies (EW(H$\alpha$)
$>$ -3Å) based on their H$\alpha$ equivalent width (see Figure 3). This is
motivated by the evolution track shown in Chen et al. (2019) and also by the
work of Cid Fernandes et al. (2011), which classifies star-forming galaxies
from retired galaxies based on EW(H$\alpha$). As AGN can also ionize gas and
contribute to the H$\alpha$ emission, it should be noted that the star-forming
and green valley classes identified by this method can be contaminated by AGN.
All 1,051 post-mergers and 10,510 control galaxies in our sample satisfy both
S/N cuts in these two identification methods.

Unlike the Goto (2007) method with strict cuts on both H$\alpha$ and [OII]
emission lines, the Chen et al. (2019) method has loose cuts on H$\alpha$ only
and can include more potential PSBs. Hence, we used the Chen et al. (2019)
selection criteria on the MaNGA resolved spectra to classify each MaNGA spaxel
into star-forming, green valley, retired, PSB, and low S/N. To obtain robust
measurements, we placed a cut on spaxels with a mean $g$-band spectral S/N
$\geq$ 3 per pixel. All bad spaxels (with MaNGA bitmask values $>$ 0) were
masked out and were not used in the resolved PSB analysis. In addition to
these cuts, we also required a S/N $\geq$ 3 for the EW(H$\delta_{A}$) to
obtain a robust measurement. We visually inspected the resolved PSB maps and
the SDSS cutout images to remove spaxels contaminated by background galaxies
or foreground stars.

| Goto (2007) | Chen et al. (2019)
---|---|---
1,051 DR14 Post-mergers | 4 (0.4% $\pm$ 0.2%) | 41 (3.9% $\pm$ 0.6%)
10,510 DR14 Controls | 3 (0.03% $\pm$ 0.02%) | 31 (0.3% $\pm$ 0.1%)
Excess | 13.1 $\pm$ 9.8 | 13.2 $\pm$ 3.1
136 MaNGA Post-mergers | 1 (0.7% $\pm$ 0.7%) | 8 (5.9% $\pm$ 2.0%)
1,360 MaNGA Controls | 1 (0.07% $\pm$ 0.07%) | 4 (0.3% $\pm$ 0.1%)
Excess | 9.9 $\pm$ 14.0 | 20.0 $\pm$ 12.1
Table 1: The numbers and fractions of PSB galaxies classified by different methods with single-fiber spectra in the DR14 samples and the MaNGA observed samples. The Chen et al. (2019) method classifies more PSBs than the Goto (2007) method while their PSB excesses are consistent. The PSB fractions and excesses in the DR14 post-merger (control) sample are consistent with those in the MaNGA observed post-merger (control) sample. | CPSBs | RPSBs | IPSBs | C+R PSBs | C+R+I PSBs
---|---|---|---|---|---
136 Post-mergers | 8 (5.9% $\pm$ 2.0%) | 13 (9.6% $\pm$ 2.5%) | 28 (20.5% $\pm$ 3.5%) | 21 (15.4% $\pm$ 3.1%) | 49 (36.0% $\pm$ 4.1%)
1,360 Controls | 4 (0.3% $\pm$ 0.1%) | 7 (0.5% $\pm$ 0.2%) | 145 (10.7% $\pm$ 0.8%) | 11 (0.8% $\pm$ 0.2%) | 156 (11.5% $\pm$ 0.9%)
Excess | 20.0 $\pm$ 12.1 | 18.6 $\pm$ 8.5 | 1.9 $\pm$ 0.4 | 19.1 $\pm$ 6.9 | 3.1 $\pm$ 0.4
Table 2: The numbers and fractions of PSB galaxies classified by using the
Chen et al. (2019) method and MaNGA resolved spectra. PSB galaxies are
classified into central (C) PSBs, ring-like (R) PSBs and irregular (I) PSBs.
The last two columns show the C+R PSBs only and the total (C+R+I) resolved
PSBs, respectively.

##  4 Results

###  4.1 Post-Starburst Galaxies from SDSS Single-Fiber Spectra

To classify PSB galaxies using the SDSS single-fiber spectra, we obtained the
equivalent widths of H$\alpha$, [OII] and H$\delta_{A}$ from the MPA-JHU
catalog.

Using the traditional Goto (2007) method, we identified four PSB galaxies from
the DR14 post-merger sample and three PSB galaxies from the DR14 control
sample. The PSB fraction is 0.4% $\pm$ 0.2% 222The binomial 1$\sigma$ error is
calculated as $\sqrt{f(1-f)/N}$, where f is the fraction and N is the sample
size. in post-mergers and 0.03% $\pm$ 0.02% in control galaxies, which implies
a PSB excess of a factor of 13.1 $\pm$ 9.8 in the DR14 post-merger sample.

Using the Chen et al. (2019) method with the DR14 samples, we classified the
1,051 post-mergers into 479 (45.6% $\pm$ 1.5%) star-forming, 142 (13.5% $\pm$
1.1%) green valley, 389 (37.0% $\pm$ 1.5%) retired and 41 (3.9% $\pm$ 0.6%)
PSB galaxies (see Figure 3). The 10,510 control galaxies are classified into
2535 (24.1% $\pm$ 0.4%) star-forming, 1869 (17.8% $\pm$ 0.4%) green valley,
6075 (57.8% $\pm$ 0.5%) retired and 31 (0.3% $\pm$ 0.1%) PSB galaxies. It
implies a PSB excess of a factor of 13.2 $\pm$ 3.1 in the DR14 post-merger
sample. The PSB excess of this identification method is consistent with that
in the traditional Goto (2007) method. Table 1 shows the PSB fractions and
excesses in our samples classified by these two different methods. Both
methods suggest that star formation quenching is more common in post-mergers
than non-merging galaxies. A Fisher exact test shows that PSBs and post-
mergers are strongly related with a p-value = $10^{-24}$ for null hypothesis.
However, the SDSS single-fiber spectra only captures the fluxes within the
2.5′′ fiber, which covers mainly the central region of galaxies. Results based
on single-fiber spectra may miss PSB galaxies where star formation quenching
happens not in the center but in the outer disk or outskirts. In the following
section, we investigated the PSB fraction in our samples using the MaNGA
resolved spectra.

Figure 4: Fraction of galaxies classified as resolved PSB in post-mergers
(purple) and control galaxies (black) vs. the used threshold of PSB spaxel
fraction. All three types of PSBs (CPSBs, RPSBs, and IPSBs) in total are shown
in solid curves. The red curve with error bars shows the PSB excess in post-
mergers relative to controls. The dashed curves show only the CPSB + RPSB and
the dotted curves show only the IPSB. There is always a PSB excess in post-
mergers relative to control galaxies at any considered thresholds.

Figure 5: Examples of SDSS cutout images and resolved PSB classification of
CPSBs in post-mergers on the left column and in control galaxies on the right.
The coverage of the MaNGA plate is shown in magenta hexagons on the images.
Spaxels are classified into star-forming (blue), green valley (green), retired
(red), PSB (purple) and low S/N (grey). White color indicates no coverage or
bad pixels.

Figure 6: Examples of SDSS cutout images and resolved PSB classification of
RPSBs in post-mergers on the left column and in control galaxies on the right.
Colors are the same as in Figure 5.

Figure 7: Examples of SDSS cutout images and resolved PSB classification of
IPSBs in post-mergers on the left column and in control galaxies on the right.
Colors are the same as in Figure 5.

###  4.2 Post-Starburst Galaxies from MaNGA IFU Spectra

In this section, we used the MaNGA IFU spectroscopy to present a resolved PSB
identification to our MaNGA observed samples. There are 136 post-mergers and
1,360 control galaxies observed in MaNGA. The single-fiber PSB fractions and
excesses in these MaNGA observed subsamples are shown in Table 1, which are
consistent with those in the DR14 post-merger and control sample.

By using the Chen et al. (2019) PSB selection criteria, we classified all the
spaxels of a galaxy into either one of the following five classes: star-
forming, green valley, retired, post-starburst and low S/N. In this way, we
obtained a spatially resolved PSB classification map for each galaxy. To
identify a galaxy as a resolved PSB galaxy, we require a minimum fraction of
spaxels to be classified as PSB. Figure 4 shows the relation of the fraction
of resolved PSBs classified in post-mergers and controls as a function of the
threshold applied on the PSB spaxel fraction. There is a PSB excess in post-
mergers relative to controls at any considered thresholds (see the red line in
Figure 4). The intense drop of the PSB fractions in control galaxies at
thresholds $<$ 5% could be due to non-PSB galaxies with sporadic PSB spaxels,
which are misclassified as PSB galaxies. Large thresholds may overlook
potential resolved PSB galaxies and also induce larger errors in statistics.
Hence, we choose 5% as our fiducial threshold for the purposes of quoting
statistics. It should be noted that Chen et al. (2019) used a minimum number
of six contiguous spaxels to classify PSB galaxies. However, the PSB galaxies
classified in our work by using the 5% threshold all have more than 16
contiguous spaxels. Hence, we have a stricter selection criterion.

When using a 5% threshold, 49 (36.0% $\pm$ 4.1%) out of the 136 MaNGA observed
post-mergers are classified as resolved PSBs while 156 (11.5% $\pm$ 0.9%) out
of the 1,360 MaNGA observed control galaxies are classified as PSBs. This
implies a resolved PSB excess of a factor of 3.1 $\pm$ 0.4 in post-mergers
compared to controls. A Fisher exact test shows resolved PSBs and post-mergers
are significantly related with a p-value = $10^{-11}$ for the null hypothesis.

We visually inspected the resolved PSB maps and classified the resolved PSB
galaxies into three types: central PSBs (CPSBs), ring-like PSBs (RPSBs) and
irregular PSBs (IPSBs). CPSBs are classified as galaxies showing PSB
contiguous spaxels concentrated in the center or all over the galaxies (see
Figure 5). RPSBs are classified as galaxies showing a full or partial ring of
contiguous PSB spaxels (see Figure 6). Galaxies showing both central PSB
regions and ring PSB regions are still classified as CPSBs. IPSBs are
classified as galaxies showing sporadic PSB regions without a central or ring-
like concentration (see Figure 7). Two of the scientists in our research group
(Li and Nair) did the visual classification and agreed with each other in all
cases. Out of the 49 resolved PSBs in the MaNGA observed post-merger sample,
eight are classified as CPSBs, 13 are classified as RPSBs and 28 are IPSBs.
Out of the 156 resolved PSBs in the MaNGA observed control sample, four are
classified as CPSBs, seven are RPSBs and 145 are IPSBs. Table 2 shows the
fraction and excess of each type of resolved PSBs in the MaNGA observed post-
mergers and controls. There are much more CPSBs and RPSBs in post-mergers than
in controls, while IPSBs are just slightly more common in post-mergers. This
is true regardless of the threshold of PSB spaxel fraction used as can be seen
in Figure 4. However, the fractions of IPSBs in post-mergers and controls
strongly increases at lower PSB spaxel thresholds (dotted lines) and have
roughly the same trend. This suggests that the main difference between the
resolved PSB galaxies fraction in post-mergers and control galaxies is due to
the CPSBs and RPSBs, which are more common in post-mergers and likely due to
true quenching activities. The IPSBs may instead be a signature of sporadic
star formation decay rather than permanent quenching. However, some IPSBs show
large contiguous PSB regions on the edge of the galaxies or in weird
distributions. We cannot rule out that some of these IPSBs are due to true
quenching activities.

Figure 5, 6 and 7 show examples of the SDSS cutout images and resolved PSB
classification maps of CPSBs, RPSBs and IPSBs respectively. PSBs in post-
mergers are shown on the left and PSBs in controls are on the right. Spaxels
are classified into star-forming (blue), green valley (green), retired (red),
PSB (purple) and low S/N (grey). The ring regions start outside of the bulge
and mostly extend to the edge of the MaNGA footprint. For the eight out of 13
post-merger RPSBs and four out of seven control RPSBs observed in the MaNGA
primary sample, their ring regions reach out to $\sim$1.5$R_{e}$, while the
ring regions in the other RPSBs observed in the secondary sample reach out to
$\sim$2.5$R_{e}$. These three subclasses of resolved PSB galaxies are also
over-plotted in the single-fiber classification shown in Figure 3. All the
CPSBs identified by the resolved spectra are also identified as PSBs by the
single-fiber spectra (see the orange crosses in Figure 3). Most of the RPSBs
(11 out of 13) in post-mergers and (6 out of 7) controls are classified as
star-forming in the center by the single-fiber spectra. It suggests quenching
only happens in the outer disk but the galaxies are still actively forming
stars in the center, indicating an outside-in quenching direction. IPSBs span
a range in star-forming, green valley and retired.

Using resolved MaNGA IFU data, the PSB fraction in post-mergers increases to
36.0% compared to 5.9% in single-fiber spectra while for control galaxies the
PSB fraction increases from 0.3% to 11.5%. While the overall excess of PSBs in
post-mergers decreases from $\sim 20$ in single-fiber to $\sim 3$ with
resolved IFU data, the overall PSB fraction increases. When considering CPSBs
and RPSBs only, the PSB fraction in post-mergers is 15.4% $\pm$ 3.1% while the
fraction in control galaxies is 0.8% $\pm$ 0.2% which implies a PSB excess of
a factor of 19.1 $\pm$ 6.9 in post-mergers. Although this is consistent with
the PSB excess found by the single-fiber spectra (see Table 1), the resolved
spectra still reveal a lot of PSBs (specifically the RPSBs) which are missed
by the single-fiber spectra. Thus IFU spectroscopy is crucial to provide a
more complete census of the resolved PSBs population. However, the origin of
IPSBs is unclear.

####  4.2.1 Direction of Star-Formation Quenching

In this section, we discuss the resolved star formation history and the
quenching directions in the resolved PSBs identified in the MaNGA observed
post-mergers and controls. By visually inspecting the resolved PSB
classification maps, we classified our PSB galaxies into six classes with
different quenching properties:

(i) AGN contaminated: AGN can contribute to the H$\alpha$ emission and
contaminate our classification of star-forming or green valley spaxels. For
PSB galaxies showing star-forming/green valley spaxels in the center and
hosting an AGN at the same time, we cannot know whether their H$\alpha$
emission is due to star formation or the AGN. Hence, we cannot fully
understand their resolved star formation history and the quenching direction.
These galaxies will be classified into the ‘AGN contaminated’ class. The
optical AGN identification using both single-fiber diagnostic and resolved
diagnostic is presented in the Appendix. In particular, the resolved AGN
diagnostic is able to reveal the AGN which are obscured by dust and are not
detected in the single-fiber diagnostic (see the Appendix). It should be noted
that PSB galaxies showing PSB/retired spaxels in the center are not affected
by the AGN signals.

(ii) Outside-in quenching: For PSB galaxies showing PSB/retired spaxels in the
outer regions surrounding star-forming/green valley spaxels in the center (and
not hosting an AGN), their resolved star formation history suggests quenching
happens in the outskirts while star formation is still on-going in the center.
This is consistent with an outside-in quenching direction, which is related to
processes happening on the disk (e.g., gas inflows or consumption during
mergers or tidal stripping). PSB galaxies showing retired spaxels in the outer
regions surrounding PSB spaxels in the center also suggests that on-going
quenching is happening in the center while the outskirts have already been
quenched. This is also consistent with an outside-in quenching direction.

(iii) Inside-out quenching: For PSB galaxies showing PSB/retired spaxels in
the center surrounded by star-forming/green valley spaxels in the outer
regions, their resolved star formation history is consistent with an inside-
out quenching direction, which is likely related to processes in the center
(e.g., AGN feedback or central starburst feedback driven by bars/secular
processes). PSB galaxies showing retired spaxels in the center surrounded by
PSB spaxels in the outer regions also suggests that the star formation in the
center has already been quenched but quenching is still happening in the
outskirts. This is also consistent with inside-out quenching. As AGN only
contaminates spaxels with strong EW(H$\alpha$) (star-forming/green valley),
the existence of AGN will not affect our classification of PSB and retired
spaxels in the center.

(iv) Globally quenching: Galaxies showing PSB regions all over are quenching
their star formation globally. There is no clear quenching direction. We
classified these population as ‘globally quenching’.

(v) Quenched overall: Some galaxies show retired spaxels nearly all over the
galaxies with small patches of PSB spaxels randomly distributed. It indicates
that star formation has been fully quenched all over the galaxies. These could
be the end products of either outside-in or inside-out quenching processes. We
classified these galaxies as quenched overall.

(vi) No clear trend: In addition to the five classes mentioned above, other
PSB galaxies either show PSB regions stochastically distributed or having
irregular distribution. These features may be related to secular processes or
sporadic star formation. However, they do not have a clear overall trend like
the other five classes, and hence we classified these galaxies as a separate
population as ‘no clear trend’.

In the 49 PSB galaxies in post-mergers, we identified 11 (22.4% $\pm$ 6.0%) as
being AGN contaminated, 22 (44.9% $\pm$ 7.1%) showing outside-in quenching,
three (6.1% $\pm$ 3.4%) showing inside-out quenching, three (6.1% $\pm$ 3.4%)
showing globally quenching, four (8.2% $\pm$ 3.9%) being quenched overall, and
six (12.2% $\pm$ 4.7%) exhibiting no clear trend. Table 3 summarizes the
fraction of each quenching direction in post-mergers. Specifically, the eight
CPSB galaxies are classified into five outside-in quenching and three globally
quenching. In the 13 RPSB galaxies, we classified three as being AGN
contaminated, nine showing outside-in and one showing inside-out quenching
signatures. For the 28 IPSB galaxies, eight are AGN contaminated, eight
exhibit outside-in quenching, two inside-out, four quenched overall, and six
with no clear trend.

| AGN | Outside-in | Inside-out | Globally quenching | Quenched overall | No clear trend
---|---|---|---|---|---|---
PM | 22.4% $\pm$ 6.0% | 44.9% $\pm$ 7.1% | 6.1% $\pm$ 3.4% |  6.1% $\pm$ 3.4% | 8.2% $\pm$ 3.9% | 12.2% $\pm$ 4.7%
Control | 17.9% $\pm$ 3.1% | 19.2% $\pm$ 3.2% | 25.6% $\pm$ 3.5% | 0.6% $\pm$ 0.6% | 3.2% $\pm$ 1.4% | 33.3% $\pm$ 3.8%
Excess | 1.3 $\pm$ 0.4 | 2.9 $\pm$ 0.6 | 0.2 $\pm$ 0.1 | 10.2 $\pm$ 11.6 | 2.6 $\pm$ 1.7 | 0.4 $\pm$ 0.1
Table 3: The fractions and excesses of different quenching situation in 49 PSBs in post-mergers and 156 PSBs in controls. Outside-in quenching is more common in post-mergers while inside-out quenching and quenching with no clear trend are more common in non-merging controls. Fractions of galaxies which are globally quenching, being quenched overall, or being AGN contaminated are similar in post-mergers and control galaxies considering errors. | AGN | Outside-in | Inside-out | Globally quenching
---|---|---|---|---
PM | 14.3% $\pm$ 7.6% | 66.7% $\pm$ 10.3% | 4.8% $\pm$ 4.7% | 14.3% $\pm$ 7.6%
Control | 9.1% $\pm$ 8.7% | 81.8% $\pm$ 11.6% | – | 9.1% $\pm$ 8.7%
Excess | 1.6 $\pm$ 1.7 | 0.8 $\pm$ 0.2 | – | 1.6 $\pm$ 1.7
Table 4: The fractions and excesses of different quenching situation in CPSBs
+ RPSBs in post-mergers (N = 21) and controls (N = 13). Once we restrict our
PSBs to CPSBs and RPSBs only, the fractions of each quenching direction in
post-mergers is similar to that in controls.

In the 156 PSB non-merging controls, we identified 28 (17.9% $\pm$ 3.1%) being
AGN contaminated, 30 (19.2% $\pm$ 3.2%) exhibiting outside-in quenching, 40
(25.6% $\pm$ 3.5%) showing inside-out quenching, one (0.6% $\pm$ 0.6%) showing
globally quenching, five (3.2% $\pm$ 1.4%) being quenched overall, and 52
(33.3% $\pm$ 3.8%) with no clear trend. Specifically, the four CPSB galaxies
are classified into three outside-in and one globally quenching. The seven
RPSB galaxies are classified as five outside-in quenching, one being AGN
contaminated and one with no clear trend. For the 145 IPSB galaxies, 27 are
AGN contaminated, 22 exhibit outside-in quenching, 40 inside-out, five
quenched overall, and 51 with no clear trend. The fractions of these six
quenching classes and the factors of excesses in post-mergers relative to
controls are summarized in Table 3.

As seen in Table 3, outside-in quenching is more common in post-mergers than
in control galaxies, with an excess of a factor of 2.9 $\pm$ 0.6. Galaxies
which are globally quenching or have been fully quenched all over the entire
galaxy have similar fractions in post-mergers and control galaxies considering
the errors. Galaxies with inside-out quenching or quenching without a clear
trend tend to be more common in non-merging systems than in post-mergers. In
addition, outside-in quenching is $\sim$7 times more common than inside-out
quenching in post-mergers while inside-out quenching is slightly more common
in non-merging controls, suggesting mergers leading to outside-in quenching.
This indicates that the mechanisms driving quenching in mergers are
preferentially operating in the disk rather than in the nuclear center. It
could be due to inflows or turbulence redistributing the gas. Gas consumption
of trigger starbursts and stellar feedback in the disk can also play a role.
On the contrary, merger-driven AGN feedback is not likely the main mechanism
driving quenching in mergers. However, we cannot rule out that AGN feedback
may have long-term effects on maintaining quenching rather than in short
merger timescales as suggested in Davies et al. (2022).

Figure 8: Fraction of resolved PSB galaxies as a function of stellar mass, g -
r color and environment density $\rho$ in MaNGA observed post-merger (solid
purple) and control galaxies (black dashed). The PSB galaxies include all the
CPSBs + RPSBs + IPSBs classified in MaNGA resolved spectra. The numbers of
galaxies in each bin are shown next to the error bars. Post-mergers have a
higher fraction of PSBs than control galaxies generally. In addition, PSB
galaxies tend to appear more in galaxies with lower stellar mass, intermediate
color and lower environment density.

Once we restrict our samples to only the CPSBs and RPSBs (see Table 4), there
is no difference found in each of the quenching directions in post-mergers
compared to controls. However, we found that outside-in quenching is still
much more common than inside-out quenching in post-mergers. In fact, the CPSBs
and RPSBs mainly show outside-in quenching regardless of post-mergers or
controls. These results suggest that outside-in quenching is playing the main
role in triggering CPSBs and RPSBs in both post-mergers and control galaxies.

###  4.3 Resolved PSB Frequency vs Galaxy Properties

To investigate whether PSB galaxies have a dependence on galaxy properties, in
Figure 8 we compared the fractions of resolved PSB galaxies as a function of
stellar mass (left panel), g - r color (middle panel), and environment density
(right panel) in the MaNGA observed post-merger and control samples. The
environment densities of our galaxies were obtained from Baldry et al. (2006),
which are defined as: $\rho=N/(\pi d^{2})$, where $d$ is the projected co-
moving distance to the N-th nearest neighbour within a specific redshift range
(with details in Baldry et al., 2006). It should be noted that 100/136 (73.5%)
post-mergers and 519/1,360 (38.2%) control galaxies in the MaNGA observed sub-
sample have environment density measurements. However, we do not have the same
number of control galaxies for each post-merger in this sub-sample, which may
induce some bias.

We found the fraction of PSB galaxies decreases with increasing stellar mass
in post-mergers. However, the fraction of PSB galaxies is independent of
stellar mass in controls given our measurement uncertainties. It indicates
that quenching in mergers is more common in low-mass systems. This is
consistent with Rowlands et al. (2018a) that there are more low-mass systems
in PSB galaxies. The middle panel shows that PSB galaxies tend to exhibit
intermediate colors (g - r $\sim$ 0.5 – 0.8) for both post-mergers and control
galaxies. This is consistent with the expectation that the PSB phase is more
likely to be found in the transition phase (or ‘green valley’) between star-
forming and quiescent. Redder galaxies in our sample tend to have lower
fractions of PSBs as they are more likely to be quenched systems. From the
right panel, the PSB fractions in post-merger galaxies tends to be higher at
lower environment densities. PSB fractions in control galaxies show no clear
dependence on environment density. However, it should be noted that our sample
does not probe large clusters. In sum, star formation quenching in post-
mergers tend to appear more frequently in lower mass, intermediate color, and
lower environment density. Quenching in control galaxies is more frequently
found in blue-cloud or green-valley galaxies while there is no trend found in
stellar mass or environment density. We also investigated the distribution of
stellar mass, color, and environment density of the three different types of
PSB (CPSB, RPSB, IPSB) galaxies. However, no clear trend is found in those
distributions due to the small sample size.

###  4.4 HI Gas Content in Resolved PSB Galaxies

As seen in Figure 8, PSB galaxies are more common in systems with lower mass
and intermediate colors. As both color and stellar mass have a tight relation
to the gas content in galaxies, we expect to see a difference between the gas
content in PSBs compared to non-PSBs. We obtained the HI gas mass from the
MaNGA-HI catalog and calculated the HI gas fraction, which is defined as
$f_{gas}$ = MHI/M∗.

Figure 9: The mean HI gas fraction in post-mergers (red circle) and control
galaxies (black square). For the 37 post-mergers and 330 controls with HI
detections, we calculated the mean $f_{gas}$ of the sample with standard
1$\sigma$ error. For the 42 non-detections in post-mergers and 563 in
controls, we obtained the mean $f_{gas}$ and error by stacking their HI
spectra. The mean $f_{gas}$ and error of the entire post-merger or control
sample was obtained by stacking both detections and non-detections.

Figure 10: The stacked HI spectra of non-detections on the upper panel and of
detections + non-detections on the lower panel for post-mergers on the left
and controls on the right. The spectra are centered at a velocity grid ranging
between +/- 1000 km $s^{-1}$ and are shown in gas fraction unit
($M_{HI}/M_{*}$) converted from HI flux using equation 1.

There are 37 post-mergers and 330 control galaxies with HI detections from the
MaNGA-HI DR3 catalog. The mean $f_{gas}$ with 1$\sigma$ error is 0.372 $\pm$
0.070 in post-mergers and 0.344 $\pm$ 0.021 in controls. For the 42 post-
mergers and 563 controls with HI non-detections, we obtained their mean
$f_{gas}$ through stacking analysis following Roberts-Borsani et al. (2020).
To summarize our stacking procedure: (1) We scaled all the HI spectra to the
rest frame and converted them from flux density units to gas fraction units by
using the following equation:

$M_{HI}/M_{*}=2.356\times 10^{5}(d[Mpc])^{2}\frac{flux[Jy]}{M_{*}}$ (1)

where $d$ is the distance to the galaxy calculated using $d=cz/H_{0}$, where
$c$ is the speed of light, $z$ is the redshift of the galaxy and $H_{0}=70$km
s-1Mpc-1. Adopting the distance factor eliminates the bias of HI flux towards
nearer galaxies. (2) We then recentered all the spectra onto a new velocity
grid ranging between $\pm$ 1000 km s-1 with interpolation at 5 km s-1
intervals and added them to create the stack. (3) Lastly, we averaged the
stack to obtain the final average stacked spectra. We used a polynomial model
to fit the baseline and integrated the baseline-subtracted profile using a
Gaussian fit to obtain the mean $f_{gas}$ of the stack.

The mean stacked $f_{gas}$ of the non-detections is 0.042 $\pm$ 0.007 in post-
mergers and 0.048 $\pm$ 0.005 in controls. To obtain the mean $f_{gas}$ for
the entire post-merger sample and the control sample, we stacked both the
detections and non-detections using the same procedure above. Figure 10 shows
the stacked HI spectra of non-detections on the upper panel and of detection +
non-detections on the lower panel for post-mergers on the left and control
galaxies on the right. Stacking both detections and non-detections, we
obtained a mean stacked total $f_{gas}$ of 0.216 $\pm$ 0.010 in post-mergers
and 0.223 $\pm$ 0.004 in controls. As can be seen in Figure 9, there is no
difference between the mean $f_{gas}$ in post-mergers and controls within
1$\sigma$ error for either the detected or stacked sub-samples.

It should be noted that Ellison et al. (2018) studied a sample of 98 post-
mergers and found a median HI gas enhancement of $\sim$ 0.51 dex in post-
mergers relative to controls matched at stellar masses, suggesting that gas
exhaustion is not the cause of star formation quenching in mergers. Our MaNGA
sample of 79 post-mergers is comparable in size to Ellison et al. (2018),
although our sample has more non-detections. A control sample without being
matched at redshift may cause a difference in the gas fraction enhancement. In
addition, we used a different method to compare the $f_{gas}$, which may also
affect the results. Sample biases such as a dependence on stellar mass,
redshift, environment cannot be ruled out with the relatively small sample. In
a future paper we will investigate the dependence of gas fraction offset with
a much larger sample of post-mergers and non-interacting controls.

In Figure 11, we investigate the dependence of mean gas fraction on the
presence/absence of resolved PSB (CPSBs, RPSBs, IPSBs) features in post-
mergers and controls. In post-mergers, there are 15 PSBs and 22 non-PSBs with
HI detections. The mean $f_{gas}$ is 0.249 $\pm$ 0.075 in PSBs and 0.454 $\pm$
0.105 in non-PSBs. For the non-detections, the mean $f_{gas}$ of the 13 PSBs
is 0.053 $\pm$ 0.018 while the mean $f_{gas}$ of the 29 non-PSBs is 0.036
$\pm$ 0.006. By stacking both detections and non-detections, we obtained a
mean stacked total $f_{gas}$ of 0.187 $\pm$ 0.012 in PSBs and 0.233 $\pm$
0.014 in non-PSBs. Figure 11 upper panel shows the mean $f_{gas}$ in PSBs and
non-PSBs in post-mergers. The detections show a slight gas deficit in PSBs
relative to non-PSBs at a 2$\sigma$ level. The stacked non-detections show no
difference in the mean $f_{gas}$ in PSBs compared to non-PSBs. Considering
both detections and non-detections, there is a small deficit of the gas
fraction of 5% in PSBs compared to non-PSBs in post-mergers. This suggests gas
consumption/expulsion may play a role in triggering PSB signatures in post-
mergers.

For control galaxies, there are 55 PSBs and 275 non-PSBs with HI detections.
The mean $f_{gas}$ is 0.275 $\pm$ 0.034 in PSBs and 0.358 $\pm$ 0.024 in non-
PSBs. For non-detections, the mean $f_{gas}$ is 0.074 $\pm$ 0.014 in the 50
PSBs and 0.045 $\pm$ 0.005 in the 513 non-PSBs. Stacking both detections and
non-detections, the mean $f_{gas}$ is 0.229 $\pm$ 0.008 in PSBs and 0.223
$\pm$ 0.004 in non-PSBs. Similar to post-mergers, the detections show a slight
gas deficit in PSBs relative to non-PSBs. The stacked non-detections show a
slight gas enhancement in control PSBs. However, accounting for both
detections and non-detections, there is no difference between the $f_{gas}$ in
PSBs and non-PSBs in controls. This suggests gas consumption/expulsion is not
playing a role in triggering quenching in non-merging galaxies.

Figure 11: Upper panel: The mean HI gas fraction in PSBs (filled circles) and
non-PSBs (empty circles) in post-mergers. There are 15 PSBs and 22 non-PSBs
with HI detections while 13 PSBs and 29 non-PSBs have HI non-detections. There
is no detection in the stack of the 29 non-detections. Hence, the upper limit
is shown. PSBs tend to have less gas than non-PSBs in post-mergers. Lower
panel: the mean HI gas fraction in PSBs (filled squares) and non-PSBs (empty
squares) in control galaxies. There are 55 PSBs and 275 non-PSBs with HI
detections while 50 PSBs and 513 non-PSBs have HI non-detections. There is no
difference in the gas fraction of PSBs compared to non-PSBs in controls.

To summarize, we found no difference in the gas fraction between post-mergers
and controls. However, we found post-mergers exhibiting PSB features have an
HI gas deficit compared to post-mergers not hosting PSB signatures. Control
galaxies show no such trend. This is consistent with gas consumption/star-
formation driven expulsion as being the cause of the PSB signatures in
mergers. It should be noted that considering only CPSBs + RPSBs as PSB
galaxies does not change our results.

##  5 Discussion

###  5.1 Post-Starburst Excess in Post-Merger Galaxies

Here we compare the PSB excesses found in our post-merger sample to a similar
work by Ellison et al. (2022), which investigated the PSB fraction in a sample
(E22 sample, hereafter) of 508 post-merger galaxies with log M∗/M${}_{\odot}>$
10 and z $<$ 0.25. By using the Goto (2007) traditional ‘E+A’ PSB
classification method, they found 6% $\pm$ 1% of post-mergers and 0.1% $\pm$
0.02% of non-merging controls classified as PSBs, with a PSB excess of a
factor of 60 $\pm$ 16 in post-mergers. In addition, a Principal Component
Analysis (PCA) analysis was also presented in their work to classify PSBs,
which results in a PSB excess of a factor of 33 $\pm$ 4 in post-mergers.

The PSB excesses in the E22 sample are higher than those of our post-merger
samples with single-fiber data (9.9$~{}\sim~{}$20). One of the reasons for
these differences can be the different stellar mass and redshift range of our
sample. The post-merger sample in our work spans a wider range of stellar mass
(9 $<$ log M∗/M${}_{\odot}<$ 12) and a much narrower range of redshift (0.02
$<$ z $<$ 0.06) compared to the E22 sample. If we restrict our DR14 post-
merger sample to the same stellar mass range as the E22 sample, we will obtain
914 post-mergers. When using the Goto (2007) traditional ‘E+A’ PSB
classification method, the number statistics are too small to draw a robust
conclusion. We also checked the PCA PSB fractions in our mass-restricted DR14
post-merger sample by using the catalog presented in (Wild et al., 2007). We
found a PCA PSB excess of a factor of 22.8 $\pm$ 5.4 in our post-mergers,
which is slightly lower than the PCA PSB fraction of the E22 sample (33 $\pm$
4). However, when using the Chen et al. (2019) single-fiber classification, we
obtained a PSB excess of a factor of 23.1 $\pm$ 6.9, which is consistent with
the PCA PSB excess of our post-merger sample or the E22 sample. If we restrict
the E22 sample to the same redshift and stellar mass range as our DR14 post-
merger sample then the sample size is too small to draw any robust conclusion.

Our work here shows that single-fiber diagnostics can only identify CPSBs and
strongly underestimates the total number of PSB galaxies. As seen in Table 1
and 2, the resolved PSB diagnostic based on MaNGA IFU spectra reveals a large
number of RPSBs and IPSBs showing quenching signatures in the outer disk (up
to $\sim$2.5 $R_{e}$) rather than only in the center (CPSBs), which are missed
by the single-fiber diagnostics. Specifically, $\sim$62% of the resolved PSBs
in post-mergers and $\sim$64% in controls are missed by the single-fiber
selection method when considering both CPSBs and RPSBs. Including the IPSBs,
the single-fiber PSB method will miss $\sim$84% of the resolved PSBs in post-
mergers and $\sim$97% in controls. However, it should be noted that some of
the IPSBs showing sporadic PSB regions could be due to stochastic star
formation decay rather than permanent quenching.

Figure 12: Distribution of CPSBs (red crosses), RPSBs (blue circles) and IPSBs
(green triangles) on the SFR vs. stellar mass diagram of post-mergers and
controls. The contour represents the parent sample with the outer most contour
including 99% of the sample. The two division lines are drawn by eye based on
the contour, which divide galaxies into active star-forming, transition and
quiescence. RPSBs are mostly located in the active star-forming region. CPSBs
are located along the transition region and the IPSBs span a range from star-
forming to quiescent.

###  5.2 Quenching Mechanisms

As seen in Table 3, the fraction of outside-in quenching in resolved PSB post-
mergers is 44.9% $\pm$ 7.1%, about 7 times higher than the fraction of inside-
out quenching (6.1% $\pm$ 3.4%) while inside-out quenching is slightly more
common in control galaxies than outside-in. This suggests that the star
formation quenching in post-mergers is more likely to be related to processes
happening in the disk (i.e., gas inflows/consumption during mergers) rather
than those happening in the center (merger-induced AGN/star formation). The HI
gas deficit seen in post-merger PSBs relative to non-PSBs (see Figure 11)
suggests that gas consumption from triggered star formation in the disk and
the related stellar feedback may be the cause of PSBs and star formation
quenching.

Although our results are quite suggestive, previous studies (Wong & Blitz,
2002; Kennicutt et al., 2007; Leroy et al., 2008; Bigiel et al., 2008) have
found that star formation rates are better correlated with molecular gas but
poorly related to atomic gas. The HI gas deficit may suggest either an
efficient conversion of atomic Hydrogen to molecular Hydrogen (Wang et al.,
2020) and hence no total gas deficit or indicate a true gas deficit. Resolved
distributions of dense molecular gas will be needed to truly constrain the
mechanisms in play.

Figure 12 shows the distribution of CPSBs, RPSBs and IPSBs on the SFR vs.
stellar mass parameter space. In post-mergers, we find nearly all the RPSBs
are located in the actively star-forming region while CPSBs are mostly located
along the transition region. Although the IPSBs span a range from star-forming
to quiescent, $\sim$75% of the outside-in quenching IPSBs are located in the
star-forming region. All of the inside-out or quenched overall IPSBs are
located in the quiescent region. These results are consistent with Chen et al.
(2019), which found RPSBs are mainly located on the star forming main sequence
while CPSBs are mainly located in the green valley. Chen et al. (2019) also
found that CPSBs and RPSBs are mainly showing outside-in quenching scenarios.
This outside-in quenching scenario can be possibly due to strong gas inflows
during mergers redistributing gas from the outer regions to the center and
triggering strong central starbursts. While the post-starburst in the outer
regions fades and becomes quiescent, the central starburst is quenched due to
gas consumption. In addition, a merger-driven starburst progressively moving
inwards followed by a truncation of star formation from the outside-in can
also cause this quenching scenario.

In the resolved control PSBs (CPSBs + RPSBs + IPSBs), the fraction of inside-
out quenching (25.6% $\pm$ 3.5%) is similar to the fraction of outside-in
quenching (19.2% $\pm$ 3.2%). It seems neither mechanism is playing a dominant
role in quenching star formation in non-interacting control galaxies. As seen
in Figure 12 right panel, six out of the seven RPSBs in controls are located
in the star-forming region while the other one is in the quiescent region.
Three out of the four CPSBs are located along or close to the transition
region while the other one does not have valid measurements on SFR. The
distribution of RPSBs and CPSBs in controls is similar to that in post-
mergers. IPSBs with outside-in quenching are located in the star-forming
region while those showing inside-out or quenched overall are in the quiescent
region. This is also similar to the IPSBs in post-mergers.

####  5.2.1 The Role of AGN

AGN have been found in PSB galaxies by many previous studies (Goto, 2006;
Tremonti et al., 2007; Melnick et al., 2015; Baron et al., 2017; Baron et al.,
2018) and are believed to play a role in quenching star formation by feedback
processes. However, other studies suggest that the AGN-driven outflows may not
be able to remove a significant amount of gas to quench star formation
globally (Yesuf et al., 2017), or AGN are just along for the ride rather than
the primary cause of PSB signatures (Yesuf et al., 2014; Maltby et al., 2019;
Yesuf & Ho, 2020; Lanz et al., 2022; Ellison et al., 2022).

As discussed in the appendix, we identified AGN in our samples using both
single-fiber and resolved optical diagnostics. We found that the optical AGN
fraction in PSBs is a factor of $\sim$ two times higher than that in non-PSBs
in our samples. This is true in both post-mergers and control galaxies,
suggesting a possible relation between AGN and PSBs. However, the existence of
AGN is not enough to correlate the cause of PSBs and star formation quenching
to AGN feedback processes. This is because the mechanisms (e.g., galaxy
mergers) which trigger the PSBs and star formation quenching could be the same
mechanisms triggering the AGN (Ellison et al., 2022). Hence, the AGN could be
the by-product of the real triggering mechanism of PSB. As seen in Table 3, we
found a consistent fraction of resolved PSBs hosting an AGN in post-mergers
($\sim$22%) and in control galaxies ($\sim$18%), which prevents us from
determining their resolved quenching direction. This suggests merger-driven
AGN is not the main quenching mechanism in post-mergers. We also found that
the AGN fraction in different types of PSB galaxies (CPSBs, RPSBs and IPSBs)
are similar to each other in post-mergers, suggesting AGN are not the cause of
the variety of resolved PSB signatures in mergers.

AGN can be identified in multi-wavebands, and different AGN identification
methods may reveal very different populations (see Padovani et al., 2017 for a
review). Using the optical diagnostic alone cannot provide a complete census
of the AGN population. A recent paper by Li et al. (2023) studies the AGN
fraction using multi-waveband diagnostics in the same post-merger sample as in
this work. They showed that X-ray diagnostics are crucial in identifying the
majority of AGN in our post-merger sample. However, only 12 out of the 136
MaNGA observed post-mergers have deep X-ray observations. Hence, we cannot
completely rule out a role for AGN in star formation quenching in post-
mergers. In addition, our systems are visually identified post-mergers. Merger
remnants with strong quasars would not have been visually identified and are
therefore not included in this analysis.

Davies et al. (2022) suggests that AGN feedback can cause quenching over a
long timescale of a few Gyrs by expelling gas from the circumgalactic medium
and halting further gas supply. This is similar to strangulation (Peng et al.,
2015; Trussler et al., 2020), which is another long-term quenching mechanism.
However, the typical timescale of PSB signatures is about a Gyr after the
starburst, which is shorter than the timescales for strangulation (3$\sim$4
Gyr). For galaxies with gradual quenching over long timescales, their spectra
are not expected to exhibit both strong Balmer absorption and no emission
lines (i.e., PSB).

In addition to galaxy mergers and AGN, previous studies also found that ram
pressure stripping plays a role in triggering PSB galaxies in dense clusters
(Poggianti et al., 2009; Dressler et al., 2013; Paccagnella et al., 2017;
Socolovsky et al., 2018; Paccagnella et al., 2019; Wilkinson et al., 2021;
Werle et al., 2022). However, the post-merger galaxies in our sample are
mainly in the field rather than in high density environments and hence ram
pressure is unlikely to be playing a role in triggering PSBs in our sample.

##  6 Summary

In this paper, we investigated the role of galaxy mergers in triggering star
formation quenching using a sample of 1,051 post-merger galaxies and 10,510
non-merging control galaxies. We classified PSB galaxies using both single-
fiber spectra and the MaNGA resolved spectra. We presented visual
classifications of the resolved star formation quenching history of our PSB
galaxies and further classified our galaxies into different quenching
directions. Here we summarize our results:

(i) Using the SDSS single-fiber spectra with different PSB selection methods,
we found a consistent (with errors) PSB excess of a factor of $\sim$10-20 in
post-mergers relative to non-merging controls. This suggests galaxy mergers
play an important role in triggering PSB signatures and star formation
quenching. The single-fiber PSB fractions and excesses are summarized in Table
1.

(ii) Using the MaNGA resolved spectra, we identified three morphological
classes of resolved PSBs: central (C)PSBs, ring-like (R)PSBs, and irregular
(I)PSBs. When only accounting for CPSBs + RPSBs, we found a PSBs excess of a
factor of $\sim$19 in post-mergers relative to controls, which is consistent
with the single-fiber PSB excess. However, we found that $\sim$62% of the
resolved PSBs (CPSBs + RPSBs) in post-mergers and $\sim$64% in controls were
missed by the single-fiber PSB selection method. Specifically, the resolved
IFU spectra are crucial to recover the RPSBs while the SDSS single-fiber
spectra can only identify the CPSBs in our sample. Including the IPSBs, we
obtained a PSB excess of a factor of $\sim$3 in post-mergers relative to
controls. Some IPSBs may be due to sporadic star formation decay rather than
permanent quenching. The resolved PSB fractions and excesses are shown in
Table 2.

(iii) In post-merger galaxies, outside-in quenching is $\sim$7 times more
common than inside-out quenching. However, inside-out quenching is slightly
more common than outside-in quenching in non-merging controls (see Table 3).
This suggests that mergers preferentially lead to quenching from the disk
rather than from the nuclear center, suggesting merger-driven AGN feedback is
not the main quenching mechanism. Gas inflows, gas consumption, and stellar
feedback driven in mergers may play a more important role.

(iv) In post-merger galaxies, the mean HI gas fraction in PSBs is lower than
that in non-PSBs (see Figure 11), suggesting gas consumption/SF driven
expulsion is the likely cause of PSB and star formation quenching in mergers
as opposed to gas inflows alone.

## Acknowledgement

We thank the referee for the comments that have improved the quality of this
work. Authors Li and Nair would like to acknowledge support by the National
Science Foundation under Grant No. 1616547. The NSF grant enabled the visual
classification of the parent sample of galaxies used in this paper. Any
opinions, findings, and conclusions or recommendations expressed in this
material are those of the author(s) and do not necessarily reflect the views
of the National Science Foundation.

The Green Bank Observatory is a facility of the National Science Foundation
operated under cooperative agreement by Associated Universities, Inc. We would
like to acknowledge Zachary Tu from the University of Washington, who provided
the stacking code used in our HI analysis and provides assistance with the HI
related problems.

This publication makes use of data from the Sloan Digital Sky Survey. Funding
for the Sloan Digital Sky Survey has been provided by the Alfred P. Sloan
Foundation, the U.S. Department of Energy Office of Science, and the
Participating Institutions. SDSS acknowledges support and resources from the
Center for High Performance Computing at the University of Utah. The SDSS
website 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, Observatário Nacional / MCTI, The Ohio State
University, Pennsylvania State University, Shanghai Astronomical Observatory,
United Kingdom Participation Group, Universidad Nacional Autónoma de México,
University of Arizona, University of Colorado Boulder, University of Oxford,
University of Portsmouth, University of Utah, University of Virginia,
University of Washington, University of Wisconsin, Vanderbilt University, and
Yale University.

## Data Availability

Part of the parent catalog used to construct the samples in this paper will be
available in Nair (2023; in preparation). The MaNGA observed post-merger
sample used in this paper is publicly available as an online supplement to its
original journal publication. The MaNGA MPL-11 data products can be generated
by the public using the raw data at this link with DRP v3.1.1 and DAP v3.1.0.
The MaNGA-HI DR3 catalog used in this paper can be found at this link. Stellar
masses, SFRs and measurements of line equivalent widths are all publicly from
the MPA-JHU DR8 catalog at this link. The environment density used in this
paper are accessible at this link.

##  Appendix A AGN Identification

As mentioned in Section 4.2, the optical emission due to AGN will affect our
classification of the resolved star formation history. Here we investigate the
optical AGN fraction in our MaNGA observed samples using both single-fiber and
resolved AGN diagnostics.

In the single-fiber diagnostic, we used the emission line flux ratios of [O
III]$\lambda 5007$/H$\beta$ vs [N II]$\lambda 6584$/H$\alpha$ measured from
the SDSS single-fiber spectra to construct a NII-BPT diagram (Baldwin et al.,
1981) with a cut on EW(H$\alpha$) $\leq$ -3Å (Cid Fernandes et al., 2011). The
fluxes of the four emission lines are obtained from the MPA-JHU catalog. We
placed a S/N $\geq$ 3 cut on the four emission lines to select galaxies with
significant flux measurements. Figure 13 shows the single-fiber NII-BPT
diagram of post-mergers and control galaxies. Galaxies with emission line S/N
$<$ 3 are not shown on the diagrams. Out of the 136 post-mergers, the single-
fiber diagnostic classifies 13 Seyferts, 31 LINERs, 40 composites, 21 star-
forming and 31 low S/N. Galaxies with emission line ratios above the Kewley et
al. (2001) curve (Seyferts + LINERs) and with EW(H$\alpha$) $\leq$ -3Å are
classified as optical AGN. There are 16 optical AGN (eight Seyferts + eight
LINERs) identified in post-mergers, implying an optical AGN fraction of 11.8%
$\pm$ 2.8%. In the 1,360 control galaxies, there are 84 Seyferts, 351 LINERs,
423 composites, 155 star-forming and 347 low S/N classified by the single-
fiber diagnostic. Applying the equivalent width cut on EW(H$\alpha$), there
are 108 optical AGN (58 Seyferts + 50 LINERs) identified in controls. This is
an AGN fraction of 7.9% $\pm$ 0.7%. The AGN excess in post-mergers relative to
controls is a factor of 1.5 $\pm$ 0.4.

Figure 13: The single-fiber BPT diagrams of post-mergers and control galaxies
with S/N $\geq$ 3 for all four lines. The grey points are general galaxies
from the parent sample. Galaxies are classified into Seyfert (red), LINERs
(orange), composites (green) and star-forming (blue). Seyferts and LINERs are
distinguished using the Schawinski et al. (2007) empirical (dashed) line.
Objects between the Stasinska et al. (2006) (dotted) and the Kewley et al.
(2001) (solid) curves are composites and those below the Stasinska et al.
(2006) curve are star-forming. Galaxies above the Kewley et al. (2001) solid
curve (Seyferts + LINERs) with EW(H$\alpha$) $\leq$ -3Åare classified as
optical AGN.

In the resolved diagnostic, we adopted and modified the criteria from
Wylezalek et al. (2018) by constructing two resolved BPT diagrams using the
emission line flux ratios of [O III]/H$\beta$ vs [N II]/H$\alpha$ and [O
III]/H$\beta$ vs [S II]/H$\alpha$ (NII-BPT, SII-BPT hereafter). The equivalent
widths are extracted from the MaNGA “MAPS-SPX-MILESHC-MASTARSSP” data analysis
pipeline. To obtain robust measurements, we only consider spaxels with a mean
$g$-band spectral S/N $\geq$ 3 per pixel. We also required a S/N $\geq$ 3 cut
on the fluxes of these five emission lines. As seen in Figure 14, spaxels are
classified into Seyferts (red), LINERs (orange), composites (green) and star-
forming (blue) by the Schawinski et al. (2007) (dashed) empirical relation,
the Kewley et al. (2001) (solid curved), Kewley et al. (2006) (solid,
straight) and the Stasinska et al. (2006) (dotted) theoretical relations.
Spaxels with low S/N are shown in grey on the resolved maps and would not be
shown on the BPT diagrams.

We applied multiple cuts on the following parameters. Firstly, we required an
AGN candidate to have at least 5% of its spaxels classified as Seyferts or
LINERs either by NII-BPT or SII-BPT. Secondly, as EW(H$\alpha$) is able to
distinguish ionization mechanisms from real AGN and star formation (Cid
Fernandes et al., 2011), we required an AGN candidate to have a mean
EW(H$\alpha$) of all the Seyfert + LINER spaxels to be less than -3Å. Thirdly,
diffuse ionized gas can affect the emission line ratio measurement
particularly in the regions where the H$\alpha$ surface brightness is lower
than 1037 erg s-1 kpc-2 (Zhang et al., 2017). Following Wylezalek et al.
(2018), we required an AGN candidate to have a mean surface brightness
SB(H$\alpha$) of all Seyfert + LINER spaxels to be greater than 1037.5 erg s-1
kpc-2. Lastly, Seyfert and LINER spaxels with emission line ratios close to
the Kewley et al. (2001) demarcation curve could be classified into composite
(on NII-BPT) or star-forming (on SII-BPT) considering measurement errors.
Composites on NII-BPT could possibly be AGN. However, star-forming spaxels
misclassified as Seyferts or LINERs on SII-BPT should be excluded from real
Seyfert and LINER spaxels. Following Wylezalek et al. (2018), we calculated
the distance (dBPT) from each Seyfert and LINER spaxel to the Kewley et al.
(2001) demarcation line on the SII-BPT diagram. We placed a cut on the mean
dBPT of all Seyferts + LINERs spaxels and required an AGN candidate to have a
mean dBPT $\geq$ 0.15 away from the star-forming demarcation line on the SII-
BPT. It should be noted that our cut on dBPT is different from Wylezalek et
al. (2018).

Here we summarize our AGN resolved classification methods. An AGN is
classified if:

  * •

$f_{SL,NII}\geq 5\%$

  * •

mean EW(H$\alpha)_{SL,NII}\leq-3$Å

  * •

mean SB(H$\alpha)_{SL,NII}\geq 10^{37.5}$ erg s-1 kpc-2

OR

  * •

$f_{SL,SII}\geq 5\%$

  * •

mean EW(H$\alpha)_{SL,SII}\leq-3$Å

  * •

mean SB(H$\alpha)_{SL,SII}\geq 10^{37.5}$ erg s-1 kpc-2

  * •

mean d${}_{BPT,SL,SII}\geq 0.15$

Using these resolved classification diagnostic, we identified 17 (12.5% $\pm$
2.8%) AGN out of the 136 post-mergers and 74 (5.4% $\pm$ 0.6%) AGN out of the
1,360 control galaxies. It implies an AGN excess of a factor of 2.3 $\pm$ 0.6
in post-mergers.

Figure 14: Examples of resolved NII-BPT and SII-BPT diagrams for two post-
mergers (top two panels) and two control galaxies (bottom two panels). Spaxels
are classified into Seyfert (red), LINERs (orange), composites (green) and
star-forming (blue). These four galaxies show clear resolved Seyfert signals
but are classified as composites or low S/N by the single-fiber BPT
diagnostic. Resolved diagnostic can reveal these optical AGN overlooked by the
single-fiber spectra.

Unlike the cuts placed on the H$\alpha$ surface brightness (Zhang et al.,
2017) and EW(H$\alpha$) (Cid Fernandes et al., 2011) which are well supported
by observations, the cuts we placed on the spaxel fractions and the dBPT are
more empirical. Hence, we investigated how the AGN fractions and excesses are
affected by choosing different thresholds. Figure 15 shows the resolved AGN
fractions and excesses in post-mergers relative to controls when using
different thresholds of Seyferts + LINERs spaxel fractions ($f_{SL}$) and the
distance away from the star-forming demarcation line (dBPT). The top panel in
Figure 15 shows the AGN identified in the NII-BPT and SII-BPT respectively.
There is an AGN excess of a factor of 2 – 8 in post-mergers relative to
control galaxies at all considered thresholds. Choosing a higher threshold on
the Seyferts + LINERs spaxel fraction will not qualitatively affect our
results. In the bottom panel, the AGN fraction declines sharply at dBPT $<$
0.15 and becomes smoother at dBPT $\geq$ 0.15. It suggests a small dBPT may
not be robust enough to distinguish the contamination of star-forming spaxels
in Seyfert/LINER spaxels and will include misclassified AGN in both post-
merger and control samples. However, using a larger dBPT ($>$ 0.3) value will
shrink the sample size and cannot lead to a robust conclusion of our results.
In addition, there is an AGN excess in post-mergers relative to controls in
all considered thresholds. In summary, using a higher threshold of spaxel
fraction or dBPT does not affect our results qualitatively. Although it will
slightly bring down the AGN fraction in both post-mergers and control galaxies
and induce larger errors, the AGN excesses remain consistent considering
errors.

In summary, the AGN fraction in post-mergers is 11.8% $\pm$ 2.8% based on
single-fiber spectroscopy and 7.9% $\pm$ 0.7% for control galaxies. With
resolved IFU data, we obtain and AGN fraction of 12.5% $\pm$ 2.8% for post-
mergers and 5.4% $\pm$ 0.6% for control galaxies. Considering AGN identified
in either single-fiber BPT or resolved BPT diagnostics, the AGN fraction is
18.4% $\pm$ 3.3% in post-mergers and 10.0% $\pm$ 0.8% in controls. It implies
an AGN excess of a factor of 1.8 $\pm$ 0.4 in post-mergers relative to
controls.

Figure 15: AGN fraction in post-mergers (red dashed) and controls (black
dotted) and AGN excess (red solid) with 1$\sigma$ errors as a function of
Seyfert + LINER spaxel fraction ($f_{SL,NII}$, $f_{SL,SII}$) thresholds and
BPT distance (dBPT) thresholds. When investigating each threshold, the other
cuts were set to the default values of our classification scheme.

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