# Evidence that Shocks Destroy Small PAH Molecules in Low-luminosity Active
Galactic Nuclei

Lulu Zhang Kavli Institute for Astronomy and Astrophysics, Peking University,
Beijing 100871, China<EMAIL_ADDRESS>Department of Astronomy, School
of Physics, Peking University, Beijing 100871, China  Luis C. Ho Kavli
Institute for Astronomy and Astrophysics, Peking University, Beijing 100871,
China<EMAIL_ADDRESS>Department of Astronomy, School of Physics,
Peking University, Beijing 100871, China  Aigen Li Department of Physics and
Astronomy, University of Missouri, Columbia, MO 65211, USA

###### Abstract

We combined mapping-mode mid-infrared Spitzer spectra with complementary
infrared imaging to perform a spatially resolved study of polycyclic aromatic
hydrocarbons (PAHs) emission from the central regions of 66 nearby galaxies,
roughly evenly divided into star-forming systems and low-luminosity active
galactic nuclei (AGNs). In conjunction with similar measurements available for
quasars, we aim to understand the physical properties of PAHs across a broad
range of black hole accretion power, with the goal of identifying
observational diagnostics that can be used to probe the effect of AGNs on the
host galaxy. Whereas the PAH emission correlates tightly with far-ultraviolet
luminosity in star-forming regions, the spatially resolved regions of AGNs
tend to be PAH-deficient. Moreover, AGN regions exhibit on average smaller PAH
6.2 ${\rm\mu m}$/7.7 ${\rm\mu m}$ and larger PAH 11.3 ${\rm\mu m}$/7.7
${\rm\mu m}$ band ratios. Although the current data are highly restrictive,
they suggest that these anomalous PAH band ratios cannot be explained by the
effects of the AGN radiation field alone. Instead, they hint that small grains
may be destroyed by the combined effects of radiative processes and shocks,
which are plausibly linked to jets and outflows preferentially associated with
highly sub-Eddington, radiatively inefficient AGNs. While quasars also present
a PAH deficit and unusual PAH band ratios, their characteristics differ in
detail compared to those observed in more weakly accreting AGNs, a possible
indicator of fundamental differences in their modes of energy feedback.

galaxies: AGN — galaxies: ISM — galaxies: star formation — infrared: ISM

††journal: ApJ

##  1 Introduction

Polycyclic aromatic hydrocarbons (PAHs; Leger & Puget 1984; Allamandola et al.
1985; see review in Tielens 2008) produce among the most prominent spectral
features in the mid-infrared (IR) spectra of active and inactive galaxies. The
main PAH features at 6.2, 7.7, 8.6, 11.3, and 12.7 ${\rm\mu m}$ together can
account for up to 20% of the total IR emission in star-forming galaxies (SFGs;
Smith et al. 2007b; Xie et al. 2018; Li 2020). PAHs radiate through IR
fluorescence following vibrational excitation after absorbing a single
ultraviolet (UV) photon (Allamandola et al. 1989; Tielens 2005). Therefore,
the intensity of PAH emission indirectly traces the strength of the UV
radiation field, and hence recent star formation (Förster Schreiber et al.
2004; Peeters et al. 2004).

A number of works have focused on calibrating the strength of individual PAH
bands, or some combination thereof, against other traditionally well-
established tracers of star formation, concluding that PAH can serve as an
effective indicator of star formation rate (SFR) in different galaxy
environments (e.g., Calzetti et al. 2005, 2007; Wu et al. 2005; Treyer et al.
2010; Shipley et al. 2016; Maragkoudakis et al. 2018; Xie & Ho 2019).
Meanwhile, extensive observations have revealed that the relative intensity of
individual PAH features varies greatly across different environments (e.g.,
Genzel et al. 1998; Kaneda et al. 2005; Farrah et al. 2007; Gordon et al.
2008; O’Dowd et al. 2009; Hunt et al. 2010; Sales et al. 2010; Lebouteiller et
al. 2011). In particular, the harsh conditions around active galactic nuclei
(AGNs) are hostile to the survival of PAHs because they can be eroded by
extreme-UV or X-ray photons (Aitken & Roche 1985; Voit 1992).

Studies of the mid-IR spectra of nearby galaxies (Smith et al., 2007b;
Diamond-Stanic & Rieke, 2010) reveal that low-luminosity AGNs (LLAGNs), such
as Seyfert nuclei and low-ionization nuclear emission-line regions (LINERs;
Heckman 1980; Ho 2008), tend to exhibit weaker 6–8 ${\rm\mu m}$ PAH emission,
which can be attributed to selective destruction of smaller PAH grains
responsible for the shorter wavelength features by the hard radiation field of
the AGN. O’Dowd et al. (2009) found that the PAH characteristics also vary
with the age of the stellar population, as galaxies with younger populations
have stronger short-wavelength PAH bands than older galaxies. However, when
matched in stellar age, AGNs and galaxies with composite sources of ionization
still manifest weaker 7.7 ${\rm\mu m}$ emission than quiescent galaxies,
likely a consequence of the preferential depletion of small grains by shocks
and/or X-rays from the AGN. While in extragalactic H II regions (Gordon et
al., 2008) and low-metallicity dwarf galaxies (Hunt et al., 2010) the PAH
strength diminishes with increasing hardness of the radiation field, as traced
by the [Ne III] 15.5 ${\rm\mu m}$/[Ne II] 12.8 ${\rm\mu m}$ ratio, the
suppression of PAH in galaxies hosting AGNs is even more severe (Smith et al.,
2007b), hinting that more than radiative effects are operating. Support for
the role of shocks also comes from the correlation between the reduction of
7.7 ${\rm\mu m}$/11.3 ${\rm\mu m}$ with increasing prominence of the shock-
sensitive ${\rm H_{2}}\ S(3)$ 9.665 ${\rm\mu m}$ rotational line (Diamond-
Stanic & Rieke, 2010).

The above considerations suggest that radiative effects by a hard radiation
field alone cannot account for the PAH properties of AGNs. Some other factor,
potentially shocks, may play a role. Interstellar shocks with velocities
$\gtrsim 100\,{\rm km\ s^{-1}}$ in principle can completely destroy large PAHs
with more than $N_{\rm C}=50$ carbon atoms through the collision between
energetic ions/electrons and PAHs (Micelotta et al., 2010a). Moreover, PAHs
immersed in the hot post-shock gas can also be destroyed by collisions with
ions and electrons, with smaller PAHs suffering the fastest damage (Micelotta
et al. 2010b). Mechanical processing, therefore, produces strong variations in
the strength of the aromatic features, as PAHs of different sizes peak in
emission efficiency at different wavelengths. For instance, PAHs with $N_{\rm
C}\simeq 50,100$, and 500–1000 radiate their peak efficiency at 6.2, 7.7, and
11.3 ${\rm\mu m}$, respectively (Draine & Li, 2007). Despite the
implementation of shocks into modern models of dust evolution (e.g., Murga et
al. 2016, 2019; Hirashita & Murga 2020; Hirashita et al. 2020), observational
evidence regarding the mechanical effect of shocks on PAH characteristics is
still largely unestablished.

The mixture of PAHs with different sizes or ionization states over multiple
physical scales can lead to diverse PAH band ratios (Draine & Li, 2001, 2007;
Draine et al., 2021). Isolating the most AGN-dominated regions using spatially
resolved analysis of mid-IR spectra can help to shed light on the underlying
physical mechanisms responsible for the diversity of PAH emission in active
galaxies. Very few such attempts have been made. In this paper, we perform a
comprehensive investigation of the mid-IR PAH features using spatially
resolved, mapping-mode observations obtained by the Spitzer Infrared Nearby
Galaxies Survey (SINGS; Kennicutt et al. 2003) acquired with the Infrared
Spectrograph (IRS; Houck et al. 2004) on the Spitzer Space Telescope (Werner
et al. 2004). Section 2 describes the observational material and data
processing, including the methodology to measure PAH emission. The results of
the spatially resolved analysis are presented in Section 3. Section 4
discusses the physical mechanisms responsible for the differences in PAH band
ratios in AGNs and SFGs, as well as implications for AGN feedback. Main
conclusions are summarized in Section 5.

##  2 Observational Material

###  2.1 Sample and Data

SINGS111https://irsa.ipac.caltech.edu/data/SPITZER/SINGS/ is a comprehensive
IR imaging and spectroscopic survey of 75 nearby galaxies conducted using
Spitzer (SINGS Team 2020). To obtain spatially resolved mid-IR emission lines
for the central regions of the galaxies, we utilize the three-dimensional
spectral datacubes released by the survey team, which were constructed using
the CUBISM tool (Smith et al. 2007a). The sample spans a representative range
of galaxy properties, including nuclear activity, and hence is well suited to
study the impact of AGN activity on PAH emission. This paper focuses on the 66
galaxies that have successful IRS mapping-mode observations covering $\sim
5-38\ {\rm\mu m}$. Table 1 provides a summary of a few key galaxy properties.
Nuclear spectral classifications are taken from Moustakas et al. (2010), but
preference is given to the more detailed classifications of Ho et al. (1997a,
b), whenever available. Bolometric luminosities for the 30 AGNs in the sample
are based on either total (narrow plus broad) H$\alpha$ emission (Ho et al.,
2003) or nuclear 2–10 keV X-ray emission (Ho, 2009), using bolometric
corrections appropriate for LLAGNs (Ho, 2008). We estimate black hole masses
from the $M_{\rm BH}-\sigma_{*}$ relation, as updated by Greene et al. (2020)
for all galaxy types, with the aid of central stellar velocity dispersions
from Ho et al. (2009).

Figure 1: Illustration of coverage of IRS mapping-mode observations of M 51,
overlaid on GALEX FUV image as the background. The black and white rectangles
delineate the central region with complete SL+LL and SH+LH spectra,
respectively, with the black star marking the center. The inset in the top-
right corner is a zoom-in of the central $2\arcmin\times 2\arcmin$ region that
shows the distribution of resolved $10\arcsec\times 10\arcsec$ spaxels within
the black rectangle that has complete SL+LL spectra, color-coded (from orange
to yellow) according to the flux of the IRS spectra integrated over $5-20\
{\rm\mu m}$.

The IRS has both a low-resolution and a high-resolution module, each with two
sets of slits to capture different wavelength ranges. The short-low (SL) mode
has two orders with slit sizes of $3\farcs 6\times 57\arcsec$ (SL2) and
$3\farcs 7\times 57\arcsec$ (SL1), covering, respectively, $5.2-7.7\ \mu$m and
$7.4-14.5\ \mu$m, while the two orders of the long-low (LL) mode cover
$14.0-21.3\ \mu$m with the $10\farcs 5\times 168\arcsec$ slit (LL2) and
$19.5-38.0\ \mu$m with the $10\farcs 7\times 168\arcsec$ slit (LL1). The
resolving power of the low-resolution module varies from
$\lambda/\Delta\lambda\approx 64$ to 128. The short-high (SH) mode samples
$9.9-19.6\ \mu$m with the $4\farcs 7\times 11\farcs 3$ slit, while the long-
high (LH) mode samples $18.7-37.2\ \mu$m with the $11\farcs 1\times 22\farcs
3$ slit, both with $\lambda/\Delta\lambda\approx 600$. The mapping-mode
observations were conducted by scanning the slit to cover large sections of
the galaxy. For the central region of each galaxy the mapping area covered
$\sim 30\arcsec\times 50\arcsec$ with complete low-resolution (SL+LL) spectra
and $\sim 15\arcsec\times 23\arcsec$ (nine galaxies have slightly larger
coverage; Dale et al. 2009) with complete high-resolution (SH+LH) spectra.
This paper focuses on the central regions covered by complete low-resolution
mapping-mode observations. An example galaxy is shown in Figure 1.

To provide spatially resolved information on the star formation activity, we
collect far-UV (FUV; 1350 Å–1750 Å) images from the Galaxy Evolution Explorer
(GALEX) UV Atlas of Nearby
Galaxies222https://archive.stsci.edu/prepds/galex_atlas/ (Gil de Paz et al.
2007). To implement the methodology of Zhang et al. (2021) for spectral
decomposition and to provide sufficient leverage to constrain the stellar
continuum (Section 2.2), we additionally make use of near-IR ($J,H,K_{s}$)
images from the Two-Micron All Sky Survey (2MASS) Large Galaxy
Atlas333https://irsa.ipac.caltech.edu/data/LGA/ (Jarrett et al., 2003, 2020)
and mid-IR (W1, W2, W3) images from
WISE444https://irsa.ipac.caltech.edu/applications/wise/ (Wright et al., 2010;
NASA/IPAC Infrared Science Archive, 2020) and Spitzer (IRAC1, IRAC2, IRAC3,
IRAC4, MIPS24). Table 2.1 lists the multiwavelength imaging datasets used in
this paper. The IRAC4, W3, and MIPS24 bands also help to mitigate against the
systematics in the convolution of the low-resolution IRS datacube (Section
2.2). To correct for the differences in the photometric calibration between
point sources and extended sources in the IRAC images, we adjust the IRAC flux
density according to the recommendations in the IRAC Instrument
Handbook.555https://irsa.ipac.caltech.edu/data/SPITZER/docs/irac/iracinstrumenthandbook/29/

Table 1: Properties of the Galaxy Sample

Galaxy | Hubble | Nuclear | $D_{L}$ | $E(B-V)$ | $\log L_{\rm bol}$ | $\log M_{\rm BH}$ | Galaxy | Hubble | Nuclear | $D_{L}$ | $E(B-V)$ | $\log L_{\rm bol}$ | $\log M_{\rm BH}$
---|---|---|---|---|---|---|---|---|---|---|---|---|---
Name | Type | Classification | (Mpc) | (mag) | ($\rm erg\ s^{-1}$) | ($M_{\odot}$) | Name | Type | Classification | (Mpc) | (mag) | ($\rm erg\ s^{-1}$) | ($M_{\odot}$)
(1) | (2) | (3) | (4) | (5) | (6) | (7) | (1) | (2) | (3) | (4) | (5) | (6) | (7)
DDO 53 | Im | SFG | 3.61 | 0.032 | $\cdots$ | $\cdots$ | NGC 3521 | SABbc | AGN(H/L2::) | 11.2 | 0.050 | 41.04$\ g$$\ g$footnotemark:  | 7.50
DDO 165 | Im | SFG | 4.57 | 0.020 | $\cdots$ | $\cdots$ | NGC 3621 | Sd | AGN | 6.55 | 0.069 | 40.49$\ h{\dagger}$$\ h{\dagger}$footnotemark:  | 6.48$\ h{\dagger}$$\ h{\dagger}$footnotemark:
Holm IX | Im | SFG | 3.5 | 0.067 | $\cdots$ | $\cdots$ | NGC 3627 | SABb | AGN(T2/S2) | 9.38 | 0.029 | 38.72 | 7.40
IC 4710 | SBm | SFG | 8.5 | 0.077 | $\cdots$ | $\cdots$ | NGC 3773 | S0 | SFG | 12.4 | 0.023 | $\cdots$ | $\cdots$
Mrk 33 | Im | SFG | 21.7 | 0.010 | $\cdots$ | $\cdots$ | NGC 3938 | Sc | SFG | 17.9 | 0.018 | $\cdots$ | $\cdots$
NGC 24 | Sc | SFG | 8.2 | 0.017 | $\cdots$ | $\cdots$ | NGC 4125 | E6 | AGN(T2) | 21.4 | 0.016 | 40.03 | 8.54
NGC 337 | SBd | SFG | 19.3 | 0.096 | $\cdots$ | $\cdots$ | NGC 4236 | SBdm | SFG | 4.45 | 0.013 | $\cdots$ | $\cdots$
NGC 584 | E4 | AGN | 20.8 | 0.036 | 40.26$\ a{\dagger}$$\ a{\dagger}$footnotemark:  | 8.13$\ a$$\ a$footnotemark:  | NGC 4254 | Sc | SFG | 14.4 | 0.033 | $\cdots$ | $\cdots$
NGC 628 | Sc | SFG | 7.2 | 0.061 | $\cdots$ | $\cdots$ | NGC 4321 | SABbc | AGN(T2) | 14.3 | 0.023 | 39.68 | 6.64
NGC 855 | E | SFG | 9.73 | 0.061 | $\cdots$ | $\cdots$ | NGC 4450 | Sab | AGN(L1.9) | 20.0 | 0.024 | 41.37 | 7.56
NGC 925 | SABd | SFG | 9.12 | 0.066 | $\cdots$ | $\cdots$ | NGC 4536 | SABbc | SFG | 14.5 | 0.016 | $\cdots$ | $\cdots$
NGC 1097 | SBb | AGN(S1) | 14.2 | 0.023 | 41.80$\ b$$\ b$footnotemark:  | 8.10$\ b$$\ b$footnotemark:  | NGC 4552 | E | AGN(T2:) | 4.9 | 0.035 | 39.62 | 8.74
NGC 1266 | SB0 | AGN(L) | 30.6 | 0.085 | 43.55$\ c{\dagger}$$\ c{\dagger}$footnotemark:  | 6.23$\ c$$\ c$footnotemark:  | NGC 4559 | SABcd | SFG | 6.98 | 0.015 | $\cdots$ | $\cdots$
NGC 1291 | SB0/a | AGN | 10.4 | 0.011 | 40.56$\ b$$\ b$footnotemark:  | 8.00$\ b$$\ b$footnotemark:  | NGC 4569 | SABab | AGN(T2) | 9.86 | 0.040 | 40.15 | 7.57
NGC 1316 | SAB0 | AGN(L) | 21.0 | 0.018 | 40.48$\ d$$\ d$footnotemark:  | 8.44$\ d$$\ d$footnotemark:  | NGC 4579 | SABb | AGN(S1.9/L1.9) | 16.4 | 0.035 | 42.33 | 7.94
NGC 1377 | S0 | SFG | 24.6 | 0.024 | $\cdots$ | $\cdots$ | NGC 4594 | Sa | AGN(L2) | 9.08 | 0.044 | 41.20 | 8.65
NGC 1404 | E1 | AGN | 20.2 | 0.010 | 41.73$\ d$$\ d$footnotemark:  | 8.50$\ d$$\ d$footnotemark:  | NGC 4625 | SABm | SFG | 9.3 | 0.016 | $\cdots$ | $\cdots$
NGC 1482 | S0 | SFG | 22.6 | 0.034 | $\cdots$ | $\cdots$ | NGC 4631 | SBd | SFG | 7.62 | 0.015 | $\cdots$ | $\cdots$
NGC 1512 | SBab | AGN | 11.6 | 0.009 | 40.37$\ e{\dagger}$$\ e{\dagger}$footnotemark:  | 7.13$\ e$$\ e$footnotemark:  | NGC 4725 | SABab | AGN(S2:) | 11.9 | 0.010 | 40.27 | 7.63
NGC 1566 | SABbc | AGN(S1.5) | 18.0 | 0.008 | 42.49$\ f$$\ f$footnotemark:  | 6.83$\ f$$\ f$footnotemark:  | NGC 4736 | Sab | AGN(L2) | 4.66 | 0.015 | 39.75 | 7.21
NGC 1705 | Am | SFG | 5.8 | 0.007 | $\cdots$ | $\cdots$ | NGC 4826 | Sab | AGN(T2) | 5.27 | 0.036 | 38.75 | 6.92
NGC 2403 | SABcd | SFG | 3.5 | 0.034 | $\cdots$ | $\cdots$ | NGC 5033 | Sc | AGN(S1.5) | 13.3 | 0.010 | 41.60 | 7.77
NGC 2798 | SBa | SFG | 25.8 | 0.017 | $\cdots$ | $\cdots$ | NGC 5055 | Sbc | AGN(T2) | 7.94 | 0.015 | 39.65 | 7.29
NGC 2841 | Sb | AGN(L2) | 14.1 | 0.013 | 39.60 | 8.50 | NGC 5194 | SABbc | AGN(S2) | 8.2 | 0.031 | 42.28 | 6.92
NGC 2915 | I0 | SFG | 3.78 | 0.236 | $\cdots$ | $\cdots$ | NGC 5195 | SB0 | AGN(L2:) | 8.2 | 0.030 | 39.09 | 7.41
NGC 2976 | Sc | SFG | 3.55 | 0.064 | $\cdots$ | $\cdots$ | NGC 5408 | IBm | SFG | 4.8 | 0.059 | $\cdots$ | $\cdots$
NGC 3031 | Sab | AGN(S1.5) | 3.5 | 0.069 | 41.37 | 7.90 | NGC 5713 | SABbc | SFG | 21.4 | 0.034 | $\cdots$ | $\cdots$
NGC 3049 | SBab | SFG | 19.2 | 0.033 | $\cdots$ | $\cdots$ | NGC 5866 | S0 | AGN(T2) | 15.3 | 0.011 | 39.80 | 7.98
NGC 3184 | SABcd | SFG | 11.7 | 0.014 | $\cdots$ | $\cdots$ | NGC 6946 | SABcd | SFG | 6.8 | 0.294 | $\cdots$ | $\cdots$
NGC 3190 | Sa | AGN(L2) | 19.3 | 0.022 | 40.61 | 8.18 | NGC 7331 | Sb | AGN(T2) | 14.5 | 0.078 | 39.87 | 7.59
NGC 3198 | SBc | SFG | 14.1 | 0.011 | $\cdots$ | $\cdots$ | NGC 7552 | Sc | SFG | 22.3 | 0.012 | $\cdots$ | $\cdots$
NGC 3265 | E | SFG | 19.6 | 0.021 | $\cdots$ | $\cdots$ | NGC 7793 | Sd | SFG | 3.91 | 0.017 | $\cdots$ | $\cdots$
NGC 3351 | SBb | SFG | 9.33 | 0.024 | $\cdots$ | $\cdots$ | Tol 89 | SBdm | SFG | 7.66 | 0.057 | $\cdots$ | $\cdots$

a-ga-gfootnotetext:  References for $L_{\rm bol}$ and $M_{\rm BH}$: (a) Lehmer
et al. (2019, converted to 2–10 keV luminosity based on $\Gamma$ and $N_{\rm
H}$ therein) and Thater et al. (2019), respectively; (b) Cisternas et al.
(2013, and references therein); (c) Alatalo et al. (2015, $L_{\rm bol}$ based
on IR SED fitting); (d) Pellegrini (2010, and references therein); (e) Ducci
et al. (2014) and Dullo et al. (2020), respectively; (f) Combes et al. (2019);
(g) $L_{\rm bol}$ based on nuclear H$\alpha$ emission from Ho et al. (1997a),
as updated in Ho et al. (2003), with bolometric correction factor $C_{\rm
H\alpha}=300$ (Ho 2009); (h) Gliozzi et al. (2009) and Barth et al. (2009),
respectively.

††footnotetext: Upper limit.

Note. — Col. (1): Galaxy name. Col. (2): Hubble type taken from NASA/IPAC
Extragalactic Database (NED). Col. (3): Nuclear classification according to
the [O III]/H$\beta$ versus [N II]/H$\alpha$ emission-line diagnostic diagram
based on the spectra extracted from a central aperture of $2\farcs 5\times
2\farcs 5$, which distinguish it as a star-forming galaxy (SFG) or an AGN (see
Moustakas et al. 2010 for details). We provide more specific classifications
for the AGN if available from Ho et al. (1997a, b) or NED, where S = Seyfert,
L = LINER, T = transition object, 1 = type 1 (broad-line), 2 = type 2 (narrow-
line), a number between 1 and 2 pertains to an intermediate-type, and “:”
indicates an uncertain classification (Ho et al., 1997a). Col. (4): Luminosity
distance taken from the updated SINGS photometry catalog (Dale et al., 2017).
Col. (5): Galactic reddening from Schlafly & Finkbeiner (2011). Col. (6):
Bolometric luminosity calculated based on the nuclear 2–10 keV luminosity,
with luminosity distance adjusted to that adopted here, and bolometric
correction $C_{\rm X}=15.8$ (Ho 2009), unless otherwise specified. Col. (7):
Black hole mass derived from the $M_{\rm BH}-\sigma_{*}$ relation (for all
galaxy types) from Greene et al. (2020) and the central stellar velocity
dispersion measured by Ho et al. (2009), unless otherwise specified.

Table 2: Summary of Multiwavelength Imaging Data

Telescope | Filter | $\lambda_{\rm eff}$ | FWHM | $\sigma_{\rm cal}$ | References
---|---|---|---|---|---
|  | $(\mu m)$ | (″) | (%) |
(1) | (2) | (3) | (4) | (5) | (6)
GALEX | FUV | 0.153 | 4.2 | 5 | 1,2
2MASS | $J$ | 1.25 | 2.5 | 5 | 3, 4
2MASS | $H$ | 1.65 | 2.5 | 5 | 3, 4
2MASS | $K_{s}$ | 2.16 | 2.5 | 5 | 3, 4
WISE | W1 | 3.35 | 6.1 | 2.4 | 5, 6
WISE | W2 | 4.60 | 6.4 | 2.8 | 5, 6
WISE | W3 | 11.56 | 6.5 | 4.5 | 5, 6
Spitzer | IRAC1 | 3.55 | 1.7 | 10 | 7, 8
Spitzer | IRAC2 | 4.49 | 1.7 | 10 | 7, 8
Spitzer | IRAC3 | 5.73 | 1.9 | 10 | 7, 8
Spitzer | IRAC4 | 7.87 | 2.0 | 10 | 7, 8
Spitzer | MIPS24 | 23.68 | 6.0 | 4 | 7, 9

Note. — Col. (1): Telescope. Col. (2): Filter. Col. (3): Effective wavelength
of the filter. Col. (4): FWHM of the PSF. Col. (5): Calibration uncertainty.
Col. (6): References.

References. — (1) Morrissey et al. 2007; (2) Gil de Paz et al. 2007; (3)
Jarrett et al. 2003; (4) Skrutskie et al. 2006; (5) Wright et al. 2010; (6)
Jarrett et al. 2013; (7) Dale et al. 2007; (8) Fazio et al. 2004; (9)
Engelbracht et al. 2007.

###  2.2 Data Processing and Spectral Measurements

####  2.2.1 Convolution, Optimal Binning, Spectral Extraction

To extract spatially resolved PAH emission, we follow the strategy of Zhang et
al. (2021), which is summarize here. Although the publicly released imaging
and spectroscopic data of SINGS are already background-
subtracted666https://irsa.ipac.caltech.edu/data/SPITZER/SINGS/doc/sings_fifth_delivery_v2.pdf,
we discovered that some ancillary images have evident background residuals
that need further treatment. After masking out real sources with elliptical
apertures through pipeline determination and visual check, we remove
background residuals by fitting and subtracting a two-dimensional polynomial
function. Then, both the images and spectral datacube are convolved to same
angular resolution with ${\rm FWHM}=10\arcsec$, which is comparable to but
slightly broader than the coarsest PSF (${\rm FWHM}=9\arcsec$) among all the
images and slices of the spectral datacube (${\rm FWHM}\approx 1\farcs
5-3\farcs 5$ for the SL mode to $4\farcs 5-9\farcs 0$ for the LL mode). After
the convolution, we reproject all the data into the same coordinate frame
using a final pixel size of 10″, which corresponds to a physical scale of
$\sim 0.17-1.25$ kpc for the galaxies studied here and ensures that all the
spaxels within each galaxy are spatially independent. In the following
analysis, the spatially resolved spaxels from each galaxy are regarded as
independent samplings of similar galactic environments with different PAH
characteristics, and, unless otherwise stated, we do not discriminate among
the resolved spaxels from different galaxies.

We optimally bin the spectral datacube into final spaxels that simultaneously
maximize spatial resolution while ensuring sufficient signal-to-noise ratio
(S/N) to yield accurate PAH measurements (Zhang et al., 2021). The binning
process considers the similarity between the spectra of different pixels to
maximize the association of spatial structures in physical properties. The
ancillary images are binned to exactly the same corresponding spaxel scale to
facilitate the construction of spatially resolved spectral energy
distributions (SEDs). The resolved photometry for the 12 photometric bands is
provided in Table 3. Finally, to mitigate against systematics in the
convolution of the spectral datacube, the IRS spectrum of each binned spaxel
is scaled by a numerical factor determined by minimizing the weighted
difference between the observed flux densities and the flux densities
synthesized from the IRS spectrum using the response curves of the mid-IR
photometric bands. The above procedure generates 1 to 15 low-resolution
spectra ($\sim 5-38\ {\rm\mu m}$) and associated spatially resolved SEDs for
the central region of each galaxy (see an example in the inset of Figure 1).
Figure 2 illustrates the collection of integrated low-resolution spectra for
the central region of all SFGs and AGNs studied in this paper, as well as
their respective average composite spectra.

Table 3: Spatially Resolved Multi-band Photometry

Region | log $f_{\rm FUV}$ | log $f_{J}$ | log $f_{H}$ | log $f_{Ks}$ | log $f_{\rm W1}$ | log $f_{\rm W2}$ | log $f_{\rm W3}$ | log $f_{\rm IRAC1}$ | log $f_{\rm IRAC2}$ | log $f_{\rm IRAC3}$ | log $f_{\rm IRAC4}$ | log $f_{\rm MIPS24}$
---|---|---|---|---|---|---|---|---|---|---|---|---
| (mJy) | (mJy) | (mJy) | (mJy) | (mJy) | (mJy) | (mJy) | (mJy) | (mJy) | (mJy) | (mJy) | (mJy)
(1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | (10) | (11) | (12) | (13)
IC4710_1 |  $-$0.12 $\pm$ 0.01 | 0.35 $\pm$ 1.37 |  $\cdots$ |  $\cdots$ | 0.58 $\pm$ 0.02 | 0.33 $\pm$ 0.03 | 0.78 $\pm$ 0.01 | 0.53 $\pm$ 0.12 | 0.36 $\pm$ 0.20 |  $-$0.01 $\pm$ 0.33 | 0.71 $\pm$ 0.05 | 1.34 $\pm$ 0.02
Mrk33_1 | 0.25 $\pm$ 0.02 | 1.06 $\pm$ 0.03 | 1.12 $\pm$ 0.03 | 1.05 $\pm$ 0.03 | 0.81 $\pm$ 0.01 | 0.66 $\pm$ 0.01 | 1.66 $\pm$ 0.02 | 0.90 $\pm$ 0.04 | 0.78 $\pm$ 0.04 | 1.32 $\pm$ 0.04 | 1.73 $\pm$ 0.04 | 2.65 $\pm$ 0.02
Mrk33_2 |  $-$0.15 $\pm$ 0.02 | 0.66 $\pm$ 0.04 | 0.73 $\pm$ 0.05 | 0.65 $\pm$ 0.06 | 0.52 $\pm$ 0.01 | 0.38 $\pm$ 0.01 | 1.38 $\pm$ 0.02 | 0.54 $\pm$ 0.05 | 0.43 $\pm$ 0.04 | 0.95 $\pm$ 0.04 | 1.36 $\pm$ 0.04 | 2.26 $\pm$ 0.02
Mrk33_3 |  $-$0.49 $\pm$ 0.02 | 0.58 $\pm$ 0.05 | 0.66 $\pm$ 0.06 | 0.56 $\pm$ 0.08 | 0.42 $\pm$ 0.01 | 0.27 $\pm$ 0.01 | 1.29 $\pm$ 0.02 | 0.38 $\pm$ 0.05 | 0.26 $\pm$ 0.05 | 0.79 $\pm$ 0.04 | 1.18 $\pm$ 0.04 | 1.97 $\pm$ 0.02
Mrk33_4 |  $-$0.39 $\pm$ 0.02 | 0.61 $\pm$ 0.05 | 0.66 $\pm$ 0.06 | 0.56 $\pm$ 0.08 | 0.36 $\pm$ 0.01 | 0.19 $\pm$ 0.01 | 1.16 $\pm$ 0.02 | 0.36 $\pm$ 0.05 | 0.21 $\pm$ 0.05 | 0.67 $\pm$ 0.05 | 1.06 $\pm$ 0.04 | 1.80 $\pm$ 0.02
Mrk33_5 |  $-$0.32 $\pm$ 0.01 | 0.95 $\pm$ 0.05 | 1.00 $\pm$ 0.07 | 0.90 $\pm$ 0.09 | 0.76 $\pm$ 0.01 | 0.60 $\pm$ 0.01 | 1.59 $\pm$ 0.01 | 0.67 $\pm$ 0.04 | 0.53 $\pm$ 0.03 | 0.93 $\pm$ 0.03 | 1.32 $\pm$ 0.02 | 1.91 $\pm$ 0.01
Mrk33_6 |  $-$2.22 $\pm$ 0.35 | 0.03 $\pm$ 0.26 | 0.17 $\pm$ 0.30 | 0.03 $\pm$ 0.42 |  $-$0.17 $\pm$ 0.01 |  $-$0.37 $\pm$ 0.02 | 0.59 $\pm$ 0.01 |  $-$0.31 $\pm$ 0.18 |  $-$0.46 $\pm$ 0.10 |  $-$0.76 $\pm$ 0.52 | 0.11 $\pm$ 0.07 | 0.23 $\pm$ 0.07
NGC24_1 | 0.08 $\pm$ 0.01 | 1.71 $\pm$ 0.01 | 1.77 $\pm$ 0.02 | 1.66 $\pm$ 0.03 | 1.41 $\pm$ 0.00 | 1.17 $\pm$ 0.00 | 1.41 $\pm$ 0.01 | 1.38 $\pm$ 0.02 | 1.20 $\pm$ 0.02 | 1.29 $\pm$ 0.11 | 1.52 $\pm$ 0.03 | 1.50 $\pm$ 0.01
NGC337_1 |  $-$0.62 $\pm$ 0.02 | 0.62 $\pm$ 0.06 | 0.69 $\pm$ 0.06 | 0.61 $\pm$ 0.10 | 0.51 $\pm$ 0.01 | 0.39 $\pm$ 0.01 | 1.26 $\pm$ 0.02 | 0.57 $\pm$ 0.04 | 0.47 $\pm$ 0.04 | 1.05 $\pm$ 0.04 | 1.45 $\pm$ 0.04 | 2.05 $\pm$ 0.02
NGC337_2 |  $-$0.78 $\pm$ 0.03 | 0.93 $\pm$ 0.04 | 1.01 $\pm$ 0.04 | 0.92 $\pm$ 0.05 | 0.70 $\pm$ 0.01 | 0.51 $\pm$ 0.01 | 1.20 $\pm$ 0.02 | 0.72 $\pm$ 0.04 | 0.57 $\pm$ 0.04 | 1.00 $\pm$ 0.04 | 1.37 $\pm$ 0.04 | 1.63 $\pm$ 0.02

Note. — Spatially resolved multi-band flux densities and corresponding
uncertainties. (This table is available in its entirety in machine-readable
form.)

Figure 2: Integrated low-resolution IRS spectra, with ionic emission lines
masked, for the central region of galaxies classified as (a) SFGs and (b)
AGNs. The main PAH features are marked in panel (a). Each integrated spectrum
is obtained by summing the spectra of all the spaxels within the corresponding
central region and normalized by the flux density at 7.7 ${\rm\mu m}$. Panel
(c) shows the composite spectra for the regions from SFGs (blue) and AGNs
(red).

The physical diagnostics in the following analysis (e.g., hardness of the
radiation field, ionization parameter, and shock strength) are based on the
narrow ionic and molecular emission lines. Although these features can be
accessed from the low-resolution spectra, their measurement is highly
sensitive to the treatment of the underlying PAH and continuum emission, and
in practice we can measure them reliably only from the high-resolution
spectra. The high-resolution spectra cover a much smaller area than that
mapped in low-resolution mode (Figure 1), as a consequence of which we do not
have matching high-resolution diagnostics for every spaxel with low-resolution
spectra. Consequently, in our subsequent analysis (e.g., Sections 3 and 4), we
resort to applying to all the spatially resolved low-resolution spaxels from
the same galaxy a single value of the physical diagnostics inferred from the
effectively spatially unresolved high-resolution spectra. To provide a very
rough validation of our approach, we extract from each high-resolution
datacube spectra of two spatially separate sub-regions: an “inner” sub-region
that covers the centralmost $10\arcsec\times 10\arcsec$ area, which
corresponds to the central spaxel of the low-resolution datacube after
projection, and another “outer” sub-region covering all the remaining area
with high-resolution observations. No convolution is applied to the high-
resolution datacube, and the SH spectra are scaled to match the continuum
level of the LH spectra. These spectra will be discussed in Section 4.3.

Figure 3: Illustration of PAH decomposition for two sample IRS spectra, from
(a) NGC 1097 and (b) NGC 2841. The gray solid line and black points are the
observed IRS spectra and near-IR photometry. The red solid lines gives the
best-fit model, which is composed of a PAH template (green dashed line), a
stellar continuum component (orange dashed line) from Bruzual & Charlot (2003,
BC03), and three dust continuum components (blue dashed lines), each
represented by a modified blackbody (MBB). Figure 4: Integrated residual PAH
spectra for the central regions of (a) SFGs and (b) AGNs, obtained by summing
the residual PAH spectra of all the spaxels within corresponding central
region and normalized by the flux density at 7.7 ${\rm\mu m}$. The residual
PAH spectrum is produced by subtracting the best-fit continuum components from
the observed spectrum. The main PAH features are marked in panel (a). Panel
(c) shows the composite residual PAH spectra for regions from SFGs (blue) and
AGNs (red). Panel (d) illustrates an example of decomposition of individual
PAH features from the residual spectrum (grey line) using Drude profiles
(dashed curves), which combine to produce the total model (red curve).

####  2.2.2 Measuring the PAH Features

Our spectral decomposition method for PAH measurement was designed and
extensively tested for low-resolution IRS spectra of diverse galactic
environments, including high-latitude clouds in the Milky Way, galaxies of
ordinary to extreme degrees of star formation, as well as a wide range of
levels of nuclear activity, from LLAGNs to systems powerful enough to qualify
as quasars (Xie et al., 2018). This template fitting method, which can
properly separate the PAH emission from the underlying continuum with only a
few free parameters, was later applied to an even more extensive sample of
SFGs covering broad galaxy properties (Xie & Ho, 2019). Zhang et al. (2021)
extended the method to spatially resolved, mapping-mode observations within
individual galaxies, crucially by using supplementary photometry to treat the
important regime when the IRS spectral coverage is incomplete (only SL or LL
spectra are available).

After masking narrow emission lines that are not blended with the main PAH
features, we fit the $\sim 5-38\,{\rm\mu m}$ spectrum with a multi-component
model comprising a theoretical PAH template and three modified blackbodies of
different temperatures to represent the dust continuum, all subject to
attenuation by foreground extinction. In anticipation of other applications of
the current dataset, some of which are explored in other papers in this series
(e.g., Zhang & Ho 2022), we augment the fitting method by including an
additional spectral component to account for the underlying stellar continuum
at near-IR wavelengths. We represent the starlight continuum using a stellar
population model from Bruzual & Charlot (2003), which assumes solar
metallicity, a Chabrier (2003) stellar initial mass function, a constant star
formation history, and a fixed stellar age of 6 Gyr. This relatively simple
model suffices for our purposes, as our primary focus is in the near-IR, which
is mainly sensitive to evolved stars. We have verified that using more
complicated star formation histories and a wider mixture of stellar ages makes
little difference to our final results.

Figure 5: (Top) Illustration of high-resolution IRS spectrum of M 51, marked
with prominent ionic and molecular hydrogen emission lines. (Bottom) Zoom-in
of individual narrow emission lines used in this paper (in units of $\rm
10^{-12}\ erg\ s^{-1}\ cm^{-2}\ {\rm\mu m}^{-1}$); the data are plotted as
black histograms, with uncertainties indicated in gray. The red curve is the
best-fitting Gaussian profile on top of a linear local continuum (green
curve).

Table 4: Spatially Resolved Measurements of PAH Emission

Region | log $L_{\rm PAH}^{5-20}$ | log $L_{\rm PAH}^{6.2}$ | log $L_{\rm PAH}^{7.7}$ | log $L_{\rm PAH}^{11.3}$
---|---|---|---|---
| ($\rm erg\ s^{-1}$) | ($\rm erg\ s^{-1}$) | ($\rm erg\ s^{-1}$) | ($\rm erg\ s^{-1}$)
(1) | (2) | (3) | (4) | (5)
IC4710_1 | 39.45 $\pm$ 0.09 |  $\cdots$ |  $\cdots$ |  $\cdots$
Mrk33_1 | 41.83 $\pm$ 0.02 | 40.90 $\pm$ 0.04 | 41.39 $\pm$ 0.04 | 40.84 $\pm$ 0.05
Mrk33_2 | 41.42 $\pm$ 0.02 | 40.40 $\pm$ 0.05 | 40.97 $\pm$ 0.05 | 40.37 $\pm$ 0.06
Mrk33_3 | 41.31 $\pm$ 0.02 | 40.36 $\pm$ 0.04 | 40.89 $\pm$ 0.03 | 40.29 $\pm$ 0.07
Mrk33_4 | 41.28 $\pm$ 0.01 | 40.33 $\pm$ 0.05 | 40.82 $\pm$ 0.05 | 40.25 $\pm$ 0.07
Mrk33_5 | 41.47 $\pm$ 0.02 | 40.50 $\pm$ 0.04 | 41.10 $\pm$ 0.03 | 40.45 $\pm$ 0.08
Mrk33_6 | 39.98 $\pm$ 0.05 |  $\cdots$ |  $\cdots$ |  $\cdots$
NGC24_1 | 40.61 $\pm$ 0.02 | 39.70 $\pm$ 0.04 | 40.27 $\pm$ 0.03 | 39.81 $\pm$ 0.04
NGC337_1 | 41.53 $\pm$ 0.02 | 40.57 $\pm$ 0.05 | 41.05 $\pm$ 0.06 | 40.44 $\pm$ 0.04
NGC337_2 | 41.38 $\pm$ 0.01 | 40.44 $\pm$ 0.04 | 40.95 $\pm$ 0.04 | 40.34 $\pm$ 0.04

Note. — Col. (1): Resolved spaxels. Col. (2): Integrated PAH luminosity from
$5-20\ {\rm\mu m}$ of best-fit PAH template. Cols. (3)–(5): PAH luminosity of
PAH 6.2 ${\rm\mu m}$, 7.7 ${\rm\mu m}$ (7.414 ${\rm\mu m}$, 7.598 ${\rm\mu
m}$, and 7.85 ${\rm\mu m}$), and 11.3 ${\rm\mu m}$ (11.23 ${\rm\mu m}$ and
11.33 ${\rm\mu m}$) features. (This table is available in its entirety in
machine-readable form.)

Figure 3 illustrates our multi-component spectral decomposition method for two
example spectra, one representing a fairly typical case with prominent PAH
emission and a strong dust continuum, and another for which the stellar
component is strong but the PAH and dust emission are weak. The SED fitting is
carried out using the Bayesian Markov chain Monte Carlo (MCMC) procedure emcee
in the Python package. As described in detail in Shangguan et al. (2018), we
include an extra 20% uncertainty to the IRS spectrum in order to balance
properly the relative weights of the sparse photometric points with the
densely sampled spectroscopic data. We take the median and standard deviation
of the posterior distribution of each best-fit parameter as the final estimate
and its corresponding uncertainty. Based on the best-fit model for each
spaxel, we define the integrated PAH luminosity, $L_{\rm PAH}$, as the
integral of the best-fit PAH template over the $5-20\ {\rm\mu m}$ region.

The strengths of the individual PAH features are measured from the residual
PAH spectrum by subtracting the best-fit stellar and dust continuum components
from the observed spectrum with the extinction corrected. Figure 4 displays
the integrated residual PAH spectra for the central region of each galaxy, as
well as the averaged, composite residual PAH spectra for galaxies classified
as SFGs and AGNs (Figure 4c). We decompose the individual PAH features using a
Bayesian MCMC procedure that fits a series of Drude profiles (Draine & Li,
2007) to the emission peaks (Figure 4d). Xie et al. (2018) showed that the
measurements of individual PAH features based on our method agree well with
those from other methods (e.g., PAHFIT; Smith et al. 2007b). This paper
focuses on the most prominent 6.2 ${\rm\mu m}$ feature, the 7.7 ${\rm\mu m}$
complex, and the 11.3 ${\rm\mu m}$ complex, which have been measured for 453
spaxels (Table 4).

####  2.2.3 Measuring the Narrow Emission Lines

The high-resolution spectra give access to a set of narrow ionic fine-
structure lines and molecular hydrogen vibrational lines, which provide
valuable diagnostics of the physical conditions of the interstellar medium
(Figure 5, top panel). The narrow emission lines have a simple profile at the
current instrumental resolution, and we measure them by fitting a single
Gaussian function plus a local, linear continuum (Figure 5, bottom panels). In
addition to the spectrum integrated over the entire mapped area, we also have
spectra extracted for the “inner” and “outer” sub-regions, as defined in
Section 4.2. All the line diagnostics are based on extinction-corrected
values, using the best-fit extinction derived from the low-resolution spectral
fitting. Table 5 lists measurements for all three extractions, emphasizing the
following line-intensity ratios: [Ne III] 15.5 ${\rm\mu m}$/[Ne II] 12.8
${\rm\mu m}$, an indicator of the hardness of the radiation field (Thornley et
al., 2000), [S IV] 10.5 ${\rm\mu m}$/[Ne III] 15.5 ${\rm\mu m}$, a surrogate
for the intensity of the radiation field (Pereira-Santaella et al. 2017),
H${}_{2}\,S(2)$ 12.3 ${\rm\mu m}$/H${}_{2}\,S(0)$ 28.2 ${\rm\mu m}$, an
indicator of gas temperature (Turner et al. 1977; Vega et al. 2010), and [S
III] 18.7 ${\rm\mu m}$/[S III] 33.5 ${\rm\mu m}$, which is sensitive to
electron density (Dale et al. 2006). Moreover, the intensity of
H${}_{2}\,S(0,1,2)$ relative to that of PAH gives an estimate of shock
strength (Roussel et al. 2007).

Table 5: Mid-IR Diagnostic Line Ratios

Galaxy |  | log $\rm\frac{[Ne\ III]}{[Ne\ II]}$ |  |  | log $\rm\frac{[S\ IV]}{[Ne\ III]}$ |  |  | log $\rm\frac{[S\ III]\,18.7}{[S\ III]\,33.5}$ |  |  | log $\frac{{\rm H_{2}}\ S(2)}{{\rm H_{2}}\ S(0)}$ |  |  | log $\frac{{\rm H_{2}}\ S(0,1,2)}{{\rm PAH\ 7.7}}$ |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---
|  Total | Inner | Outer |  Total | Inner | Outer |  Total | Inner | Outer |  Total | Inner | Outer |  Total | Inner | Outer
(1) |  (2) | (3) | (4) |  (5) | (6) | (7) |  (8) | (9) | (10) |  (11) | (12) | (13) |  (14) | (15) | (16)
IC 4710 | 0.69 | 0.74 |  $\cdots$ |  $-$0.09 |  $-$0.02 |  $\cdots$ |  $-$0.16 |  $-$0.41 |  $-$0.77 |  $\cdots$ |  $\cdots$ |  $-$0.14 |  $\cdots$ |  $\cdots$ |  $\cdots$
Mrk 33 |  $-$0.12 |  $-$0.12 |  $-$0.10 |  $-$0.45 |  $-$0.42 |  $-$0.60 | 0.06 | 0.14 | 0.03 |  $-$0.09 |  $-$0.23 | 0.11 |  $-$1.58 |  $-$2.16 |  $-$2.19
NGC 24 |  $-$0.37 |  $-$0.71 | 0.02 |  $-$0.30 |  $\cdots$ |  $\cdots$ |  $-$0.17 |  $-$0.09 |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
NGC 337 |  $-$0.37 |  $-$0.42 |  $-$0.39 |  $-$0.71 |  $-$0.49 |  $-$0.33 |  $-$0.20 |  $-$0.08 |  $-$0.09 |  $\cdots$ | 0.21 |  $\cdots$ |  $\cdots$ |  $-$1.94 |  $\cdots$
NGC 628 |  $-$0.82 |  $\cdots$ |  $\cdots$ | 0.27 |  $\cdots$ |  $\cdots$ |  $-$0.20 |  $-$0.34 | 0.06 |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
NGC 855 |  $-$0.74 | 0.11 | 0.12 | 0.31 |  $-$0.62 |  $-$0.61 |  $-$0.06 | 0.06 | 0.03 |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
NGC 925 |  $-$0.31 |  $-$0.40 |  $-$0.31 |  $-$0.49 |  $\cdots$ |  $\cdots$ |  $-$0.29 |  $-$0.12 |  $-$0.03 |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
NGC 1097 |  $-$1.07 |  $-$0.89 |  $-$1.13 |  $-$0.84 |  $-$1.11 |  $-$1.25 |  $-$0.19 | 0.01 |  $-$0.01 | 0.20 | 0.64 | 0.42 |  $-$1.69 |  $-$1.75 |  $-$1.94
NGC 1266 |  $-$0.48 |  $-$0.54 |  $-$0.33 |  $-$0.73 |  $\cdots$ |  $\cdots$ |  $-$0.53 |  $\cdots$ | 0.04 |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
NGC 1291 | 0.14 | 0.09 | 0.18 |  $-$0.63 |  $-$0.58 |  $-$0.44 |  $-$0.06 | 0.08 |  $\cdots$ | 0.11 | 0.73 |  $\cdots$ |  $-$1.09 |  $-$1.13 |  $\cdots$

Note. — For each line ratio, we give three measurements: integrated over the
central region, the inner region (central $10\arcsec\times 10\arcsec$ of low-
resolution datacube after projection), and the outer region (remaining area
with complete high-resolution observations). (This table is available in its
entirety in machine-readable form.)

Figure 6: Correlation between integrated PAH luminosity and extinction-
corrected FUV luminosity for spaxels in galaxies classified as (a) SFGs and
(b) AGNs. The data points are color-coded according to [Ne III]/[Ne II]. In
panel (b), the points with a black dot pertain to the centralmost spaxel of
the AGN. The dashed line is the best-fit linear regresson for the SFG spaxels
(Equation 1). Figure 7: (a) Spatially resolved diagnostics of PAH band ratios
6.2 ${\rm\mu m}$/7.7 ${\rm\mu m}$ versus 11.3 ${\rm\mu m}$/7.7 ${\rm\mu m}$
for spaxels in galaxies classified as SFGs (blue) and AGNs (red). Points with
a black dot pertain to the centralmost spaxel of each AGN (AGN nucleus). AGN
periphery are spaxels from AGNs that exclude the nucleus and cover the
periphery of each AGN. Panels (b) and (c) respectively show the cumulative
distribution of 6.2 ${\rm\mu m}$/7.7 ${\rm\mu m}$ and 11.3 ${\rm\mu m}$/7.7
${\rm\mu m}$ band ratios, with the median of each distribution indicated by
the corresponding short line.

##  3 Spatially Resolved Analysis

We use the spatially resolved measurements to investigate the impact of AGN
activity on PAH emission. Among the 453 spaxels resolved within the sample of
66 galaxies, 269 are extracted from the central regions of galaxies classified
as AGNs, while the remaining 184 derive from the central regions of SFGs.
Although we refer to these as AGN spaxels and SFG spaxels, respectively, we
stress that not all the AGN spaxels are necessarily strictly powered by the
central active nucleus. Apart from the centralmost spaxel on which the
published nuclear classification was based, the current lack of optical
integral-field spectroscopic observations of the same regions mapped by the
IRS prevents us from ascertaining the dominant ionization mechanism of the
non-nuclear spaxels. While the radiation field of the AGN can reach
significant physical extension depending on the power of the ionizing source
(e.g., Chen et al. 2019; Molina et al. 2022), the ionization is highly
anisotropic, and some of the peripheral spaxels could be powered by other
sources of ionization.

###  3.1 Evidence that AGNs Suppress PAH Emission

Previous work, based on integrated spectra, has revealed that PAH emission is
relatively weaker in AGN environments, plausibly due to the destruction of
small grains by their hard radiation field (e.g., Smith et al. 2007b; O’Dowd
et al. 2009). We aim to shed light on this issue using our spatially resolved
analysis. We begin by establishing the baseline level of PAH emission
associated with the star-forming spaxels in inactive galaxies. Figure 6a
confirms, as already well-established using integrated spectra (e.g., Shipley
et al. 2016; Xie & Ho 2019) and more limited explorations of spatially
resolved spectra (e.g., Maragkoudakis et al. 2018; Zhang et al. 2021), that
PAH emission correlates strongly with star formation activity. We use the
extinction-corrected FUV emission to gauge the level of ongoing star
formation, avoiding H$\alpha$, even though it is available for the SINGS
sample, to obviate contamination by nebular emission powered by AGNs in our
ensuing comparative analysis. To account for Galactic extinction, we adopt the
extinction curve of Cardelli et al. (1989) with $R_{V}=3.1$, and $E(B-V)$ from
Schlafly & Finkbeiner 2011777https://irsa.ipac.caltech.edu/applications/DUST/
(IRSA 2022), and $A_{\rm FUV}=7.9\ E(B-V)$ (Gil de Paz et al., 2007).
Correction for internal extinction follows $L_{\rm FUV}=L_{\rm FUV}^{\rm
obs}+6.0\,L_{\rm 24\,{\rm\mu m}}$ (Liu et al., 2011), where $L_{\rm FUV}^{\rm
obs}$ has already been corrected for Galactic extinction. The points are
color-coded by the [Ne III]/[Ne II] line ratio, a measure of the hardness of
the radiation field. All else being equal, a harder radiation field, which
supplies relatively more high-energy photons, produces higher [Ne III]/[Ne II]
(Thornley et al. 2000). We note that spaxels with higher [Ne III]/[Ne II] tend
to have systematically lower $L_{\rm PAH}$ at fixed $L_{\rm FUV}$, with the
highest excitation and largest PAH deficit seen in three late-type, dwarf
galaxies (IC 4710, NGC 1705, and NGC 2915), as expected from the inverse
correlation between nebular excitation and metal abundance (e.g., O’Halloran
et al. 2006; Xie & Ho 2019). A hard radiation field can destroy PAH molecules
(Gordon et al., 2008; Hunt et al., 2010; Li & Draine, 2002).

A linear regression of the SFG spaxels with the Python package linmix (Kelly
2007) yields the following relation, which has an intrinsic scatter of $\rm
0.24\ dex$:

$\displaystyle\begin{aligned} &{\rm log}\,L_{\rm PAH}=\\\ &(0.856\pm
0.022)({\rm log}\,L_{\rm FUV}-41.5)+(40.722\pm 0.021).\end{aligned}$ (1)

Consistent with the spatially resolved analysis of M 51 (Zhang et al., 2021),
the best-fitting slope is less than 1. Such a sub-linear slope may be a
consequence of PAH excitation by evolved stars in regions of low star
formation activity (Zhang & Ho, 2022).

The spaxels from the central regions of AGNs show a markedly different
behavior (Figures 6b). While the majority of the spaxels occupy a distribution
whose upper envelope closely follows the best-fit $L_{\rm PAH}-L_{\rm FUV}$
relation of SFGs, a subset of points deviates markedly below the best-fit
line, by as much as $\Delta\log\,L_{\rm PAH}\approx 2-3$ dex. The suppression
of PAH emission is generally associated with spaxels of relatively high
excitation ($\rm\log~{}[Ne\,{\small III}]/[Ne\,{\small II}]\simeq 0$), but,
interestingly, the degree of PAH suppression in AGNs can be much more extreme
than in star-forming dwarf galaxies, even though the latter can have higher
values of excitation than the former. Moreover, among the deviant AGN spaxels,
we see no systematic dependence between the degree of PAH deficit and [Ne
III]/[Ne II]. These results suggest that some factor other than the hardness
of the radiation field—one that is uniquely associated with black hole
accretion—affects the PAH properties in AGNs.

###  3.2 Evidence that AGNs Affect the PAH Band Ratios

Theoretical calculations show that the size distribution and ionization state
of PAH molecules play an important role in determining the relative strength
of their spectral features (Draine & Li, 2001, 2007). Smaller PAHs, upon
absorbing a UV photon, reach much higher levels of vibrational excitation and
radiate at shorter, more energetic wavelengths because of their low heat
capacity. On the other hand, PAHs with a larger neutral fraction generate
higher values of the 11.3 ${\rm\mu m}$/7.7 ${\rm\mu m}$ ratio, as neutral PAHs
produce stronger C–H modes responsible for the 3.3 ${\rm\mu m}$ and 11.3
${\rm\mu m}$ PAH features, while the C–C modes of cationic PAHs efficiently
emit the $6-9\ {\rm\mu m}$ PAH features (Draine & Li, 2007; Tielens, 2008).
PAH spectra also depend on the age of the stellar population responsible for
their excitation, with the 6.2 ${\rm\mu m}$/7.7 ${\rm\mu m}$ ratio dropping by
a factor $\sim 1.5$ as the population varies from a 3 Myr starburst to the
advanced evolutionary state of M 31’s bulge (Draine et al. 2021).

While to first order SFGs and AGNs share similar PAH spectral characteristics,
on closer inspection it is apparent that the spaxels of the two classes of
objects have statistically different distributions of PAH band ratios (Figure
7). Relative to the spaxels in SFGs, AGN spaxels tend to have somewhat lower
6.2 ${\rm\mu m}$/7.7 ${\rm\mu m}$ ratios and significantly stronger 11.3
${\rm\mu m}$/7.7 ${\rm\mu m}$ ratios. To quantify the statistical difference
between the band ratios of the two groups, we examine their cumulative
distributions in Figures 7b and 7c. Based on the Kolmogorov-Smirnov test with
$p<10^{-6}$, we can reject the null hypothesis that the two samples are drawn
from the same parent distribution. The stronger 11.3 ${\rm\mu m}$/7.7 ${\rm\mu
m}$ ratio is even more pronounced for the centralmost spaxel (AGN nucleus)
after we distinguish it from its peripheral spaxels, although the lower 6.2
${\rm\mu m}$/7.7 ${\rm\mu m}$ becomes less pronounced. The differences between
the band ratios of the two groups support the notion that the spectral
peculiarities of active galaxies are driven by factors related to the nuclear
region, as further explored in the following section.

Figure 8: Spatially resolved diagnostics of PAH band ratios 6.2 ${\rm\mu
m}$/7.7 ${\rm\mu m}$ and 11.3 ${\rm\mu m}$/7.7 ${\rm\mu m}$ for spaxels in
galaxies classified as (a) SFGs and (b) AGNs, color-coded according to the [Ne
III]/[Ne II] line ratios. In panel (a), the three black stars, from left to
right, give the median PAH band ratios for SFG spaxels that cover the range
$\rm log\ [Ne\,{\small III}]/[Ne\,{\small II}]<-0.7$, $\rm-0.7<log\
[Ne\,{\small III}]/[Ne\,{\small II}]<-0.5$, and $\rm log\ [Ne\,{\small
III}]/[Ne\,{\small II}]>-0.5$. In panel (b), the points with a black dot
pertain to the centralmost spaxel of the AGN. The black crosses are model
predictions by Draine & Li (2001) for neutral (top sequence) and ionized
(bottom sequence) PAHs, as well as PAHs of different sizes, with increasing
number of C atoms ($N_{\rm C}$) from right to left. Typical error bars of the
PAH band ratios are marked in the top-right of each panel.

##  4 Discussion

A central aim of this work is to investigate the extent to which the
properties of PAH emission in active galaxies differ from those in normal,
star-forming galaxies, with the hope of elucidating the factors that affect
the detailed mechanisms that govern PAH emission in extragalactic
environments, which, in turn, may provide us with new diagnostic tools to
probe physical processes in galaxy evolution. Our systematic comparison of
nearby galaxies with mapping-mode IRS observations has revealed two clear
trends between galaxies with and without an active nucleus: the presence of an
AGN tends to suppress the overall level of PAH emission (Section 3.1) and
produce subtle differences in the relative intensities of the main PAH
features (Section 3.2). Here we explore the possible physical drivers of these
observed trends.

###  4.1 Influence on PAH Band Ratios from Radiative Effects

We begin by establishing, as a baseline reference, the behavior of the PAH
band ratios 6.2 ${\rm\mu m}$/7.7 ${\rm\mu m}$ and 11.3 ${\rm\mu m}$/7.7
${\rm\mu m}$ in galaxies classified as SFGs, in response to variations in
nebular excitation as judged by the [Ne III]/[Ne II] ratio (Figure 8a)888In
this and subsequent sections, we omit a few objects whose individual PAH
features are too uncertain ($\gtrsim 0.5$ dex) to yield reliable band ratios.
In a few extreme cases, the flux of an individual PAH feature may even
formally exceed the integrated flux of all PAH emission derived from the
template decomposition. The spectra of the SFG NGC 1377 are too difficult to
measure because of strong silicon absorption (Roussel et al. 2006). We exclude
all the spaxels of the AGNs NGC 1291, 1316, 1404, 4125, 4552, 4594, and 4725,
and some spaxels of IC 4710, Mrk 33, NGC 3031 (i.e., spaxels with only
integrated PAH luminosity as shown in Table 4), because their spectra are so
starlight-dominated or so noisy that it proved impossible to measure robust
PAH band ratios. The omitted spaxels account for 12% of the sample spaxels.. A
higher value of [Ne III]/[Ne II] signifies a harder radiation field, one whose
SED has relatively more high-energy photons. We overlay theoretical tracks
from the model predictions of Draine & Li (2001) for neutral (top sequence)
and ionized (bottom sequence) PAHs of different size, as parameterized by the
number of C atoms, $N_{\rm C}$. It is immediately apparent that the spatially
resolved regions within SFGs have rather uniform spectra, as evidenced by the
fact that the majority of the spaxels are clustered around a small range of
values for the two band ratios999We excluded the spaxels from the dwarf
galaxies to provide a clearer view of the color bar.. Moreover, subtle
dependence on nebular excitation can be seen. The 6.2 ${\rm\mu m}$/7.7
${\rm\mu m}$ ratio mildly increases with increasing [Ne III]/[Ne II]. The
behavior of 11.3 ${\rm\mu m}$/7.7 ${\rm\mu m}$ is more complex: it first
appears to decrease with [Ne III]/[Ne II], and then the trend reverses, such
that the blueish and reddish points are located on the top of the diagram,
while the grayish points with $\rm log\ [Ne\,{\small III}]/[Ne\,{\small
II}]\approx-0.6$ cluster more to the bottom. To help visualize this complex
behavior, we divide the SFG spaxels into three subgroups according to their
neon line ratios, and then plot the median of the PAH band ratios for the
three groups as the black stars in Figure 8a.

How does excitation affect 6.2 ${\rm\mu m}$/7.7 ${\rm\mu m}$? A harder
illuminating spectrum contains more UV photons at higher energy and hence
directly enhances PAH emission at shorter wavelengths relative to that at
longer wavelengths (Draine et al., 2021; Rigopoulou et al., 2021). If this
were true, the 11.3 ${\rm\mu m}$/7.7 ${\rm\mu m}$ band ratio should decrease
strictly with increasing excitation, because a harder radiation field
preferentially enhances PAH emission at shorter wavelengths and would boost
the fraction of ionized PAH molecules. However, as explained above, the
variation of 11.3 ${\rm\mu m}$/7.7 ${\rm\mu m}$ with [Ne III]/[Ne II] is not
monotonic. Alternatively, a harder radiation field can modify PAH size
distribution through preferential photo-erosion of smaller PAH grains (Allain
et al. 1996a; Micelotta et al. 2010a, b; Murga et al. 2016). The normally
narrow peak size distribution of 150 C atoms for astronomical PAHs
(Weingartner & Draine, 2001a; Draine & Li, 2007) would shift with photo-
erosion toward sizes of fewer than 100 C atoms (with total amount of smaller
PAHs reduced as well). This effect also can potentially account for the
increase of 6.2 ${\rm\mu m}$/7.7 ${\rm\mu m}$, but it cannot explain why 11.3
${\rm\mu m}$/7.7 ${\rm\mu m}$ does not simultaneously increases in lockstep.
The 11.3 ${\rm\mu m}$/7.7 ${\rm\mu m}$ band ratio first decreases and then
increases with [Ne III]/[Ne II]. To account for this peculiar behavior, we
propose that, during the photo-erosion process, a harder radiation field first
elevates the ionization fraction of the PAH molecules up to a certain
threshold, which results in the decrease of 11.3 ${\rm\mu m}$/7.7 ${\rm\mu
m}$, beyond which photo-erosion proceeds to dominate by modifying the PAH size
distribution, which results in the increase of 6.2 ${\rm\mu m}$/7.7 ${\rm\mu
m}$ and 11.3 ${\rm\mu m}$/7.7 ${\rm\mu m}$. In addition, preferential photo-
destruction of the ionized PAH grains, which are more vulnerable relative to
the neutral ones (Allain et al. 1996b; Holm et al. 2011), also may contribute
to the increase of 11.3 ${\rm\mu m}$/7.7 ${\rm\mu m}$. This scenario further
provides a plausible explanation for the severe depletion of the overall PAH
emission among the dwarf galaxies with the hardest radiation field (Figure
6a).

By sharp contrast, AGN spaxels occupy a much broader distribution of PAH band
ratios (Figure 8b), displaying characteristically larger 11.3 ${\rm\mu m}$/7.7
${\rm\mu m}$ but smaller 6.2 ${\rm\mu m}$/7.7 ${\rm\mu m}$ compared to the SFG
population. No clear correlation exists with [Ne III]/[Ne II], except for the
slight tendency for the highest 11.3 ${\rm\mu m}$/7.7 ${\rm\mu m}$ ratios to
be associated with the most enhanced [Ne III]/[Ne II], most notably for the
data points that exceed the upper boundary of model predictions by Draine & Li
(2001). Given the same range of [Ne III]/[Ne II], the distributions of PAH
band ratios are significantly different for the AGN spaxels compared to the
SFG spaxels. While the overall population shift toward lower 6.2 ${\rm\mu
m}$/7.7 ${\rm\mu m}$ and higher 11.3 ${\rm\mu m}$/7.7 ${\rm\mu m}$ can be
broadly understood in terms of a grain population biased in favor of larger
species (Draine & Li, 2007; Draine et al., 2021), the absence of clear trends
with excitation suggests that radiative effects alone cannot fully explain the
PAH characteristics of the AGN spaxels. Similarly, although the relative
fraction of ionized versus neutral PAH molecules can affect the band ratios,
we also regard variation in ionization fraction to be an unlikely culprit for
the observed spectral differences between active and inactive galaxies, for
the simple fact that the harsher environment anticipated in the vicinity of an
AGN should produce more ionized PAHs, and hence weaker—not stronger—11.3
${\rm\mu m}$/7.7 ${\rm\mu m}$. We expect AGN spaxels to gravitate toward the
theoretical sequence for ionized PAHs instead of the one for neutral PAHs
(bottom instead of top of Figure 8b).

Figure 9: The PAH band ratios 6.2 ${\rm\mu m}$/7.7 ${\rm\mu m}$ and 11.3
${\rm\mu m}$/7.7 ${\rm\mu m}$ for AGN spaxels that have measurements of [S
IV]/[Ne III], H${}_{2}\,S(2)$/H${}_{2}\,S(0)$, and [S III] 18.71 ${\rm\mu
m}$/[S III] 33.48 ${\rm\mu m}$, color-coded according to the ionization
parameter calculated based on these line ratios. Figure 10: The PAH band
ratios 6.2 ${\rm\mu m}$/7.7 ${\rm\mu m}$ and 11.3 ${\rm\mu m}$/7.7 ${\rm\mu
m}$ for AGN spaxels, color-coded according to (a) the line ratio
H${}_{2}\,S(0,1,2)$ / PAH 7.7 ${\rm\mu m}$, which is an indicator of shock
strength, and (b) the combination of H${}_{2}\,S(0,1,2)$ / PAH 7.7 ${\rm\mu
m}\times\rm[Ne\,{\small III}]/[Ne\,{\small II}]$, which is an indicator of the
combined effects of shocks and radiative effects.  Figure 11: Illustration of
the PAH decomposition for (a) NGC 4736 and (b) NGC 3351. NGC 4736 exhibits
stronger PAH emission at 11.3 ${\rm\mu m}$ and longer wavelengths than NGC
3351.

Nevertheless, we examine whether ionization effects can produce the stronger
11.3 ${\rm\mu m}$/7.7 ${\rm\mu m}$ ratios for the AGN spaxels. The ionization
balance is conventionally described by $G_{0}T^{1/2}/n_{e}$, where $G_{0}$ is
the intensity of the radiation field, $T$ is the gas temperature, and $n_{e}$
is the electron density (Bakes & Tielens, 1994; Weingartner & Draine, 2001b).
We compute the modified quantity $G_{0}^{*}T^{*}/n_{e}^{*}$ as an
observational proxy for the ionization parameter.101010Parameters denoted by *
serve as an empirical surrogate for the actual physical parameters. We
represent $G_{0}^{*}$ using [S IV] 10.5 ${\rm\mu m}$/[Ne III] 15.5 ${\rm\mu
m}$, a line ratio that tightly correlates with the dimensionless ionization
parameter (Pereira-Santaella et al., 2017). In light of the scaling relation
between gas temperature and the theoretically calculated intensity ratio of
the high-energy to low-energy rotation-vibration transitions of molecular
hydrogen (Turner et al., 1977; Vega et al., 2010), we assume that $T^{*}$ is
proportional to H${}_{2}\,S(2)$ 12.3 ${\rm\mu m}$/H${}_{2}\,S(0)$ 28.2
${\rm\mu m}$. Finally, we use [S III] 18.7 ${\rm\mu m}$/[S III] 33.5 ${\rm\mu
m}$ to approximate $n_{e}^{*}$, as these two fine-structure lines are density-
sensitive because they come from the same ion but have different critical
densities (Dale et al., 2006; Pereira-Santaella et al., 2017). Figure 9 shows
the distribution of PAH band ratios color-coded according to the modified
ionization parameter. This result strongly conflicts with the actual
observations, which show that the regions with high ionization parameter
actually exhibit large 11.3 ${\rm\mu m}$/7.7 ${\rm\mu m}$ ratios, more
consistent with neutral instead of ionized PAHs.

###  4.2 Influence on PAH Band Ratios from Mechanical Effects

Having failed to establish a clear link between the PAH characteristics of AGN
spaxels and radiative effects, we turn our attention to the possible role of
mechanical effects from shocks, which can effectively destroy smaller PAH
molecules and lead to strong variations in the strength of the $5-8\ {\rm\mu
m}$ PAH features in AGNs (Diamond-Stanic & Rieke, 2010; Micelotta et al.,
2010a, b). Roussel et al. (2007) found that the intensities of the IR H2 lines
scale tightly with PAH emission over a large range of radiation field
intensity, conceivably because both tracers originate predominantly in either
dense or diffuse photo-dissociation regions (PDRs). Moreover, Roussel et al.
proposed that shock heating contributes to an excess of IR molecular hydrogen
emission. Figure 10a showcases the PAH band ratios of the spaxels extracted
from active galaxies whose central region has reliable measurements of the
H${}_{2}\,S(0)$, H${}_{2}\,S(1)$, and H${}_{2}\,S(2)$ emission lines, where
now the color encodes the diagnostic ratio H${}_{2}\,S(0,1,2)$/PAH 7.7 used to
indicate shock strength. Here, H${}_{2}\,S(0,1,2)$ denotes the sum of all
three molecular hydrogen vibrational lines. A distinctly bimodal distribution
can be seen. For the AGN spaxels with 11.3 ${\rm\mu m}$/7.7 ${\rm\mu m}$
$\lesssim 0.3$ (as well as the SFG spaxels not shown here), the
H${}_{2}\,S(0,1,2)$/PAH 7.7 ratios are consistent with the theoretical values
expected from PDRs (Guillard et al. 2012; Stierwalt et al. 2014). However, for
most of the AGN spaxels with larger 11.3 ${\rm\mu m}$/7.7 ${\rm\mu m}$, their
H${}_{2}\,S(0,1,2)$/PAH 7.7 ratios greatly exceed the threshold found in PDRs,
$\rm log(H_{2}/PAH)=-1.4$. We propose that the elevated
H${}_{2}\,S(0,1,2)$/PAH 7.7 ratios originate from shocks, and that the direct
destruction of small PAHs by shocks results in the larger 11.3 ${\rm\mu
m}$/7.7 ${\rm\mu m}$ and smaller 6.2 ${\rm\mu m}$/7.7 ${\rm\mu m}$ ratios
observed in LLAGNs.

NGC 4736, a type 2 LINER (Ho et al., 1997a) notable for its UV-bright nucleus
(Maoz et al., 1995) and recent burst of star formation (Taniguchi et al.,
1996), stands out as a highly instructive outlier on account of its
exceptionally strong 11.3 ${\rm\mu m}$/7.7 ${\rm\mu m}$ ratio and yet
relatively weak shock strength. At the same time, this galaxy has the hardest
radiation field among the AGN regions shown in Figure 8b. Figure 11 highlights
the unique PAH characteristics of NGC 4736, in contrast to NGC 3351, which has
the lowest [Ne III]/[Ne II] ratio in the sample, one that is more than 10
times lower than in NGC 4736. This example illustrates that when mechanical
effects are sub-dominant, a hard radiation field, whether it originates from
star formation or AGN accretion, produces large 11.3 ${\rm\mu m}$/7.7 ${\rm\mu
m}$ and 6.2 ${\rm\mu m}$/7.7 ${\rm\mu m}$ by the selective photo-erosion and
photo-destruction of PAH molecules (see Section 4.1).

We surmise that in general both radiative and mechanical effects operate to
establish the spectral diversity of PAH emission in AGNs. The relative
proportion of the two mechanisms, which depends on the specific attributes of
individual systems and the intrinsic diversity in the composition of any
galaxy sample, then determines the observed scatter. This hypothesis is
strongly supported in Figure 10b, where now the color coding represents a new
quantity that combines the joint effects of radiation field hardness and
shocks. A striking result emerges: the bimodal distribution of the PAH band
ratios becomes even more distinctive than either effect alone. In agreement
with previous studies (e.g., Diamond-Stanic & Rieke 2010; Micelotta et al.
2010a, b), we find that small grains, which emit more strongly at shorter
wavelengths, are the most vulnerable to destruction by shocks. Our current
work reveals that radiative effects may serve an important supporting role, by
contributing to the ionization and erosion of the PAH molecules and thereby
making them even more vulnerable to destruction by shocks.

Figure 12: Statistics of mid-IR diagnostic line ratios as measured in the
centralmost, inner ($10\arcsec\times 10\arcsec$) region compared to those
measured in the remaining outer region. The line ratio H${}_{2}\,S(0,1,2)$/PAH
7.7 ${\rm\mu m}$ is sensitive to shocks, while the combined diagnostic
H${}_{2}\,S(0,1,2)$/PAH 7.7 ${\rm\mu m}\times\rm[Ne\,{\small
III}]/[Ne\,{\small II}]$ reflects the joint influence of shocks and radiative
effects. This plot illustrates that these indicators are even more prominent
in the inner region. Figure 13: The PAH band ratios 6.2 ${\rm\mu m}$/7.7
${\rm\mu m}$ and 11.3 ${\rm\mu m}$/7.7 ${\rm\mu m}$ for AGN spaxels that have
measurements of the strength of nuclear activity, color-coded according to the
(a) bolometric luminosity ($L_{\rm bol}$) and (b) Eddington ratio ($L_{\rm
bol}/L_{\rm Edd}$) of the AGN.  Figure 14: Dependence of PAH deficit of AGN
spaxels, defined as the offset between the observed integrated ($5-20\ {\rm\mu
m}$) PAH luminosity and that predicted from the extinction-corrected FUV
luminosity based on the $L_{\rm PAH}-L_{\rm FUV}$ relation of SFG spaxels
(Figure 6; Equation 1) versus (a) shock strength, H${}_{2}\ S(0,1,2)$/PAH 7.7
${\rm\mu m}$, and (b) the combined effects of shocks and radiative excitation,
H${}_{2}\ S(0,1,2)$/PAH 7.7 ${\rm\mu m}\times\rm[Ne\,{\small
III}]/[Ne\,{\small II}]$. The points with a black dot pertain to the
centralmost spaxel of each AGN.

We reiterate an important caveat that applies to all the results discussed
throughout this section. The physical diagnostics (i.e., for excitation,
ionization parameter, and shocks) deduced from the high-resolution IRS
observations are available only for a portion of the total area for which PAH
emission was mapped in low-resolution mode. We assume that the physical
diagnostics extracted from the central region of each galaxy applies to all
the spaxels spanning a larger area. While we cannot verify in detail whether
this assumption is correct, we can perform a rough comparison of the physical
diagnostics over two crudely divided spatial scales, the innermost
$10\arcsec\times 10\arcsec$ region versus the remaining outer, flanking
region, to validate that the unique PAH characteristics of LLAGNs arise from
physical processes relevant to the active nucleus. Figure 12 shows that both
diagnostics (sensitive to shocks and to the combined effects of shocks and
radiative excitation) are statistically stronger toward the inner region of
the galaxy, suggesting that they indeed appear to be associated intimately
with the active nucleus.

Our discussion has focused on the role of AGNs in reducing the strength of PAH
emission or in modifying its detailed relative band ratios. In principle, the
AGN itself can also excite PAH molecules (Jensen et al. 2017), raising concern
over the reliability of SFRs in AGNs inferred from PAH emission, especially as
applied to individual features such as the 11.3 ${\rm\mu m}$ band (e.g.,
Diamond-Stanic & Rieke 2010; Alonso-Herrero et al. 2014; Esquej et al. 2014).
We note, however, that biases incurred from the selective suppression and/or
enhancement of individual PAH features can be mitigated by using SFR
calibrations based on the integrated PAH emission instead of the strength of
any single band (e.g., Xie & Ho 2019). Integrated PAH emission appears to
yield unbiased SFRs even for AGNs powerful enough to qualify as quasars (Xie
et al., 2021).

###  4.3 Implications for AGN Feedback

Having shown that the environment around active galaxies leaves an imprint on
the PAH spectrum, we next investigate whether we can establish a more direct
link with the physical properties of the AGN. No clear trends can be seen with
either AGN bolometric luminosity or Eddington ratio111111For ease of
presentation, four sources with upper limits in $L_{\rm bol}$ are omitted from
the plot, but we have verified that excluding them does not change our
conclusions., defined as $L_{\rm bol}/L_{\rm Edd}$, with $L_{\rm
Edd}=1.26\times 10^{38}\,(M_{\rm BH}/M_{\odot})$ (Figure 13). As with the
majority of the nearby galaxy population (Ho, 2008), the SINGS sample contains
almost exclusively AGNs of extremely low luminosities ($L_{\rm bol}\approx
10^{39}-10^{42}$ $\rm erg\ s^{-1}$) and Eddington ratios ($L_{\rm bol}/L_{\rm
Edd}\approx 10^{-6}-10^{-3}$). Such highly sub-Eddington systems, dominated by
LINERs, have tiny mass accretion rates and extremely low radiative efficiency
(Ho et al. 2003; Ho 2009). As evidenced by their unique SEDs (i.e., Ho 1999;
Di Matteo et al. 2003; Pellegrini et al. 2003) and in the inverse correlation
between radio-loudness and Eddington ratio (Ho 2002, 2008; Terashima & Wilson
2003; Sikora et al. 2007), LLAGNs redirect most of their accretion power from
radiation to kinetic energy. Interestingly, the AGN spaxels with the larger
11.3 ${\rm\mu m}$/7.7 ${\rm\mu m}$ ratios—those that we consider to show the
most convincing evidence for shocks (Section 4.2)—coincide with LINERs that
have among the lowest values of $L_{\rm bol}/L_{\rm Edd}$. In short, we are
witnessing the consequences of mechanical energy injection into the
interstellar medium, presumably associated with jets and outflows launched by
radiatively inefficient accretion flows (Yuan & Narayan, 2014). Distinct from
luminous, highly accreting supermassive BHs, whose radiative energy
efficiently expels the surrounding gas through “quasar-mode” feedback (e.g.,
Di Matteo et al. 2005; Hopkins et al. 2008), low-level nuclear activity heats
the gas through shocks (e.g., McNamara & Nulsen 2007). This form of “kinetic-
mode” feedback has been widely embraced in recent cosmological simulations of
galaxy evolution (Weinberger et al. 2017; Davé et al. 2019). Here, we suggest
that kinetic-mode is directly responsible for the destruction of small PAH
molecules.

It is illuminating to compare directly our sample of LLAGNs with powerful
AGNs, where quasar-mode feedback might operate. The recent study of Xie & Ho
(2022) finds that low-redshift quasars also exhibit distinct characteristics
in their PAH spectrum, but their properties differ from those of the sources
considered here. As with LLAGNs, quasars also have smaller values of 6.2
${\rm\mu m}$/7.7 ${\rm\mu m}$ compared to SFGs, but their differences are even
more extreme (median value of 0.09 for quasars versus 0.25 for LLAGNs; see
Figure 8 of Xie & Ho 2022). Most notably, quasars in general have 11.3
${\rm\mu m}$/7.7 ${\rm\mu m}$ ratios that are quite similar to those of SFGs,
and lie close to the theoretical sequence of ionized PAHs with $N_{\rm
C}\gtrsim 1000$, much larger than attained in LLAGNs. Although the analysis of
the quasars was based on globally integrated instead of spatially resolved
spectra, quasars seem to lack the very large values of 11.3 ${\rm\mu m}$/7.7
${\rm\mu m}$ reached by LLAGNs. If, as we argue, the highly elevated values of
11.3 ${\rm\mu m}$/7.7 ${\rm\mu m}$ are indicative of shocks, we are tempted to
conclude that shocks are less prevalent in quasars than in LLAGNs. While in
both situations the PAH size distribution is depleted of smaller grains, the
underlying causes differ, and the PAH size distribution of quasars shifts
further toward larger grains. We argue that shocks directly destroy small
grains in LLAGNs, especially the ionized grains, but the powerful radiation
field in quasars deplete more small grains and keep the surviving larger
grains ionized.

Lastly, apart from the above subtle variations imparted on the PAH spectrum,
the two extremes of AGN activity also manifest themselves differently in the
degree to which they impact the overall level of PAH emission. Among LLAGNs,
the PAH suppression becomes most severe at $L_{\rm FUV}\lesssim 10^{42}$ $\rm
erg\ s^{-1}$ (Figure 6b), which corresponds to ${\rm SFR}\approx 0.05$ $\rm
M_{\odot}\ yr^{-1}$ (Hao et al., 2011), reaching values as extreme as
$\Delta\log\,L_{\rm PAH}\approx-3$ dex. The magnitude of the PAH deficit
increases with shock strength, albeit with large scatter (Figure 14a), and
especially in response to the joint effect of shocks and radiation (Figure
14b). By stark contrast, quasars only begin to exhibit a PAH deficit among the
most luminous sources ($L_{\rm bol}\gtrsim 10^{46}$ $\rm erg\ s^{-1}$) with
the highest levels of star formation activity (${\rm SFR}\gtrsim 50$ $\rm
M_{\odot}\ yr^{-1}$; see Figure 6 in Xie & Ho 2022). And even then, the amount
of PAH reduction is only $\Delta\log\,L_{\rm PAH}\lesssim-0.5$ dex, markely
milder than in LLAGNs.

##  5 Summary

We apply the methodology of Zhang et al. (2021) to perform a comprehensive
study of the spatially resolved mid-IR Spitzer spectra of 66 nearby galaxies
for which mapping-mode IRS observations are available from the SINGS survey.
Comprisingly roughly half in low-luminosity AGNs, the SINGS sample is ideal to
investigate the effects of weak AGN activity on the properties of PAH
emission. When combined with the recent results of Xie & Ho (2022), we aim to
use PAH emission as a diagnostic of AGN-related energy feedback on the
interstellar medium, covering an unprecedented dynamic range in BH accretion
rate, from the most feeble LINERs to powerful quasars. Combining the low-
resolution spectra with near-IR and mid-IR images, we obtain robust SEDs that
allow us to derive measurements of the total integrated ($5-20\,{\rm\mu m}$)
PAH emission, as well as the emission from the individual 6.2, 7.7, and 11.3
${\rm\mu m}$ bands. Although more limited in spatial coverage, the high-
resolution spectra furnish ionic fine-structure lines and molecular hydrogen
lines that provide valuable physical diagnostics.

Our main conclusions are as follows:

  1. 1.

In star-forming regions, the integrated PAH emission correlates tightly with
the extinction-corrected FUV luminosity, with an intrinsic scatter of $\rm
0.24$ dex. Although the sub-linear correlation suggests that PAHs may be
excited more than just young stars, the tight correlation confirms that PAH
emission serves as an effective SFR indicator in star-forming environments.

  2. 2.

Relative to the star-forming regions, the spatially resolved regions of low-
luminosity AGNs, especially those with highly sub-Eddington luminosities, have
a tendency to exhibit weaker PAH emission for a given FUV luminosity, as well
as on average smaller 6.2 ${\rm\mu m}$/7.7 ${\rm\mu m}$ and larger 11.3
${\rm\mu m}$/7.7 ${\rm\mu m}$ PAH band ratios.

  3. 3.

Radiative effects alone cannot account for the observed trends. Instead,
shocks, through the selective destruction of small grains, are plausibly the
dominant agent responsible for the unique PAH spectra of low-luminosity AGNs,
although radiative effects may also contribute a secondary role.

  4. 4.

By comparison, quasars only suppress PAH emission by a modest level at very
high luminosities, and their PAH band ratios reflect the influence of the
strong radiation field in ionizing and destroying the grains.

  5. 5.

The contrasting PAH characteristics of quasars and low-luminosity AGNs can
serve as a potential diagnostic to differentiate the effects of quasar-mode
versus kinetic-mode AGN feedback.

We thank the anonymous referee for helpful comments and suggestions. This work
was supported by the National Science Foundation of China (11721303,
11991052), China Manned Space Project (CMS-CSST-2021-A04, CMS-CSST-2021-A06),
and the National Key R&D Program of China (2016YFA0400702).

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