# AGN Selection and Demographics: A New Age with JWST/MIRI

Jianwei Lyu (吕建伟) Steward Observatory, University of Arizona, 933 North Cherry
Avenue, Tucson, AZ 85721, USA  Stacey Alberts Steward Observatory, University
of Arizona, 933 North Cherry Avenue, Tucson, AZ 85721, USA  George H. Rieke
Steward Observatory, University of Arizona, 933 North Cherry Avenue, Tucson,
AZ 85721, USA  Irene Shivaei Steward Observatory, University of Arizona, 933
North Cherry Avenue, Tucson, AZ 85721, USA  Centro de Astrobiología (CAB),
CSIC-INTA, Ctra. de Ajalvir km 4, Torrejón de Ardoz, E-28850, Madrid, Spain
Pablo G. Pérez-González Centro de Astrobiología (CAB), CSIC-INTA, Ctra. de
Ajalvir km 4, Torrejón de Ardoz, E-28850, Madrid, Spain  Fengwu Sun Steward
Observatory, University of Arizona, 933 North Cherry Avenue, Tucson, AZ 85721,
USA  Kevin N. Hainline Steward Observatory, University of Arizona, 933 North
Cherry Avenue, Tucson, AZ 85721, USA  Stefi Baum Department of Physics and
Astronomy, University of Manitoba, Winnipeg, MB R3T 2N2, Canada  Nina
Bonaventura Steward Observatory, University of Arizona, 933 North Cherry
Avenue, Tucson, AZ 85721, USA  Andrew J. Bunker Department of Physics,
University of Oxford, Denys Wilkinson Building, Keble Road, Oxford OX1 3RH, UK
Eiichi Egami Steward Observatory, University of Arizona, 933 North Cherry
Avenue, Tucson, AZ 85721, USA  Daniel J. Eisenstein Center for Astrophysics
$|$ Harvard & Smithsonian, 60 Garden St., Cambridge MA 02138 USA  Michael
Florian Steward Observatory, University of Arizona, 933 North Cherry Avenue,
Tucson, AZ 85721, USA  Zhiyuan Ji Steward Observatory, University of Arizona,
933 North Cherry Avenue, Tucson, AZ 85721, USA  Benjamin D. Johnson Center for
Astrophysics $|$ Harvard & Smithsonian, 60 Garden St., Cambridge MA 02138 USA
Jane Morrison  Steward Observatory, University of Arizona, 933 North Cherry
Avenue, Tucson, AZ 85721, USA  Marcia Rieke Steward Observatory, University of
Arizona, 933 North Cherry Avenue, Tucson, AZ 85721, USA  Brant Robertson
Department of Astronomy and Astrophysics University of California, Santa Cruz,
1156 High Street, Santa Cruz CA 96054 USA  Wiphu Rujopakarn Department of
Physics, Faculty of Science, Chulalongkorn University, 254 Phayathai Road,
Pathumwan, Bangkok 10330, Thailand  National Astronomical Research Institute
of Thailand, 260 Moo 4, T. Donkaew, A. Maerim, Chiangmai 50180, Thailand
Sandro Tacchella Kavli Institute for Cosmology, University of Cambridge,
Madingley Road, Cambridge CB3 0HA, UK  Cavendish Laboratory, University of
Cambridge, 19 JJ Thomson Avenue, Cambridge CB3 0HE, UK  Jan Scholtz  Kavli
Institute for Cosmology, University of Cambridge, Madingley Road, Cambridge
CB3 0HA, UK  Cavendish Laboratory, University of Cambridge, 19 JJ Thomson
Avenue, Cambridge CB3 0HE, UK  Christopher N. A. Willmer Steward Observatory,
University of Arizona, 933 North Cherry Avenue, Tucson, AZ 85721, USA

(Received Oct 18, 2023)

###### Abstract

Understanding the co-evolution of supermassive black holes (SMBHs) and their
host systems requires a comprehensive census of active galactic nuclei (AGN)
behavior across a wide range of redshift, luminosity, obscuration level and
galaxy properties. We report significant progress with JWST towards this goal
from the Systematic Mid-infrared Instrument Legacy Extragalactic Survey
(SMILES). Based on comprehensive SED analysis of 3273 MIRI-detected sources,
we identify 217 AGN candidates over a survey area of $\sim$34 arcmin2,
including a primary sample of 111 AGNs in normal massive galaxies
($M_{*}>10^{9.5}~{}M_{\sun}$) at $z\sim$0–4, an extended sample of 86 AGN
candidates in low-mass galaxies ($M_{*}<10^{9.5}~{}M_{\sun}$) and a high-$z$
sample of 20 AGN candidates at $z\sim$4–8.4. Notably, about 80% of our MIRI-
selected AGN candidates are new discoveries despite the extensive pre-JWST AGN
searches. Even among the massive galaxies where the previous AGN search is
believed to be thorough, 34% of the MIRI AGN identifications are new,
highlighting the impact of obscuration on previous selections. By combining
our results with the efforts at other wavelengths, we build the most complete
AGN sample to date and examine the relative performance of different selection
techniques. We find the obscured AGN fraction increases from $L_{\rm
AGN,bol}\sim 10^{10}~{}L_{\odot}$ to $10^{11}~{}L_{\odot}$ and then drops
towards higher luminosity. Additionally, the obscured AGN fraction gradually
increases from $z\sim 0$ to $z\sim 4$ with most high-$z$ AGNs obscured. We
discuss how AGN obscuration, intrinsic SED variations, galaxy contamination,
survey depth and selection techniques complicate the construction of a
complete AGN sample.

dust, extinction — galaxies: active — galaxies: Seyfert — infrared: galaxies —
quasars: general

††journal: ApJ

##  1 Introduction

Supermassive black holes (SMBHs) are found at the centers of most, if not all,
galaxies and are thought to be intimately linked to the evolution of galaxies
across cosmic time (see reviews by e.g., Alexander & Hickox, 2012; Kormendy &
Ho, 2013). The formation and growth of SMBHs are regulated by the supply of
accreting material with the efficiency influenced by various host galaxy
properties at a wide range of physical scales (e.g., Hopkins et al., 2008;
Hopkins & Quataert, 2010; Inayoshi et al., 2020). As SMBHs accrete matter,
they release enormous amounts of energy in the form of radiation and powerful
outflows that shape the host systems through feedback mechanisms (e.g.,
Fabian, 2012; Heckman & Best, 2014). How to unravel the nature of this SMBH-
galaxy connection has been a major quest in extragalactic astronomy. The
growing phase of SMBHs, commonly known as Active Galactic Nuclei (AGNs), is
particularly interesting, as they represent the most active phase of the SMBH-
galaxy interaction and have distinctive observational characteristics to
separate them out from other objects.

Since the original identifications through optical spectroscopy by Seyfert
(1943), extensive ground-based optical surveys have successfully yielded
statistical samples of AGN over wide ranges of galaxy properties and cosmic
time (e.g., Adams, 1977; Filippenko & Sargent, 1985; Ho et al., 1997; Richards
et al., 2002; Kauffmann et al., 2003; Hao et al., 2005). Meanwhile, a large
number of AGNs missed in the optical have been revealed by new techniques
developed at other wavelengths such as radio (e.g., Edge et al., 1959), X-rays
(e.g., Gursky et al., 1971) and ultraviolet excess (e.g., Markarian, 1977).
Nevertheless, all these methods have limitations — only a minority of AGNs are
prominent in the radio and the other techniques are significantly hampered by
absorption by the dust and gas surrounding the AGN. Luckily, most of the
luminosity of obscured AGNs emerges in the mid-IR, producing SEDs dominated by
emission by warm dust. To mitigate the deficiencies of AGN selections at other
wavelengths, various AGN infrared selection techniques, particularly mid-IR
color diagnostics with Spitzer and WISE data have been developed (e.g., Lacy
et al., 2004; Stern et al., 2005; Alonso-Herrero et al., 2006; Padovani et
al., 2017; Hickox & Alexander, 2018; Hviding et al., 2018), However, these
methods are mostly effective on power law SEDs (Donley et al., 2012), i.e., on
lightly obscured sources, and they leave the heavily obscured population only
partially explored (see reviews by Hickox & Alexander 2018; Lyu & Rieke
2022a). In addition, the high-$z$ obscured and faint AGN population (e.g.,
$z\gtrsim 2$) is even less explored given the very limited wavelength coverage
and sensitivity beyond $\sim$8 $\mu$m. Consequently, there is wide
disagreement in estimates of the fraction of the AGN population that has been
overlooked due to strong obscuration, from 10% (Mendez et al., 2013) to 30%
(Del Moro et al., 2016) to much more (Ananna et al., 2019), and how it evolves
with AGN and/or galaxy properties. It is a bit embarrassing to admit that
despite an odyssey of $\sim$80 years, our current AGN census is still patchy
and incomplete, resulting in a major challenge to reach a comprehensive
picture of SMBH evolution, and its connections with the host galaxy and
surrounding environment.

With the successful launch and operation of JWST, we have entered a new era.
Notably, from NIRSpec and NIRCam spectral observations at $\lambda<5{\rm\mu
m}$, people have found a number of high-$z$ AGNs or AGN candidates through
rest-frame UV-optical emission line properties (e.g., Harikane et al., 2023;
Kocevski et al., 2023; Larson et al., 2023; Übler et al., 2023) with the
highest records at $z\sim 10$ (Maiolino et al., 2023; Goulding et al., 2023).
Since most of these searches target preferentially the broad-line systems that
do not represent the whole AGN population, it is unclear how many AGNs have
been missed. Previous systematic searches of obscured AGNs in the IR have been
handicapped because of the very limited number of spectral bands with relevant
data past $\sim$ 8 $\mu$m, leaving the SED models under-constrained. This
obstacle has been lifted by the Mid-Infrared Instrument (MIRI; Wright et al.
2023) on JWST, which provides nine photometric bands from 5 to 26 $\mu$m that
continuously sample the AGN hot dust emission features ($\lambda_{\rm
peak}\geq 3\mu$m) up to redshift $z\sim 8$. Compared to the Spitzer mission,
the MIRI sensivities are about 10–100 times higher with about eight times
better spatial resolution at similar wavelengths, allowing the search and
characterization of the AGN population in unprecedented precision, depth and
redshift range (e.g., Yang et al., 2023)

In this paper, we present the first results of AGN selection and demographics
using the Systematic Mid-infrared Instrument Legacy Extragalactic Survey
(SMILES; Rieke et al. 2017) led by the US MIRI Science Team. This survey is
composed of 3$\times$5 MIRI pointings in 8 imaging bands at $\lambda\sim$5–27
${\rm\mu m}$ over the central region of the Great Observatories Origins Deep
Survey–South (GOODS-S; Giavalisco et al. 2004), including the Hubble Ultra
Deep Field (HUDF; Beckwith et al. 2006) (see Figure 1 for the survey footprint
and sensitivities). In the pre-JWST era, this field was extensively observed
across the electromagnetic spectrum, often with the deepest observations
anywhere in the whole sky, offering the best ancillary dataset for AGN study.
In the UV to the near-IR, it has been visited multiple times by the Hubble
Space Telescope (HST) as well as many ground-based facilities that include
both imaging and spectral observations. AGNs have been identified through
emission line features (e.g., Santini et al., 2009; Silverman et al., 2010) or
time variability (e.g., Pouliasis et al., 2019). In the X-ray, this field is
covered as part of the Chandra Deep Field South (CDF-S; Giacconi et al. 2002)
with a total exposure time about 7 Ms at 0.5–7 keV, enabling the
identification of many X-ray detected AGNs (Luo et al., 2017). In the radio-
band, super deep data at 3 GHz and 6 GHz with the Jansky Very Large Array
(JVLA) have also been obtained and used in AGN searches (Alberts et al.,
2020). In the infrared, very deep IRAC and confusion-limited MIPS 24 $\mu$m
images from Spitzer are available and have been utilized to identify AGNs by
mid-IR colors (e.g., Donley et al., 2008) and through SED fitting (Alberts et
al., 2020; Lyu et al., 2022a). These multi-wavelength datasets and the
corresponding results provide the best benchmark to show the advances brought
by JWST and to characterize the relative performance of the MIRI AGN
selection.

Figure 1: Survey layout from X-ray to radio (left) and the 5$\sigma$ flux
limits of photometric bands at 0.3 – 26 $\mu$m (right) used for SED fittings
in GOODS-S/HUDF. On the left, the footprint of our MIRI survey is shown as the
red-shaded region with JADES NIRCam deep (solid green line) and medium (dashed
green line) observations, FRESCO NIRCam/grism coverage (solid blue line), HST
ACS GOODS-S coverage (thick gray line), Chandra X-ray coverage (light blue
shaded region) and JVLA radio observations at 3 GHz (dark green lines) and 6
GHz (dark orange lines). On the right, we denote the pre-JWST filters in gray,
JWST/NIRCam filters in green and JWST/MIRI filters in red. Besides the flux
limits of these filters as a function of wavelength, we also show some
representative SEDs of obscured AGNs (orange), unobscured AGNs (blue) and
starburst galaxies (magenta) at $z$=2 and $z$=4.

Besides the pre-JWST multi-wavelength dataset, our MIRI survey also has almost
full overlap with the deep NIRCam imaging observations from the JWST Advanced
Deep Extragalactic Survey (JADES; Eisenstein et al. 2023) and a large overlap
with spectroscopic data from the First Reionization Epoch Spectrosopic
COmplete Survey (FRESCO; Oesch et al. 2023), which can help to reveal the
nature of these MIRI sources and characterize their properties. Compared to
other MIRI imaging surveys in JWST cycles 1 and 2 (e.g., CEERS, COSMOS-Web),
SMILES has larger sky coverage and/or more complete wavelength coverage.
Combined with very deep JWST/NIRCam and HST data, this MIRI dataset provides a
unique opportunity for AGN study over a wide range of redshifts and
properties.

Taking advantage of the deep JWST (and HST) photometric data that continuously
covers from 0.4 to 26 $\mu$m, we will conduct comprehensive SED analyses to
identify AGNs with a wide range of luminosity and obscuration levels from
$z\sim 0$ to $z\sim 8$. We will demonstrate the new discovery space offered by
MIRI and present a major leap in AGN selection compared to all the previous IR
missions. Combined with the super deep X-ray and radio data in the same field,
we are able to build the most complete AGN sample in terms of bolometric
luminosity and discuss the limits of different wavelengths and selection
techniques. These results can establish the foundation of a number of follow-
up investigations on AGN-related science such as AGN obscuration, luminosity
functions, AGN duty cycles, host galaxy properties and AGN-galaxy relations.

This paper is structured as follows. Section 2 describes the observations,
data reduction and photometry measurements. In Section 3, we introduce the AGN
selection methods and present the results, which include SED identifications
and other techniques with and without using the JWST data. In Section 4, we
report the AGN number densities from MIRI, compare them to the AGN selected at
other wavelengths and characterize the relative performance of different
selection techniques across the electromagnetic spectrum. Section 5
characterizes the obscured AGN fraction from SED analysis and studies its
possible evolution. The nature of AGNs missed at different wavelengths is also
explored. In Section 6, we discuss various issues that can complicate the
construction of a complete AGN sample. A summary is provided in Section 7.

Throughout this work, we assume a flat $\Lambda$CDM cosmology with
$\Omega_{m}=0.287$ and $H_{0}=69.3$km s-1Mpc-1(Hinshaw et al., 2013).

##  2 Data and Measurements

###  2.1 JWST/MIRI Observations and Image Reduction

Our JWST MIRI imaging observations (JWST ID 1207; PI: George Rieke) comprise
15 separate pointings that produce a 3$\times$5 mosaic at the central 34
square arcminutes of the GOODS-S/HUDF sky area. To improve the PSF sampling,
and mitigate cosmic rays and detector artifacts, we used the 4-Point-Sets
Dither optimized for point sources with the SMALL pattern. The full MIRI field
was used with the FASTR1 readout pattern. For each pointing, the total science
exposure was about 2.17 hours with the time spread over 8 MIRI filters: F560W,
F770W, F1000W, F1280W, F1500W, F1800W, F2100W and F2550W with the shortest
integrations lasting 10.7 min (F1000W) and the longest 36.4 min (F2100W). The
exposure time per band was aimed at optimizing detection of a typical AGN or
star-forming galaxy at $z\sim 2$, except for F2550W which has a shorter
exposure time aimed at $z\sim 1$ galaxies. Most of these observations were
successfully carried out during the scheduled Dec 7–14, 2022 time window with
the same position angle (PA), but 2 pointings failed due to telescope guiding
failures and the relevant data was re-taken on Jan 1 and 28, 2023. The
different observing visits introduce position angle rotations of 26.6 and 48.2
degrees for these two pointings relative to the others and cause some
irregularity in the final MIRI mosaic layout.

The data reduction used the JWST Calibration Pipeline v1.10.0 (Bushouse et
al., 2022) and JWST Calibration Reference System (CRDS) context
jwst_1084.pmap. Starting from the raw (“uncal”) data files obtained from
MAST111https://mast.stsci.edu/portal/Mashup/Clients/Mast/Portal.html, the
pipeline module calwebb_detector1 was used with default parameters to apply
detector-level corrections, including linearity, dark current, first and last
frame effects, and jump detection. For the latter, we implemented newly
introduced algorithms with default parameters to correct for cosmic ray
showers, i.e., large cosmic ray events that can have an impact over a large
number of pixels.

Next, the default step calwebb_image2 was run to apply instrumental
corrections and calibrations to produce “cal” files. Given that MAST data
products can still suffer from strong background noise structures, at this
point we apply a custom, external background subtraction from the Rainbow
Database (Pérez-González et al., 2005, 2008; Barro et al., 2011a, b) JWST
pipeline. Briefly, as explained in more detail in Álvarez-Márquez et al.
(2023), we use an iterative process that progressively masks sources and
median filters out large gradients and striping along detector columns and
rows to produce “clean” cal files. For each (unfiltered) cal file, a “super
background” is then constructed by median combining and scaling all other
clean cal files. We found that better results are achieved by using all
(relatively contemporaneous) cal files to construct a super background as
opposed to constructing a background per visit. The latter can result in over-
subtraction around extended sources due to inadequate availability of
background pixels at the source location in detector space. The final super
backgrounds were then subtracted from each cal file and an additional 265x265
pix2 box median subtraction is applied to remove any remaining varying
background, mainly caused by cosmic ray showers.

The background-subtracted cal files were then astrometrically-corrected on a
per visit and per filter basis using tweakreg222https://jwst-
pipeline.readthedocs.io/en/latest/jwst/tweakreg/README.html with custom
routines external to the JWST pipeline. The F560W image is first registered to
the JADES image mosaic (which is registered to GAIA DR3) by creating matched
catalogs of high S/N F444W and F560W sources, which will have similar
morphologies and therefore centroids. The corrected F560W positions were then
used to correct the next longer wavelength image and so on, so as to minimize
the effects of morphological changes333Due to the decreasing number of high
S/N sources, the astrometrically-corrected F1500W catalog was used to correct
the F1800W, F2100W, and F2550W images.. The astrometric accuracy achieved by
this process is 0.01–0.02′′ ($1\sigma$) for all filters except F2550W, which
has a low number of high S/N sources and an astrometric accuracy of $\sim
0.04^{\prime\prime}$. Finally, the astrometry-corrected and background-
subtracted cal files were processed through calwebb_image3, which produced the
final mosaics with a pixel scale of 0.06′′. The final 5$\sigma$ point source
sensitivities are 0.21, 0.21, 0.44, 0.60, 0.68, 1.7, 2.8, and 15 $\mu$Jy in
F560W, F770W, F1000W, F1280W, F1500W, F1800W, F2100W, and F2550W respectively,
for apertures encompassing $65\%$ of the encircled energy of the PSFs (see
below). Compared to the JWST ETC predictions, our measured detection limits
have been improved by a factor of 1.6–2 for F770W-F1800W.

Figure 2 shows a representative cutout of our JWST/MIRI images together with
JWST/NIRCam, Chandra X-ray, and JVLA radio data in the same region. Almost all
of the X-ray and radio sources have been detected in the well-resolved MIRI
images (see Section 2.5). The MIRI image resolutions are at 0.2–0.8$\arcsec$
(PSF FWHM) while Chandra is at $\sim$0.5$\arcsec$ (field center), JVLA at
$\sim$0.3–1$\arcsec$ and JWST/NIRCam at 0.02–0.16$\arcsec$, allowing reliable
associations and accurate photometry across the full wavelength range.

Figure 2: Showcase of multi-wavelength data used for AGN searches in SMILES
with a field of view 3.5 arcmin$\times$1.0 arcmin. The yellow circles indicate
the AGNs or AGN candidates identified from this work. The JWST NIRCam and MIRI
data are shown as three-color images with the long wavelength in red,
intermediate wavelength in green and short wavelength in blue.

###  2.2 MIRI Source Identification and Photometry Measurements

Source detection and photometric catalogs were produced using a modified
version of the JADES photometric pipeline (M. Rieke et al. 2023). The object
detection uses stacked MIRI F560W and F770W high S/N images and a series of
iterative steps, starting with the creation of a low S/N threshold blended
segmentation map which is then processed to remove spurious noise detections
and deblend sources (see M. Rieke et al. 2023 for details). The final
segmentation map is used to define source centroids and 2.5x scaled Kron
apertures, which are then used to measure photometry in all filters. Aperture
corrections are applied by determining the fraction of flux outside a given
Kron aperture using a model PSF for each filter. Model PSFs are created using
WebbPSF (Perrin et al., 2014)444https://github.com/spacetelescope/webbpsf for
the F1000W-F2550W filters. The F560W and F770W PSFs include an extended cross-
like imaging artifact known as a “cruciform” (Gáspár et al., 2021) which is
not yet adequately modeled by WebbPSF. As such, we constructed empirical PSFs
using high dynamic range imaging of stars taken during commissioning (Gaspar,
private communication). Photometric uncertainties are determined by placing
random apertures across the mosaics to account for correlated pixel noise
(e.g., Whitaker et al., 2011; M. Rieke et al., 2023).

The nature of our source detection does not impose a hard S/N threshold and
our resulting catalog contains both real and spurious detections down to faint
limits. We conduct visual inspections with the aid of deep NIRCam and HST
images to throw out spurious sources. Finally, we have 3273 MIRI sources with
3-$\sigma$ detection in at least one band.

By comparing with previous Spitzer photometry of the same sources, we found
that our flux measurements in the F560W and F770W bands are systematically
higher and concluded the origin is likely due to an underestimation of the
cruciform during the flux calibration step. To mitigate the problem, we
derived the correction factors as follows. We fitted the ASTRODEEP photometry
up to IRAC band-2555The IRAC bands 3 and 4 are also affected by the same
“cruciform” problem because of the same detector architecture as in MIRI, so
they are not used in the calibration. of 25 isolated stars with single stellar
population models from the Flexible Stellar Population Synthesis (FSPS)
code666Although designed for fitting galaxies, at the wavelengths of interest
these models are dominated by the output of red giants, and thus represent a
typical average over the expected behavior of the stars. and computed the
synthetic MIRI fluxes in F560W and F770W. The median offsets between the
observed (aperture-corrected) Kron flux and the model flux were then computed;
the values are 1.26 for F560W and 1.04 for F770W. Finally, we divide the F560W
and F770W fluxes by these factors during the SED fitting in Section 3. The
photometry is archived in the v0.4.2 MIRI photometry catalog.

###  2.3 JWST/NIRCam, HST/ACS, HST/WFC3 Photometry and SED Constructions

Most of the SMILES survey area overlaps with the NIRCam image footprint from
JADES (Eisenstein et al., 2023). This includes the JADES GOODS-S NIRCam/Deep
(ID: 1180; PI: Daniel Eisenstein), partly taken from September 29 to October
10, 2022 and already publicly released (M. Rieke et al., 2023), and the JADES
GOODS-S NIRCam/Medium (ID 1210; PI: Nora Luetzgendorf), taken from October 20
to 24, 2023. All these datasets include deep F090W, F115W, F150W, F200W,
F277W, F335M, F356W, F410M, F444W images, offering critical insights into the
nature and properties of the MIRI sources. In addition, these NIRCam
observations were carried out no more than 3 months earlier than the MIRI
data, which together with the time dilation (typical redshifts are $z$=1–3),
greatly mitigates the possible influence from AGN variability on the source
SEDs.

Although the previous HST data is not as deep as JADES NIRCam data at the same
wavelength, it is still indispensable as HST provides measurements at
$\lambda<0.8~{}{\rm\mu m}$ that are missed by NIRCam. The addition of HST
bands also improves the SED analysis such as constraining the photo-$z$. The
HST/ACS F435W, F606W, F775W, F814W, F850LP and HST/WFC3 F105W, F125W, F140W,
F160W images of this field have been reprocessed by the JADES team, including
registering the astrometry against JWST/NIRCam images and measuring the HST
and NIRCam fluxes in a consistent way.

For the NIRCam and HST photometry in the JADES field, we have adopted the PSF-
convolved KRON flux from the JADES v0.8.1 catalog. We searched for the NIRCam
counterparts for all the MIRI sources within a radius of 0.3 arcsec. For MIRI
sources outside the current JADES footprint, we searched for the nearest
CANDELS source within a radius of 0.6 arcsec and adopted the corresponding
ASTRODEEP (Merlin et al., 2021) photometry up to IRAC band 2 (4.5 $\mu$m).

We have verified the quality of the JADES+MIRI photometry measurements with
synthetic JWST photometry based on the SED fittings of the same sources with
the ASTRODEEP+Spitzer IRS 16 ${\rm\mu m}$ and MIPS 24 ${\rm\mu m}$ data. On a
statistical level, they agree with each other within 1-4%.

###  2.4 Redshift Constraints for MIRI Sources

To aid the SED analysis, redshifts of the MIRI sources are collected from
various sources that can be grouped into three categories:

  * •

spectroscopic redshifts: These include high confidence spectroscopic redshift
measurements published in the literature over last twenty years (Kodra et al.
2023; H. Hathi, private communication), the redshift catalog of the MUSE HUDF
surveys published this year (Bacon et al., 2023), and also the recently
released redshifts from JADES NIRSpec data release 1 (Bunker et al., 2023).

  * •

grism redshifts: Most of the MIRI field has also been covered by FRESCO grism
spectral observations with NIRCam/F444W (3.8–5.0 $\mu$m) at R$\sim$1600 (Oesch
et al., 2023). Since many objects only show a single emission line, we need to
guide the redshift measurements with photo-$z$ from JADES NIRCam as priors.
Typically, the confidence level of the prism redshift is the same as the
spectroscopic ones above given the spectral resolution and depth of the NIRCam
grism observations. When a FRESCO grism redshift is not available, we search
for the 3D-HST grism redshift from WFSC3/G141 (1.1–1.65 ${\rm\mu m}$) at
R$\sim$130\. Given the shorter wavelength coverage and poorer resolution, the
latter is mostly only useful for objects at low redshifts.

  * •

photometric redshifts: Thanks to the extensive multi-band HST and JWST/NIRCam
image coverage of the JADES, reasonably accurate photo-z measurements are
possible for objects without spectroscopic observations. The JADES team has
updated EAZY (Brammer et al., 2008) configurations and provided the photo-z
measurements based on fitting HST and JWST/NIRCam photometry (see details in
Hainline et al., 2023). We adopt this product by default. For objects outside
the JADES/NIRCam coverage, we adopt the optimized photometric redshifts for
CANDELS published in Kodra et al. (2023).

Figure 3 gives a summary of the redshift distribution of the MIRI sources.
Despite the rather short exposure times of these MIRI bands, we detected over
30 objects at $z>6.0$. About 45% of the sample have spectroscopic or grism
redshifts. The EAZY-based photometric redshifts based on NIRCam data alone
have 5% outliers and a scatter (excluding outliers) of
$\left<z_{spec}-z_{phot}\right>$ of 0.024, 1$\sigma$ (M. Rieke et al., 2023).
With the combination of MIRI photometry at longer wavelength, we expect the
updated photo-$z$ computed from our SED fitting with Prospector (see Section
3.1) to be improved further with even less outliers.

Figure 3: Redshift distribution of MIRI sources. We highlight the sources of
the final adopted redshifts with different colors.

###  2.5 Other Supporting Datasets and Catalogs

Following the strategies in Lyu et al. (2022a), we cross-matched our MIRI
sources with the CDF-S and JVLA catalogs to look for AGN signatures within a
search radius of 3.0$\arcsec$. As the astrometry of the Chandra data was
registered in the pre-Gaia era, we investigated the coordinate differences
between the closest-matched MIRI and X-ray sources and applied a systematic
shift of $\Delta$RA=0.128$\arcsec$ and $\Delta$Dec=$-$0.288$\arcsec$ to all
the CDF-S sources to remove the relative astrometric offsets. There are 202
X-ray sources within the MIRI footprint but four of them do not have MIRI
counterparts. Upon visual inspection, their X-ray detections are below
3-$\sigma$. For the radio sources, there are 183 objects but one does not have
a MIRI counterpart. The nature of this radio source will be explored in the
future.

In Section 3.4, we will discuss AGN selections for sources detected in X-ray
or radio.

##  3 AGN Identifications

With the data described above, we conducted SED analysis to search for AGNs.
Section 3.1 introduces the semi-empirical SED models used for AGN
identifications. In Section 3.2, we conduct the initial round of SED analysis
to get an overview of the sample properties of all the MIRI sources and check
the performance of our models. Section 3.3 describes how the AGNs are
identified from SED analysis with the removal of possible false positives. In
Section 3.4, we summarize the AGN samples selected by other methods in the
same field, including those utilizing the JWST data and those in the pre-JWST
era.

###  3.1 SED Models and Fitting Setup

As discussed in detail in Lyu & Rieke (2022a), reliable AGN identification
through SED fitting requires careful minimization of the free parameters of
the models while achieving accurate fits to the data. To meet this
requirement, we introduced a fitting package for AGN SED selection and
analysis in Lyu et al. (2022a) that features a modified version of the
Prospector code (Johnson et al., 2021) with the Flexible Stellar Population
Synthesis (FSPS; Conroy et al. 2009; Conroy & Gunn 2010) model for the stellar
component; semi-empirical SED models for the AGN component (Lyu et al., 2017;
Lyu & Rieke, 2017, 2018, 2022a); and a well-proven template for dust emission
from the star-forming galaxies (Rieke et al., 2009; Lyu et al., 2022a). The
same tool is also used for this work with a few updates described below. In
Figure 4, we show the SEDs of different components and illustrate some of
their variations.

The configuration of the stellar component is very similar to Lyu et al.
(2022a) — we assumed the same Kroupa initial mass function and delayed-tau
star-formation history but adopted the Calzetti attenuation curve with a
flexible slope as in Kriek & Conroy (2013). Since there are objects at very
high redshifts, IGM absorption is also included. In total, there are seven
free parameters to describe the stellar component: (1) stellar mass formed
($mass$); (2) stellar metallicity ($logzsol$); (3) stellar age ($tage$); (4)
$e$-folding time of the star formation history ($tau$); (5) the attenuation
level at 5500 Å ($dust2$); (6) the power law index of the attenuation
($dust\\_index$); (7) the IGM absorption factor ($igm\\_factor$). For stellar
populations older than $\sim$ 10 Myr, regardless of the metallicity and star
formation history, the near-to-mid-IR infrared photospheric emission is
dominated by red giants and supergiants with very similar spectra, so any
degeneracy of the parameters (2)–(4) will not influence the AGN
identification.

Figure 4: Illustration of how the AGN SED shape is changed by different levels
of attenuation as well as the templates for the galaxy stellar and dust
components in our model. The upper panel shows the AGN SED obscured by the SMC
extinction curve, mainly used for UV-optical reddened AGNs. The lower panel
presents the AGN SEDs modified by the empirical IR attenuation law. We show a
galaxy stellar SED with a stellar age of 500 Myr (light green thick line), and
the pure dust emission templates for normal star-forming galaxies (light
orange line) and low-metallicity dwarf galaxies (light magenta line) to
compare with the AGN SEDs in both panels. See text for details.

For the star forming galaxy (SFG) dust emission component, by default we also
adopt the template for $log(L_{\rm IR}/L_{\odot})=11.25$, which is the best
description of the average properties of normal SFGs at a wide range of
redshifts that constitute our primary sample (see Appendix A in Lyu et al.
2022a for details). Furthermore, as shown later, our MIRI data is deep enough
to probe the dwarf galaxy regime, where the systems can be low-metallicity and
their dust emission SEDs likely behave differently from those of more luminous
SFGs. These galaxies constitute our extended sample. We therefore adopt the
Haro 11 SED template developed in Lyu et al. (2016) as the choice to match the
dust emission SED for dwarf galaxies. This object has been selected from the
Dwarf Galaxy Survey (Rémy-Ruyer et al., 2013) to represent the extreme SED
behavior for low-metallicity star-forming galaxies and it features a much
higher dust temperature ($T$=46.5 K) than normal dust SFGs ($T\sim$20–30K)
with a warmer mid-IR SED and weak PAH features. De Rossi et al. (2018) have
also shown that the Haro 11 SED template is the best choice among other
options for young, presumably sub-solar metallicity, galaxies undergoing
extremely vigorous episodes of star formation. In Section 3.2, we will test
the performance of these two sets of galaxy dust emission templates with MIRI
data and explore the situation where the Haro 11 template is preferred for our
SED fits. Once the template is decided, there is only one free parameter
$L\\_IR\\_obs$ to scale it to match the data together with other components.

Compared to Lyu et al. (2022a), several improvements have been made for the
AGN component:

  * •

Based on the SDSS DR7 optical spectral archive (Abazajian et al., 2009) and
near-IR AGN templates in the literature (Glikman et al., 2006; Hernán-
Caballero et al., 2016), we increased the resolution of the AGN template from
the UV to the near-IR and added AGN emission lines to the model. These
emission lines are further decomposed into narrow and broad components with
the separation at FWHM=1200 km/s and these two groups of lines can be modified
in different ways (e.g., obscuration at different physical scales).

  * •

We introduced a hybrid extinction configuration for the AGN component that
considers the possibly different dust grain properties at different scales or
angular directions. This setup features a Small Magellanic Cloud (SMC) curve
for the commonly seen UV-optical extinction in type-1 AGNs and an empirical
attenuation law constructed for the AGN IR obscuration. The former extinction
law is associated with small dust grains at large physical scales far away
from the central engine and the latter is for the circumnuclear obscuration,
such as the dusty torus, which is likely to be dominated by larger dust grains
because of the harsh environment (Baskin & Laor, 2018).

Figure 4 presents this updated AGN model with various emission lines and shows
how the SED can be modified by the different levels of extinction
characterized by the two attenuation laws parameterized by $tau\\_opt$ and
$tau\\_ir$. In our fitting, the AGN continuum and broad emission line
component are bound together and can be obscured by both attenuation laws
(i.e., $tau\\_opt$ and $tau\\_ir$) while the relative strength of the narrow
line component can be only changed by the SMC extinction ($tau\\_ir$). We
adopt a uniform prior for $tau\\_opt$ = [0, 3.0] and $tau\\_ir$ = [0, 20.0] as
these ranges cover the majority of AGN obscuration without too much
redundancy. For the purpose of AGN selections, we also adopt the normal AGN
template following Lyu et al. (2022a); Lyu & Rieke (2022b). In total, there
are three parameters for the AGN component: (1) AGN luminosity ($L\\_AGN$),
(2) AGN SMC extinction level ($tau\\_opt$) and (3) AGN IR extinction level
($tau\\_ir$).

During the SED fittings, we fix the redshift if the object has a spectroscopic
or grism redshift (from FRESCO or 3D-HST). For objects that only have JADES or
3D-HST photo-z, we will re-compute the photo-z with the new SED fittings that
include the MIRI data points. Accordingly, we have up to 12 (11 if the
redshift is fixed) free parameters for the SED fitting. We use Dynamic Nested
Sampling to find the best-fitting results and assess the model degeneracies.
Readers are highly encouraged to read the relevant sections in Lyu et al.
(2022a) for details.

Our approach has a number of advantages. First, our models for the AGN
component and the SFG dust component are derived from empirical observations
and have been extensively tested, as elaborated in detail in Lyu et al.
(2022a); Lyu & Rieke (2022a). This allows us to narrow the number and range of
free parameters while retaining realistic fits, in contrast to approaches that
are more strongly based on theoretical models. In addition, the SED model of
our AGN component not only provides a good fit to the AGN observations, but
can change continuously from the unobscured phase to the most heavily obscured
phase, offering an unbiased view on the distribution of AGN obscuration
properties. Moreover, compared to most other tools, our AGN model includes
broad and narrow emission lines, a typically over-looked but now very needed
feature for the much improved photometric data in the JWST era. Finally,
although preliminary, our SED fittings have the option to use an empirical
low-metallicity galaxy template to fit the IR SEDs of the dwarf galaxy
population.

More details of this SED fitting package, including its public release, will
be presented in a separate paper in the future.

###  3.2 SED Fitting of MIRI Sources

We fit the SEDs for all the MIRI sources with the redshift priors described in
Section 2.4. Within the current JADES footprint, the fits can utilize a
maximum of 27 photometric bands that include five HST/ACS bands at 0.44 – 0.9
$\mu$m, fourteen JWST/NIRCam bands at 0.9 – 4.4 $\mu$m and eight JWST/MIRI
bands at 5.6 – 25.5 $\mu$m. For the 89 MIRI sources outside the current JADES
NIRCam catalog footprint, besides the MIRI photometry, we have 23 bands from
the ASTRODEEP catalog at 0.3 – 4.5 $\mu$m, which includes HST ACS and WFC3,
ground-based deep optical and near-IR observations and Spitzer/IRAC bands 1
and 2. In the first round, we adopted the normal luminous SFG dust template
for the fitting to get an overall picture of the sample properties and to test
the performance of our SED models.

####  3.2.1 Testing the SED Models for the Massive Galaxy Population

To demonstrate the robustness of our SED models, we present some
representative SED fittings of dusty star-forming galaxies (DSFGs) and
quiescent galaxies (QGs) from $z\sim 0.2$–4 in Figure 5. As we require good
MIRI coverage to test our SED templates, most of these galaxies are relatively
massive for their redshifts, by selection (i.e., $M_{*}\gtrsim
10^{9.5-10}~{}M_{\odot}$) and they form our primary sample. For DSFGs, various
SED features are successfully matched by our templates, showing that the MIRI
bands can be used to trace the various PAH features at e.g., 3.3, 6.2, 8.6,
11.3 $\mu$m. For the QG population, the MIRI photometry is deep enough to
constrain the stellar Rayleigh-Jeans tail at rest-frame 5 $\mu$m even for
galaxies at $z\sim 4$, as shown in the bottom-right panels of Figure 5.

The high-quality MIRI photometry as well as the satisfactory performance of
our SED models for massive DSFGs and QGs establish a solid foundation for
identifying AGN signatures through SED analysis in such systems.

Figure 5: Example JWST NIRCam+MIRI SED fittings of dusty star-forming galaxies
(left) and IR-quiescent galaxies (right). The object redshifts increase from
top to bottom. Below each SED plot, we also show 3″$\times$3″cutouts of the
source NIRCam and MIRI images and comparisons of the source light profile
(black line) and the corresponding ePSF (red line).

####  3.2.2 Testing the SED Models for the AGN Population

Figure 6 shows some representative examples of MIRI sources with AGN evidence
revealed by SED fittings. Compared to galaxies without AGNs shown in Figure 5,
the SEDs of these objects present much larger variations that can be explained
by the different level of AGN-galaxy contrast and the SED variations of the
AGN component itself. Our examples include AGNs with a wide range of redshift,
obscuration level and galaxy contamination as revealed by the very successful
SED decompositions.

Figure 6: Similar to Figure 5 but for AGNs. In the top six panels, we show
normal AGNs with a range of nuclear obscuration and host galaxy contamination
at $z<4$. The four panels in the bottom row are example AGNs in low-mass
galaxies.

####  3.2.3 The Complicated Situation of the Dwarf Galaxy Population

Besides reproducing the photometric SEDs, our fittings provide measurements of
the galaxy stellar mass. As shown in Figure 7, this MIRI sample includes a
considerable number of dwarf galaxies ($M_{*}<10^{9.5}~{}M_{\sun}$), where our
templates for normal SFGs may not work. To determine where this may become an
issue, we visually inspected the fittings of all the MIRI sources and picked
out those pure SFGs where the updated Rieke et al. (2009) template gives a
good match (orange crosses). These “normal” SFGs have a stellar mass
distribution peaked between $10^{9.5}$ and $10^{10}~{}M_{\odot}$, which is
about 0.5 dex above the median stellar mass of the whole MIRI galaxy
population. However, we found that the IR SED features (e.g., PAH strength) of
many low-mass galaxies are not captured by these “normal” SFG templates.
Compared to the normal galaxy populations, these low-mass galaxies could have
different IR SED behavior associated with low-metallicity ISMs (e.g., Rémy-
Ruyer et al., 2013; Shivaei et al., 2020, 2022). In particular, the reduced
PAH emission strengths and warmer dust continuum emission associated with the
dwarf galaxy component can mimic the mid-IR colors of obscured AGNs to some
level (e.g., Hainline et al., 2016), making the AGN identification with
classical SED models not convincing. Thus, we need to introduce a method to
remove most, if not all, false positives of AGN candidates in this extended
sample.

Based on empirical observations, a correlation between PAH strength and
metallicity has been revealed from the low-$z$ Universe (e.g., Engelbracht et
al., 2005, 2008; Madden et al., 2006) to cosmic noon at $z\sim 2.0$ (Shivaei
et al., 2017). While the PAH intensity and relative strength of different
bands stay almost the same at higher metallicity, below $\sim$ 12 + log(O/H) =
8.2 the PAH strength drops suddenly (Engelbracht et al., 2005, 2008; Marble et
al., 2010; Aniano et al., 2020).777The behavior at z $\sim$ 2 is not clear.
Shivaei et al. (2017) indicate a drop below log(O/H)$\sim$8.3 for O3N2
metallicities, but a drop at a higher level for N2 metallicities. The physical
basis for a large change with metallicity at redshift $\sim$ 2 is not clear,
so we assume the local (and O3N2) behavior. Given the correlation between the
galaxy stellar mass and metallicity observed over a wide range of stellar mass
and redshift (e.g., Tremonti et al., 2004; Lee et al., 2006; Zahid et al.,
2013), we use the stellar mass from our SED fittings to approximate the gas
metallicity 12 + log(O/H) following the empirical fittings in Ma et al.
(2016), and adopt the dwarf galaxy dust template (see Section 3.1) when the
metallicity (or stellar mass) threshold is not met. According to fittings in
Ma et al. (2016), the corresponding mass threshold $M_{\rm*,lim}$ as a
function of redshift $z$ for 12 + log(O/H) $\sim$8.2 is

$\log(M_{\rm*,lim}/M_{\odot})=10.714+2.657\exp(-0.43z)$ (1)

This curve is shown as the red solid line in Figure 7. To account for the
uncertainties of e.g., stellar mass measurements and the dispersion of the
mass-metallicity relation, we will compare the normal and dwarf galaxy dust
template fittings for all objects within $\pm$0.3 dex of $M_{\rm*,lim}$
(dashed red lines), and use the normal galaxy dust model results for galaxies
above +0.3 dex of this mass threshold and use the dwarf model below -0.3 dex
of it. In addition, this threshold is not a hard cut as we will compare the
fittings with the normal SFG and Haro 11 templates for objects within $\pm$0.3
dex of the mass cut, corresponding to a 12+(O/H) range from 8.3 to 8.1.

Figure 7: The stellar mass as a function of redshift for MIRI sources. We
highlight all AGNs identified with the normal SFG template regardless of the
object redshift and stellar mass in light blue on the left, and the refined
AGN sample after adopting the Haro 11 dwarf SFG template to reject possible
false positives among low-mass galaxies in dark blue on the right. In both two
panels, we show the normal SFG galaxies as orange crosses and other galaxies
as gray dots. The red solid line represents the stellar mass threshold for
12+log(O/H)$\sim$8.2 with the $\pm$0.3 dex variation range as red dashed line.

Despite these steps, it is possible that the AGN selections in our primary
sample are mildly contaminated by cases with stellar-heated hot dust. In the
case of our extended sample, Haro 11 is one of the most extreme cases with
significant warm dust emission and weak PAH strength among the Dwarf Galaxy
Survey (Rémy-Ruyer et al., 2013). The usage of this template therefore may
have biased our results against AGN identifications in this sample, as
discussed in Section 3.3.2.

###  3.3 AGN Identifications through SED Analysis

We now describe our AGN identifications through SED analysis for the three
sub-samples of our full object list. Following the same strategies in Lyu et
al. (2022a), we picked out AGN candidates by analyzing the quality of the SED
fittings, including inspections of any degenerate models from the posterior
distributions of the fitted parameters. Typically, the AGN solution will be
accepted if the distribution of the fitted AGN luminosity parameter L_AGN has
one single isolated peak and the AGN-dominated bands are not all noisy (i.e.,
S/N$<$3.0). In addition, the high spatial resolution provided by NIRCam and
MIRI allows us to further check if the AGN evidence inferred from the SED
analysis is supported by the galaxy having a point-like core. Depending on
galaxy stellar mass and redshift, the AGN identifications have some additional
considerations described below.

####  3.3.1 AGNs in Massive Galaxies (Primary Sample)

For typical massive galaxies ($M_{*}\gtrsim 10^{9.5}~{}M_{\odot}$) at $z=$0–4,
the MIRI data has good coverage of the AGN warm dust features and our
templates are well-tested. As a result, we treat the selected objects as SED-
identified AGNs. In total, we identify 111 AGNs under this category.

In the top six panels of Figure 6, we show some representative SED fittings
together with some NIRCam and MIRI images at the source location. In general,
once the AGN emission dominates in a spectral band according to our SED
decomposition, the corresponding radial profile of the source in that band is
consistent with the instrument PSF, supporting the validity of our SED
interpretation.

####  3.3.2 AGNs in Dwarf Galaxies (Extended Sample)

As discussed in Section 3.2.3, low-metallicity dwarf galaxies have IR SED
shapes that can mimic the relatively hot dust emission features of AGN (see
one example in Figure 8). For such galaxies, we replace the massive “normal”
SFG template with a dwarf galaxy template based upon Haro 11 to fit the SEDs
and look for AGN candidates. An additional challenge is the reduction of MIRI
coverage at longer wavelengths at the lower infrared luminosities of these
objects. These two effects restrict our identification of AGNs in these
galaxies to cases with SEDs significantly bluer (hotter) than that of Haro 11.
Our selection is probably missing some obscured AGNs with SEDs similar to that
of Haro 11.

Figure 8: Example SED fittings of a low-mass galaxy. The top panel shows the
object is identified as an AGN if we use the typical templates for normal SFGs
and the bottom panels shows the fittings with the Haro 11 template, where the
AGN evidence is gone.

However, using the Haro 11 template greatly reduces the chance of a source
being mis-classified as an AGN, especially for SF-AGN composite systems. We
have shifted the mass threshold to test whether the value of our adopted mass
threshold is reasonable. If we shift it upward by 0.3 dex (corresponding to
higher gas metallicity), some previous AGNs selected with the normal SFG
template are re-classified as pure star-forming (dwarf) galaxies as expected.
However, most such objects are either X-ray sources or show point-like
morphology in the NIRCam or MIRI images, indicating that they are probably
real AGNs and the additional shift of the threshold is not desirable. As a
result, we think the current mass threshold in Equation 1 is valid.

We adopted the definition of dwarf galaxies to be $M_{*}\lesssim
10^{9.5}~{}M_{\odot}$, regardless of the object redshift. In the first round,
187 AGN candidates that belong to this category are picked out. We have
visually inspected all of these fits and rejected cases where the results are
ambiguous due to low signal to noise, inconsistencies in the photometry from
band to band, etc. Finally, 86 convincing AGN detections are left. This value
is a lower limit given the limitations discussed above, particularly if AGNs
with SEDs similar to that of Haro 11 have been overlooked. Methods to improve
the AGN identifications throughout this extended sample need further
development.

In Figure 6, the second row from the bottom shows two example dwarf galaxies
where the AGN revealed by the MIRI SEDs is consistent with the obscured AGN
template. The SED of MIRI 431 even shows evidence of strong silicate
absorption, a typical IR tracer for a heavily embedded nucleus.

####  3.3.3 AGNs at $z>4$ (High-$z$ Sample)

For galaxies at $z>$ 4, the MIRI data at the longest wavelengths are typically
not deep enough to constrain the AGN hot dust SED shape well and the other
MIRI bands are probing the rest-frame near infrared (or even optical) bands
where some of the signatures of obscured AGNs are no longer accessible.
Therefore, the identification of high-$z$ AGNs is relatively less robust and
likely only yields a subset. In total, we identify 20 AGN candidates at z $>$
4 with the highest redshift at $z=$8.4.

Figure 9 presents six examples of AGNs identified at $z>$ 4\. All of these
galaxies have spectroscopic redshifts and the AGN nature of MIRI 1104 is
confirmed by the broad H$\alpha$ emission revealed by the FRESCO spectrum
(Matthee et al., 2023). The infrared excesses in all of these cases extend to
wavelengths significantly shorter than the excesses of star-forming galaxies.
These examples demonstrate that MIRI observations can find obscured AGNs at
$z>4$, although the samples will be incomplete.

Figure 9: Similar to Figure 5 but for high-z AGN candidates. The limited rest
frame infrared coverage still allows identification of obscured AGNs, although
not as completely as for lower redshifts.

$\ast$ $\ast$ $\ast$

Combining the results above, we found 217 AGNs (or candidates) from SED
analysis in total as listed in Table 1. In Figure 10, we present the AGN
luminosity distribution as a function of redshift. Compared to the AGN sample
selected in the pre-JWST era (Lyu et al., 2022a), the new sample contains more
objects with lower bolometric luminosities and/or higher redshifts.

Table 1: List of MIRI-selected AGN in the SMILES Footprint

MIRI ID | RA | Dec | $z$ | z_type | sub-sample | $\log(L_{\rm AGN,bol}/L_{\odot})$ | $\log(M_{\rm star}/M_{\odot})$
---|---|---|---|---|---|---|---
67 | 53.137839 | -27.87013 | 2.07 | z_easy | norm | 11.51 | 9.98
68 | 53.101515 | -27.86996 | 3.46 | z_easy | norm | 12.29 | 11.18
……… |  |  |  |  |  |  |

Note. — Only a portion of this table is shown here to demonstrate its form and
content. The full table will be made avaliable with the published journal
online

Figure 10: AGN luminosity as a function of redshift. The MIRI AGNs are shown
in blue and the pre-JWST AGNs are shown in gray.

As mentioned above, there are several caveats in the AGN search among the
extended and high-$z$ samples and our selections are conservative to reduce
the number of false positives. In the future, we will improve these selections
with more comprehensive analysis and additional data.

###  3.4 Other Selections in the SMILES Footprint

Besides SED fitting of MIRI sources, there are many other methods to identify
AGNs. Now we describe the identification of radio-loud AGNs, broad-line AGNs
and mid-IR variable AGNs from JWST data with the combination of datasets at
other wavelengths when necessary, and then summarize AGN samples identified
without the usage of JWST data within the same footprint.

####  3.4.1 Radio-Loud AGNs with MIRI and JVLA

Since we now have deeper F2100W photometry than the previous Spitzer/MIPS
24$\mu$m data, it is possible to improve the previous radio-loud AGN
selections based on the ratio between the radio and mid-IR fluxes (i.e.,
[rtype_rl] in Lyu et al. 2022a). As the F2100W data is deeper than F2550W, we
adopt $q_{\rm 21,obs}=\log(S_{\rm 21\mu m,obs}/S_{\rm 1.4~{}GHz,obs})$ to
reveal outliers from the radio-infrared correlations for dusty star-forming
galaxies, where $S_{\rm 21\mu m,obs}$ is the observed MIRI F2100W flux and
$S_{\rm 1.4~{}GHz,obs}$ is the observed radio flux at 1.4 GHz. For the latter,
we assume a power-law index of $\alpha=-0.7$ to estimate the 1.4 GHz flux
density from 3 GHz, similar to Lyu et al. (2022a). Following the same strategy
in Alberts et al. 2020, we determine the locus of SFGs by calculating $q_{\rm
21,obs}$ at $z=$0–3 from the Rieke et al. (2009) SFG templates at $\log(L_{\rm
IR}/L_{\odot})=[11,11.5,12]$. An outlier from the SFG radio-infrared relation
is defined as being 0.5 dex below the midpoint of SFG $q_{\rm 21,obs}$ locus.
Using this criterion, we identify 8 radio-loud AGNs among 167 $z<3.0$ MIRI
sources that have been detected at 3 GHz, as shown in Figure 11. In total,
there are 39 AGNs in this radio-detected sample and the radio loud fraction is
about 20%.

Figure 11:  The observed IR-to-radio flux $q_{\rm 21,obs}$ as a function of
redshift of all the sources. The red dotted line is 0.5 dex below the mid-
point of the radio-infrared correlations in the Rieke et al. (2009) SFG
templates (the green solid line); we define the source as a radio-loud AGN if
it falls below this line at $\geq$ 2-$\sigma$ significance. We color-code
different sources on the main plot and show their relative distribution of
$q_{\rm 21,obs}$ in the right panel.

####  3.4.2 Spectroscopically-identified broad-line AGNs with JWST

FRESCO has conducted deep NIRCam/grism observations with the F444W filter for
the 62 arcmin2 area of GOODS-S that has a considerable overlap with our SMILES
survey (see Figure 1), offering the chance to identify AGNs through broad-line
emission. At $z>5$, only one AGN has been reported at z=5.481 in GOODS-S,
based on the broad H$\alpha$ emission line (Matthee et al., 2023). Our MIRI
SED fitting also identified this AGN (MIRI ID v0.4.2: 1104). We have also
looked for AGN candidates at lower redshifts based on broad emission lines
from Pa$\alpha$, Pa$\beta$ and He II (Lyu et al, in prep). All the sources we
found have been previously identified in Lyu et al. (2022a).

In addition, a few AGNs in GOODS-S have been identified from JADES/NIRSpec
spectral observations (Bunker et al., 2023). In terms of broad-line AGNs,
there are only two: 10013704 ($z$=5.92) and 8083 ($z$=4.64) (Maiolino et al.,
2023). In contrast to the FRESCO AGN, these objects are much fainter by
selection. In our MIRI images, they are only marginally detected in F560W
and/or F770W without additional measurements in other bands and thus we cannot
conduct robust SED fittings for AGN selection.

####  3.4.3 Mid-IR Variable AGNs with NIRCam and IRAC

Given the deep IRAC and NIRCam images available across the field, we can also
look for long-term mid-IR variable AGNs. We have degraded the JADES/NIRCam
F356W images to match the PSF of Spitzer/IRAC band-1 mosaics and carried out
local difference photometry to look for variable objects (Lyu et al., 2023, in
prep). Nevertheless, no new AGNs have been revealed.

####  3.4.4 AGNs with weak Pa $\alpha$

Potential identification of AGN candidates from weak Pa $\alpha$ compared with
mid-infrared continuum is discussed in Appendix A. We have not yet applied
this method; with current data it adds a small number of candidates.

####  3.4.5 AGN Selected in the Pre-JWST Era

In Lyu et al. (2022a), we have presented an AGN sample selected based on the
pre-JWST data from X-ray to radio with eight different methods that included
X-ray luminosity, X-ray-to-radio luminosity ratio, UV-to-mid-IR SED fitting,
mid-IR IRAC color, radio-to-mid-IR ratio (for radio-loud AGN), radio slope
(for flat spectral radio sources), and time variability in the X-ray and
optical bands. We can now replace the AGN sample selected by IRAC color and
SED fittings from the Lyu et al. (2022a) pre-JWST catalog with the new SED-
identified AGNs obtained in this work using the improved JWST photometry. In
the MIRI survey footprint, there are 203 pre-JWST candidate AGNs in total;
only five of them do not have a MIRI counterpart. Among these pre-JWST AGNs
without MIRI source association, four are identified by variability and one is
identified by the X-ray to radio flux [x2r]. The latter object is also an
X-ray source with very low S/N detection that does not have a MIRI
counterpart, which therefore may be spurious.

We can also compare the SED fittings with the Spitzer and JWST data for the
same sources. Two examples are given in Figure 12. The first object (MIRI 32)
does not show evidence for an AGN, since the SED constraints for wavelengths
longer than IRAC band 2 are very limited in the shallower Spitzer data. In
contrast, the new MIRI data points reveal this object is a composite galaxy
with an obscured nucleus in the mid-IR. The second object (MIRI 1691) seems to
be an obscured AGN based on the limited wavelength coverage by the Spitzer
data while the new fittings with MIRI data prefer the star forming galaxy
solution. In fact, the AGN solution can be ruled out due the extended MIRI
source morphology.

Figure 12: Comparison of the SED fittings with pre-JWST data (left) and JWST
data (right). Two examples are shown.

For the other selections based on X-ray or radio properties, the new mid-IR
JWST data do not have any influence.

##  4 AGN Statistics and Comparison of Selection Techniques

With the various selection methods presented above, we can now compile all
known AGNs in the MIRI footprint and compare the relative performance of
different selections. As described above, four variable AGN candidates and one
X-ray-to-radio AGN candidate do not have MIRI counterparts and they are likely
to be spurious; thus we will ignore them and focus the discussion on the MIRI-
detected sources.

###  4.1 MIRI AGN Number Density

Given the survey area of $\sim$34 arcmin2, the total number density for the
robust MIRI AGNs in the primary sample is 3.4 per arcmin2, or nearly eight
AGNs per MIRI pointing. In addition, there are 2.9 AGNs per arcmin2 from the
extended sample. For each band, the total MIRI-selected AGN (candidates)
number densities are 6.4, 6.3, 5.6, 5.0, 4.7, 3.7, 3.2 and 2.1 arcmin-2 from
F560W to F2550W (3$\sigma$ detections, corresponding to flux limits at 0.16,
0.13, 0.26, 0.36, 0.41, 1.0, 1.7 and 9.0 $\mu$Jy). These estimates will evolve
as we develop more sophisticated methods to differentiate the emission by warm
dust in extended-sample galaxies from that of embedded AGNs. For now, these
values should be treated as lower limits for total numbers of AGNs detected by
MIRI since (1) due to the reduced sensitivities at longer wavelengths, we are
likely missing obscured AGNs at high-$z$; and (2) for the AGNs in the
extended-sample galaxy population, we are using an extreme SFG template based
on Haro 11 and it is possible that some AGNs are missed due to the
conservative selection; (3) there are AGNs that have been identified by other
selection techniques and that are detected by MIRI but are missed by the SED
analysis, as shown in the upcoming section.

In Figure 13, we plot the number density of the various MIRI sources as well
as the total AGN fraction as a function of observed flux for all the MIRI
bands. In every band, the AGNs in the primary sample of massive galaxies have
dominated the distribution at the bright end while those in the extended
sample of low-mass galaxies and high-$z$ galaxies contribute mainly at the
faint end. The total AGN fraction typically dominates the bright end with a
fraction $>$40-60% and gradually drops toward the faint end to about 13–16%.
For F560W, F770W and F1000W, the AGN fraction presents an apparent drop at the
faint end, which is expected given our reduced selection efficiency since
these sources are not detected at longer wavelengths. For F1280W, F1500W and
F1800W, the AGN fraction seems to present a trend that decreases from the
bright end to the intermediate flux followed by a gradual upturn towards the
faint end. Given that the redshift distribution of our MIRI sources is peaked
around $z\sim$1–1.5, such trends are likely caused by the strong 7.7$\mu$m PAH
features from DSFGs.

Figure 13: The MIRI source number density and AGN fraction as a function of
observed flux in each MIRI band.

###  4.2 Comparison to Previous AGN Samples

In Figure 14, we present the Venn diagram to show how the MIRI SED-identified
AGN sample intersects with the CDF-S AGN catalog (Luo et al., 2017) and the
pre-JWST AGN catalog (Lyu et al., 2022a). Over the same area, there are 94
X-ray detected AGNs reported from the 7Ms CDF-S catalog with an AGN number
density about 2.8 per arcmin2. Among the 217 MIRI AGNs, only 48 of them (22%)
have X-ray detections in the 7Ms CDF-S catalog and 38 of them (18%) are
reported as AGN by Luo et al. (2017). In other words, our relatively shallow
MIRI survey ($\sim$2.17 hours per pointing) yields 2.4 times more AGNs than
the deepest X-ray survey ($\sim 1800$ hr) and 80% are new discoveries.

Figure 14:  Venn diagram of the various AGN samples within the SMILES
footprint. We compare the X-ray detected AGN sample in Luo et al. (2017), the
pre-JWST AGN sample compiled in Lyu et al. (2022a) and the MIRI SED-identified
AGN sample in this work. The numbers in the brackets are the total numbers of
AGN from each source. For objects identified by MIRI, we also plot a
horizontal stacked bar chart to show the number of normal AGNs (green), dwarf
AGNs (red) and high-$z$ AGNs (yellow) for each subset.

Within the same footprint, there are 181 sources in the pre-JWST AGN catalog
that include not only X-ray detected but also radio- and mid-IR detected AGNs.
Still, about 78% of the MIRI AGN sample does not overlap with the pre-JWST AGN
sample, demonstrating the huge discovery space offered by JWST. Among the 168
AGNs only identified by MIRI SEDs, there are 64 AGNs in massive galaxies at
$z<$4.0, 85 AGN candidates in dwarf galaxies and 19 high-$z$ AGN candidates.
The latter two populations are mostly found by MIRI, as there are only 1 dwarf
AGN candidate and 1 high-$z$ AGN candidate also identified by other
selections.

Meanwhile, there are 56 CDF-S AGNs and 134 pre-JWST AGNs not identified by the
MIRI SED analysis, which contribute to $\sim$60% and $\sim$74% of the
corresponding catalog within the MIRI survey footprint. As pointed out in Lyu
et al. (2022a), there is no single method or wavelength can identify all the
AGNs. The results here provide very clear support to this argument — despite
the much improved AGN hunting capability offered by MIRI, there are always
some AGN population(s) left out and the combination of all possible selections
across the electromagnetic spectrum is necessary to complete the AGN census.

To illustrate the new discovery space provided by MIRI in terms of AGN
bolometric luminosity, Figure 15 shows the detection limits of the Chandra
X-ray and JVLA radio bands as a function of redshift. In the X-ray, the
Chandra data can only probe the most luminous MIRI AGNs, which is just the tip
of the iceberg. This is consistent with the fact that only 33 out of 217 MIRI
AGNs (15%) have reported X-ray luminosity that passes the AGN selection
criteria. Most of the MIRI AGN sample are much fainter and the MIRI limit is
about one order of magnitude deeper than the Chandra one.

Figure 15: $L_{\rm AGN,bol}$ detection limits as a function of redshift for
different bands.

Regarding the radio emission, there is a wide range of intrinsic variation of
its strength from radio-loud to radio-quiet populations and we only show the
AGN bolometric luminosity limits from the JVLA data from some average
templates (see Lyu et al. 2022a for details about the template models). It is
very clear that the current JVLA data is not deep enough to detect the radio
emission from the radio-quiet MIRI AGNs and any detected radio emission is
most likely coming from the host galaxies (Alberts et al., 2020). The JVLA
data can probe the radio properties of bright radio-loud MIRI AGNs but would
miss relatively faint radio-loud AGNs.

###  4.3 The Relative Performance of Different Selection Methods

To compare the yields of different selection methods and the possible
overlaps, we used the UpSet visualization tool (Lex et al., 2014) to show the
intersection of different AGN samples in Figure 16. Similar to the conclusions
reached in Lyu et al. (2022a), the top three most efficient selection
techniques are SED fittings, X-ray to radio luminosity cut ([X2R]) and X-ray
luminosity cut, which identify 97.5% of the whole sample. For the remaining
AGNs, 1.6% are identified by variability and 0.9% by the JWST-JVLA radio-loud
AGN selection.

Figure 16: Comparison of AGN sample selected with different methods and their
overlaps in the SMILES footprint, visualized with the UpSet technique. The
sample intersections of AGNs selected with different methods are plotted as a
matrix with bar charts on the left and top to show the corresponding sample
sizes. Each row corresponds to one selection method, and the bar charts on the
left show the number of AGN identified with a given method. Each column
corresponds to a possible intersection: the filled-in cells indicate which
selection method is part of the intersection. The lines connecting the filled-
in cells show in which direction the plots should be read. The bar charts on
the top give the size of the AGN sample identified with the corresponding
intersection of various selection techniques. We further break each vertical
bar into different colors to show the relative fraction of normal, dwarf and
high-$z$ AGNs within each subset. We highlight the subsets contributed by JWST
SED selections in red.

Despite the substantial improvement of the AGN SED identification with JWST
data, about 34% of the known AGNs in the field have been missed and most of
them have been identified by [X2R] (23% of the whole AGN sample). Meanwhile,
53% of the AGN are only revealed by the JWST SED analysis. The other selection
methods such as [xtype_lumcut], [var], [jwst_rl] all include unique AGNs that
are solely picked out by one method, despite their much smaller contribution
compared to the [jwst_sed] sample. Given the discussion in Section 4.2, the
yields of all the other selection methods are limited by the depth of X-ray or
radio data and there should be additional galaxies detected by current MIRI
data that have AGNs missed by the current SED analysis.

Regarding the different AGN groups (primary, extended and high-$z$), the vast
majority of extended-sample AGN candidates (89%) and high-$z$ AGN candidates
(100%) have been identified through the MIRI SED analysis. For all the normal
AGNs in massive galaxies at $z<4$, the MIRI SED analysis has identified 114
out of 211 these objects ($\sim$54%). Notably, after merging all AGN samples
together, 18 out of 20 high-$z$ AGN candidates ($\sim$92%), 88 out of 109
dwarf AGN candidates (81%) and 72 out of 211 normal AGNs ($34\pm 4$%) are only
identified by MIRI SED fittings, where the error is from the sample size. In
our previous study (Lyu et al., 2022a), which was less effective at
identifying AGNs due to less complete data but covered a five times larger
area, we found that $\sim$ 20% of the AGNs were very strongly obscured and 26%
were undetected in the deepest Chandra data, qualitatively in agreement with
the new estimate of the fraction missed in previous studies.

###  4.4 Comparison to MIRI AGN Studies in other fields

Pre-JWST efforts to use infrared photometry to identify AGNs are reviewed by
Lyu & Rieke (2022a). Although they began with IRAS, they came into their own
with the wide and deep surveys obtained with Spitzer and WISE (e.g., Lacy et
al., 2004; Stern et al., 2005; Alonso-Herrero et al., 2006; Donley et al.,
2008; Mateos et al., 2012; Asmus et al., 2020; Hviding et al., 2022). The
possibilities are greatly expanded with JWST.

Based on four MIRI pointings from CEERS, Yang et al. (2023) reported 102 SF-
AGN mixed systems and 25 AGNs from a total of 560 MIRI detections over a sky
coverage of $\sim$ 9 arcmin2 by fitting the galaxy SEDs with CIGALE. If we
combine their SF-AGN mixture and AGN categories, their reported AGN number
density is $\sim$14 arcmin-2. Our fits include SF-AGN mixtures, so they can be
compared directly. We find significantly lower numbers: 3.4 arcmin-2 for the
main sample and 2.9 arcmin-2 for the extended one, for a total of $\sim$ 6.3
arcmin-2. The high AGN numbers in Yang et al. (2023) presumably come with a
caution because of the possibility of significant contamination by low-mass
galaxies. Some other important distinctions between the two studies are that:
(1) we cover a larger field, with five times as many overall MIRI detections
and with more ultra-deep ancillary data; (2) we have more accurate redshifts,
with $\sim$ 40% spectroscopic ones and somewhat more accurate photometric ones
for the rest; and (3) we separate galaxies assumed to have PAH emission from
those without on the basis of mass/metallicity, whereas they leave this
difference as a free parameter.888They reported anomalously weak PAH features
in their median star forming SED. However, for the more massive galaxies in
our sample at the relevant redshifts, the PAH features have strengths similar
to local galaxies, as shown both by our fits in Figure 5 and by Spitzer
spectra of similar galaxies amplified by lensing, e.g., Rigby et al. (2008).

Based on the same CEERS dataset, Kirkpatrick et al. (2023) found $\sim$ 3 AGNs
per arcmin-2 at F1000W based on mid-IR color selections, compared to our
$\sim$5 AGNs per arcmin-2 in the same band. There are two likely reasons for
this low yield: (1) their template fitting is based on a single set of
templates from Kirkpatrick et al. (2015) in which AGN SEDs are represented by
power laws with a narrow range of slopes and thus do not represent the full
range of AGN SED variations; (2) color-color plots are less diagnostic than
SED fitting, as discussed extensively in Lyu & Rieke 2022a. Consequently, the
AGN numbers identified in this way are expected to be less than those from SED
analysis. Another limitation of the Kirkpatrick et al. (2023) study is that
their templates include strong PAH emission for the normal SFGs and do not
capture the IR behavior of lower-metallicity galaxies, which may result in
misclassifying some dwarf galaxies with strong warm dust emission as obscured
AGNs.

##  5 The Obscured AGN population

Given the AGN sample constructed from this work, we now discuss the obscured
AGN population seen at different wavelengths.

###  5.1 The Fraction of Obscured AGNs from SED analysis

We first characterize the obscured AGN fraction of MIRI-selected AGNs and
explore its dependence on the AGN luminosity and redshift. Our discussion will
focus on the 114 MIRI-selected AGNs at $L_{\rm AGN,bol}>10^{10}~{}L_{\odot}$,
$M_{*}\gtrsim 10^{9.5}~{}M_{\odot}$ and $z$=0–4. These criteria ensure a
reasonably large sample to carry out statistical studies that cover a large
range of source properties while minimizing the complications caused by dwarf
and high-z AGN candidates where the selections are less robust.

Depending on the observed wavelengths and available spectral features, the
definition of what is an obscured AGN varies in the literature. To first
order, the obscuration of an AGN, i.e., whether the AGN can be seen or not, at
a given band is caused by two major effects: (1) how much of the AGN emission
can be diminished by obscuring materials (e.g., a dusty torus); (2) how
strongly the host galaxy emission dilutes the AGN light. Our SED fittings
provide direct measurements of both effects at rest-frame UV to mid-IR. We
define AGN obscuration in three ways:

  * •

optically obscured: regardless of the AGN intrinsic luminosity and dust
attenuation, if the observed AGN light fraction at rest-frame 0.1–1 ${\rm\mu
m}$ is less than 20% of the whole galaxy, we count it as an optically obscured
AGN;

  * •

IR obscured: the near- to mid-IR attenuation level of the AGN component is
described by tau_ir, which can be transferred to the silicate strength
following equation $S10=0.2-tau_{\rm IR}/5.5$ (negative value for absorption)
(Lyu et al., 2022a). We define an AGN as IR obscured if $S10<0$, corresponding
to tau_ir$>$1.1;

  * •

IR heavily obscured: we define an AGN as IR heavily obscured if tau_ir$>$3.85.
This corresponds to the silicate strength of $S10<-0.5$, indicating
significant dust attenuation (e.g., Goulding et al., 2012).

In a statistical sense, the significance of AGN obscuration increases from
optically obscured, IR obscured to IR heavily obscured, though the definition
of the first case is complicated by AGN-host galaxy contrast as well as the
SMC extinction law.

Figure 17: The obscured AGN fraction as a function of AGN luminosity (left)
and object redshift (right). See text for the details.

In Figure 17, we show how the obscured AGN fractions change with different
source properties. All three obscuration fractions first increase with AGN
bolometric luminosity from $L_{\rm AGN,bol}\sim 10^{10}~{}L_{\odot}$ to
$L_{\rm AGN,bol}\sim 10^{11.5}~{}L_{\odot}$, with the slope increasing from
optical obscured, IR obscured to IR heavily obscured, and then dropping
towards higher luminosity in a consistent way. For redshift evolution, all the
obscured AGN fractions increase as a function of object redshift. We discuss
the implications of these trends below.

Previous studies have repeatedly shown that the obscured AGN fraction
decreases with increasing AGN luminosity (e.g., Lawrence, 1991; Simpson, 2005;
Lusso et al., 2013), so the trend in the left panel of Figure 17 is a surprise
at first glance. However, most of these studies have been limited to $L_{\rm
AGN,bol}\gtrsim 10^{11}L_{\odot}$. At the low-luminosity end, the obscured
fraction has been found to increase with AGN luminosity in the X-ray (e.g.,
Burlon et al., 2011). In particular, Buchner et al. (2015) did show a similar
trend with the X-ray obscuration level peaking at $L_{X}\sim 10^{43.5}$ erg
s-1 and declining both above and below this value around Cosmic Noon (see
also, e.g., Brightman & Nandra 2011 for a similar trend reported at low
redshifts). Adopting the bolometric luminosity correction of $K_{X}\sim 11$
(Duras et al., 2020), this corresponds to $L_{\rm AGN,bol}\sim
10^{11}~{}L_{\odot}$, consistent with the peak luminosity of our SED-inferred
obscuration.

From a theoretical perspective, AGN obscuration occurs from the gas inflow
that feeds the central SMBH, where the disk becomes unstable due to turbulence
that produces a torus-like structure or dusty wind (e.g., Wada 2012, 2015;
Hopkins et al. 2012; see review by Netzer 2015). Based on high-resolution
hydrodynamic simulations, the obscuring gas column density presents positive
correlations with the AGN luminosity (e.g., Blecha et al., 2018; Trebitsch et
al., 2019). When the AGN becomes sufficiently bright, strong feedback is
expected to blow out the surrounding material, reducing the chance of
obscuration (e.g., Sanders et al., 1988; Hopkins et al., 2008).

Based on the analysis of the rest-frame UV to mid-IR SEDs, our study suggests
an increasing fraction of obscured AGNs towards higher redshifts. Previous
work in the X-ray based on the gas column density $N_{H}$ has also revealed an
increasing fraction of obscured AGNs from the local Universe to Cosmic Noon
(e.g., Ueda et al., 2003; La Franca et al., 2005; Treister & Urry, 2006;
Buchner et al., 2015; Vijarnwannaluk et al., 2022). Such results are possibly
associated with the large amount of gas in high redshift galaxies (e.g.,
Carilli & Walter, 2013) as well as the more vigorous growth of SMBHs in the
early Universe (Inayoshi et al., 2020).

Finally, AGN selections in our extended and highz samples (dwarf and $z>4$
galaxies) are less secure or complete so we only report the integrated numbers
of the obscured fractions for reference. For extended sample AGN candidates,
the fraction of optically obscured, IR obscured and IR heavily obscured AGN is
86%, 76% and 42%. These numbers are subject, of course, to further
confirmation of the AGN nature of individual members of this sample. For the
AGN candidates at $z>4$, the fraction of optically obscured, IR obscured and
IR heavily obscured AGN is very roughly 54%, 75% and 46%. Given that the MIRI
data is shallower than that from NIRCam, the obscured AGN fraction among these
galaxies may be underestimated.

###  5.2 X-ray Bright but Mid-IR Faint AGNs

As shown in Section 4.2, our MIRI+NIRCam data is much deeper than other multi-
wavelength data used for AGN selection in terms of source bolometric
luminosity. Naively, one would expect that the superb JWST data can pick out
all the AGNs found at other wavelengths. However, this is not the case.
Notable examples are 23 X-ray bright AGNs not identified by SED fittings. In
other words, these systems are X-ray detected bright AGNs but very faint in
the optical to the mid-IR.

In Figure 18, we illustrate the locations of such objects on the X-ray
luminosity vs the rest-frame 6 ${\rm\mu m}$ AGN luminosity plot. Previous
studies have established a strong correlation between the absorption-corrected
X-ray luminosity and the AGN luminosity at 6 $\mu$m for most AGNs (e.g., Asmus
et al., 2015; Stern, 2015; Mateos et al., 2015; Chen et al., 2017). For AGNs
identified by both MIRI SED fitting and X-rays, such a trend also exists
despite a large scatter. However, there are many X-ray bright AGNs with very
weak AGN 6$\mu$m emission (left side of Figure 18). In fact, even if we ignore
the SED decomposition and assume all the observed flux is from the AGN, eleven
objects cannot be moved near the typical $L_{X,int}-L_{6\mu m,AGN}$ relation.

Figure 18: The relation between X-ray luminosity and AGN continuum luminosity
at rest-frame 6 $\mu$m for AGN in the SMILES footprint.

Figure 19: Similar to Figure 5 but for sample X-ray bright AGNs not confirmed
by MIRI SED fittings. We show the Chandra image cutout of the source with a
FOV of 6″$\times$6″. According to the CDF-S catalog (Luo et al., 2017), the
intrinsic X-ray luminosity $\log(L_{\rm X,int}/{\rm erg~{}s}^{-1})$ and the
corresponding gas column density $\log(N_{H}/{\rm cm}^{-2})$ of each object
are: MIRI 419 – 41.58, 23.47; MIRI 757 – 42.70, 23.65; MIRI 779 – 42.72,
21.84; MIRI 1382 – 43.12, 22.46.

We have carefully inspected these objects. Besides 8 objects with noisy SEDs
or degenerate fitting results, 15 do not show any AGN evidence in the UV-to-
mid-IR SEDs. In Figure 19, we present some example SEDs with the cutouts from
NIRCam, MIRI and Chandra. Besides the unresolved SED analysis, all these
systems are extended in both NIRCam and MIRI images, consistent with the lack
of AGN signatures. Meanwhile, their emission is very strong in the X-ray.
Similar AGNs have been reported previously (LaMassa et al., 2019; Lyu et al.,
2022a).

One possible explanation could be dust-deficient AGNs that lack the hot/warm
dust emission (Lyu et al., 2017). If we limited the sample to X-ray detected
AGNs, the fraction of such objects is about 16–25%, consistent with the
typical fraction of dust deficient AGN in an IR-unbiased sample as reported in
Lyu et al. (2017). Another possibility is that the X-ray emission of these
AGNs is boosted by, e.g., jets. Typically, the X-ray jet emission is
associated with radio loud AGNs. Among these 23 X-ray bright AGNs, 15 are
detected by the JVLA at 3 GHz – one is a confirmed radio-loud AGN, two are
ambiguous cases and 12 are consistent with typical SFGs. It is therefore
likely that the X-ray bright but mid-IR faint AGNs arise from both the radio-
loud and radio-quiet populations.

###  5.3 MIRI AGNs without X-ray Detections

As described in Section 4.2, a large fraction of our MIRI AGNs do not have
X-ray detections (see also Figure 18). The intrinsically faint AGNs will not
be detected in X-ray even if they are unobscured due to the shallower Chandra
detection limits compared to MIRI. For relatively bright AGNs, the X-ray non-
detected AGNs can be interpreted as being Compton-thick or intrinsically X-ray
weak. We now focus on the first possibility and put some constraints on the
Compton-thick AGN fractions with the aid of the MIRI SED results.

Since our SED fittings provide a measurement of the AGN bolometric luminosity,
we can convert them to the equivalent X-ray luminosity for different gas
column densities and compare with observations. In Figure 20, we show the
ratio between the SED-derived bolometric luminosity and the X-ray luminosity
at 2–10 keV against the AGN bolometric luminosity for MIRI-identified AGNs at
different redshifts. We compute an upper limit to the 2–10 keV luminosity
assuming no extinction for objects without X-ray detections. We also plot the
bolometric correction for the extinction-corrected AGN X-ray luminosity at
2–10 keV from Duras et al. (2020) as well as the expected correlation if the
X-ray emission is Compton-thick with gas column density $N_{H}\sim 10^{24}$
cm-2. The relative location of an AGN on this diagram is determined by its
intrinsic luminosity as well as the gas column density. AGNs above the red
curve can be counted as Compton-thick AGNs.

Figure 20: Bolometric to X-ray luminosity fraction as a function of AGN
luminosity at different redshifts for MIRI-identified AGNs. On the bottom of
each panel, we denote the total number of AGNs as $N$ and Compton-thick AGN
fraction $f_{\rm CT}$ for $z<3.0$ and X-ray Intrinsic Weak AGN fraction
$f_{\rm XIW}$ for $z>3.0$.

The weak X-ray emission of most AGNs at relatively low redshift is well-
explained in terms of absorption (Saade et al., 2022; Wang et al., 2022).
Above $z\sim$1, we found the obscured AGN fraction gradually increases with
redshift, possibly due to an increase in the number of Compton thick sources.
Our finding of $\sim$ 34% X-ray-undetected AGNs with SEDs suggestive that they
are Compton thick aligns well with the typical explanation of diffuse X-ray
background (e.g., Akylas et al., 2012). Although there are some variations, a
summary of its properties is: (1) $92.7\pm 13.3$ % of the hard X-ray (2–7 keV)
background is resolved into individual sources by the Chandra 7 Msec exposure
(Luo et al., 2017; Cappelluti et al., 2017); (2) models to fit the background
are centered on the AGN population at redshifts of 0.5 - 2; and (3) many of
these models invoke a population of Compton thick AGNs not detected in X-rays
to fit the background around 30 keV (e.g., Gilli et al., 2007; Moretti et al.,
2009; Treister et al., 2014). It should be possible to advance our
understanding of the X-ray background significantly through deep JWST surveys
for infrared-discovered AGNs, although such studies are beyond the scope of
this paper.

Although the increase of the fraction of X-ray undetected AGN population
apparently continues for $z>$3, Compton absorption becomes weak at the
relevant rest-frame energies999For example, at z = 5.28, the redshift of MIRI
1104, the hard X-ray 2–7 keV band is at rest energies of 12–45 keV. Rather
than obscuration, some of these AGNs may be intrinsically weak in the X-ray.
Indeed, the existence of such objects has been suggested previously by, e.g.,
Leighly (2004); Simmonds et al. (2016); Lyu et al. (2022a). Stacking the X-ray
observations for all six of the AGNs in Figure 9 yields non-detections of
$3.6\pm 5.0\times 10^{-18}$ erg cm-2 s-1 for the 0.5 $-$ 2 keV band and
$-1.13\pm 1.28\times 10^{-17}$ erg cm-2 s-1 for the 2 $-$ 7 keV band. These
stacked values reinforce the result in Figure 20 that the high redshift AGNs
are in general very X-ray weak.

We now explore the implications of this result. The X-ray output of high
redshift quasars in general does not show any deficiency of X-ray emission
with an average $\alpha_{OX}$ of $\sim-1.5$ for those with measured values
(omitting two outliers), using the results from Li et al. (2021). This does
not exclude weak X-rays from the undetected members, which are the majority of
the sample, although these authors conclude that the relation of the X-ray
outputs to the other parameters of the quasars is similar to that for lower
redshift quasars. The low X-ray fluxes for the lower luminosity high-z AGNs
could arise due to Compton thick absorption with $n_{H}\gtrsim 10^{25}$ cm-2,
but such high absorbing columns are very rare locally. The infrared AGN-driven
SED shapes for the six sources in Figure 9, extending down to 1–2 $\mu$m and
even shorter wavelengths, are similar to those of typical local Type-2 Seyfert
galaxies, rather than extreme Compton thick examples like NGC 1068. A hint
toward an alternative explanation is provided by the very X-ray weak AGNs in a
number of low-metallicity dwarf galaxies (Dong et al., 2012; Simmonds et al.,
2016; Cann et al., 2021; Bohn et al., 2021; Latimer et al., 2021), often two
orders of magnitude below the values expected by analogy with AGNs in massive
galaxies. That is, the behavior might be associated with low metallicity in
modest mass high redshift host galaxies.

Hard X-rays from AGNs are produced by Compton scattering in the coronal
regions above their accretion disks. Therefore, a process that disrupts the
coronal regions could suppress the X-ray outputs. The dwarf galaxies have
strong AGN-driven outflows that are believed to be effective at clearing gas
from the galaxies (Bohn et al., 2021); these outflows are also candidates to
disrupt the coronal regions. A possible issue with this explanation is that a
small sample indicates that the outflow and coronal line strengths may be
correlated (Bohn et al., 2022). Overall, this is a complex situation requiring
much larger samples than provided in this paper to test, particularly since
AGN variability is likely to influence the observational properties of any
sample of AGNs.

##  6 The Blind Men and the Elephant: How to Build a Complete AGN Sample?

The process of selecting AGNs is like the story of the blind men and the
elephant. Every study is limited by the wavelength regime and survey depth, so
the results are inevitably biased and incomplete. One might expect that a
complete picture can be obtained by combining all the results together as this
ancient parable hints. Unfortunately, the AGN population is more diverse than
elephants. Regardless of the disagreements they have, the blind men at least
knew they were standing around the same animal and the elephant would reveal
consistent features when a blind person touched the right part. In contrast,
astronomers typically work on different samples built at different wavelengths
and the AGNs seen in one study can be totally missed in another study due to
e.g., the intrinsic variation of AGN properties, the apparent obscuration
caused by dust and gas, the efficiency of the selection method for AGN, and
the data quality that the selection has to be based on.

As also shown in previous efforts, there is no single wavelength regime or
selection technique that can pick out all AGNs. In Section 4, we show the
existence of X-ray bright AGNs without relevant IR signatures in the JWST SEDs
and bright mid-IR AGNs without X-ray detections. As a result, we should
combine all the multi-wavelength datasets and use as many selection methods as
possible to improve the AGN census (see also Lyu et al. 2022a). Albeit the
great advances that have been (or will be) brought by JWST, new X-ray and IR
telescopes are still needed to complete the census of the obscured AGN
population at high-redshift. The time-domain surveys from, e.g. the Roman
Telescope, are also expected to bring significant progress on AGN selections.

In terms of selection techniques, the so-called gold standards for AGN
identification, such as very broad emission lines, bright X-ray emission or
clear hot dust signatures, are definitive but certainly not complete
indicators. Given how complicated and diverse the AGN population is, we could
miss a substantial fraction of AGNs if the selections rely on these most
obvious features, as seen in Section 5. Traditionally, when people call some
object an “AGN candidate”, there is a hint that some relatively robust method
may confirm its nature in the future, which is actually not guaranteed to
happen. A practical solution is to refine one selection to make sure it is
robust enough, so we can drop the ‘candidate’ modifier and trust the selected
objects as real AGNs. In this regard, we need to review our techniques
carefully and compare the performance with other techniques. For example, for
SED fittings, the SED models should match the observations and have enough
predictive power, rather than just providing a good fit to known AGNs merely
due to the large parameter space. When we are confident that the technique
works for bright objects where other multi-wavelength selections can also
confirm the AGN nature, we can cautiously extend its usage to faint objects.

Besides refining the AGN selections, there is also a balance between
robustness and completeness that needs to be considered carefully. When multi-
wavelength data is available, it is reasonable to relax the criteria and use
multiple (relaxed) selections to complete the AGN census. From the AGN hints
from other wavelengths, selections at one band can be calibrated and improved
(e.g., Donley et al., 2012; Hviding et al., 2022). Meanwhile, we should also
be aware of the limitations of each selection technique so it is not used
inappropriately. For example, we should not adopt the classical AGN color
criteria or SED fittings with normal galaxy templates to look for AGN in the
dwarf galaxy population (e.g., Hainline et al., 2016). As we enter a new
regime, relevant tools need to updated or developed.

Given the various factors that influence the observed AGN behavior at
different wavelengths, whether an AGN sample is complete or not is actually
determined by how we define the term “AGN” observationally, which can be very
controversial (e.g., the extended discussion about how to classify LINERs, now
understood to be a mixed category). We should keep in mind that our ultimate
goal is not to collect all kinds of AGN samples but to understand the physics
behind the AGNs themselves and the SMBH-galaxy interaction. In this sense, one
intriguing future direction is to combine the observed AGN statistics with the
predicted observables from cosmological simulations that involve reasonable
treatments of various selection biases. This approach has more physics behind
it and can mitigate the issues of obscured AGNs that may not be easily
revealed by the data. A direct comparison between models and observations
benefits everyone — theorists can calibrate their models with data to make
better predictions and observers are motivated to look for new populations
suggested by theorists. If one day we could build such a physical framework
that matched all the key empirical correlations between SMBHs and galaxies
with good predictive power for the observed AGN statistics at different
wavelengths, our long journey to complete the AGN census might reach its final
end.

##  7 Summary

In this work, we presented the first results on AGN selection and demographics
from SMILES, a JWST Cycle 1 GTO program that has targeted the central region
of GOODS-S with eight MIRI filters from 5.6 to 26 ${\rm\mu m}$. Combining the
MIRI data with JADES NIRCam and HST photometry at shorter wavelengths, we
conducted comprehensive SED analyses of 3273 MIRI-sources and reported 217
AGNs over a footprint of 34 arcmin2. This MIRI-selected AGN sample includes
111 AGNs in the primary sample of normal massive galaxies, 86 AGN candidates
in the extended sample of dwarf galaxies and 20 high-z AGN candidates at z $>$
4\. Our major conclusions are:

  * •

We reached a total number density of SED-identified AGNs of $\sim$ 6.6
arcmin-2, which is over two times larger than that for the confusion-limited
Spitzer/MIPS 24 $\mu$m observations of the same field (Lyu et al., 2022a). In
addition, compared to the deepest X-ray survey by Chandra in the same
footprint, our relatively shallow MIRI surveys ($\sim$2.17 hours per pointing)
yield 2.4 times more AGNs, of which 80% are new discoveries.

  * •

AGNs in our primary sample of massive galaxies should be sufficiently luminous
for detection by previous searches in other wavebands, assuming they are not
obscured. However, 34% of the AGNs found in our study do not have previous
identifications. This result indicates that 34 $\pm$ 6% of AGNs have been
missed due to obscuration, where the error arises from the modest sample size
(114);

  * •

For the first time at z $\sim$ 2, JWST/MIRI is detecting significant numbers
of AGNs in low-mass galaxies [$\log(M_{*}/M_{\odot})<9.5$] – from our
conservative SED identifications, they are comparable in number with the AGNs
we have found in the more massive primary sample;

  * •

We confirm the existence of X-bright but mid-IR faint AGNs and suggest that
they may be dust-deficient AGNs that do not present strong hot dust SED
features, or AGNs with X-ray emission boosted by e.g. a jet, or a mixture of
both populations;

  * •

$\sim$80% of the MIRI-selected AGNs do not have X-ray detections. Most of them
are too faint to be detected by Chandra, some of which are likely to be
Compton-thick AGNs. For MIRI AGNs without X-ray detections at higher redshifts
($z\sim$4–6), they may be intrinsically weak in the X-ray, possibly due to the
lack of a strong corona component due to their lower metallicity environment.

  * •

Based on SED analysis, we find the obscured AGN fraction increases with AGN
luminosity towards L${}_{AGN}\sim$ 1011 L⊙ and then drops at higher
luminosity. The obscured AGN fraction also increases with redshift, regardless
of the detailed definitions. For AGNs at $z>4$, it is likely half of the
population is heavily obscured.

  * •

Despite the comprehensive MIRI photometric data and the substantially improved
SED selections, about 28% of the known AGN (candidates) in the field have been
missed by the SED analysis, indicating a huge diversity of AGN properties that
challenges the completion of the AGN census. In other words, no single method
or wavelength can identify all the AGNs and a combination of multi-wavelength
dataset and selection techniques is always desired;

Our results demonstrate the unique power of JWST MIRI for AGN selection and
characterization. This paper summarizes first results from a modest initial
survey and we look forward to larger and more ambitious studies.

This work was supported by NASA grants NNX13AD82G and 1255094. We thanks
Andras Gaspar for his help on MIRI PSF construction. AJB has received funding
from the European Research Council (ERC) under the European Union’s Horizon
2020 Advanced Grant 789056 “First Galaxies”. PGP-G acknowledges support from
grant PID2022-139567NB-I00 funded by Spanish Ministerio de Ciencia e
Innovación MCIN/AEI/10.13039/501100011033, FEDER, UE. C.N.A.W. was supported
by the NIRCam Development Contract NAS5-02105 from NASA Goddard Space Flight
Center to the University of Arizona. This work is based on observations made
with the NASA/ESA/CSA James Webb Space Telescope. The data were obtained from
the Mikulski Archive for Space Telescopes at the Space Telescope Science
Institute, which is operated by the Association of Universities for Research
in Astronomy, Inc., under NASA contract NAS 5-03127 for JWST. These
observations are associated with program #1207.

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##  Appendix A AGN Identification from Weak Pa $\alpha$

Figure 21 shows the distribution of H$\alpha$ to 12 $\mu$m luminosities for
Type 2 AGNs, relative to the average value for star forming galaxies. We have
selected 12 $\mu$m for this comparison both because of the availability of
all-sky data bases, and because the flux at this wavelength is representative
of the total luminosity of many galaxies (Spinoglio & Malkan, 1989). The
integrated H$\alpha$ fluxes for the star forming galaxies have been taken from
Moustakas & Kennicutt (2006) and the 12 $\mu$m fluxes from IRAS (which has a
sufficiently large beam that the flux is close to incorporating the full
galaxy). The luminosity at the latter wavelength is taken to be proportional
to $\lambda$Fλ. The H$\alpha$ fluxes for the AGNs are from Winter et al.
(2010); Rose et al. (2013) (which provide flux-calibrated line strengths),
with 12 $\mu$m fluxes from ALLWISE. The ratio for Type 1 AGNs, obtained from
the same references plus Osterbrock & Pogge (1987), is nominally just a factor
of about two below the average for the star forming galaxies, but Figure 21
shows that the majority of Type 2 AGNs fall more than an order of magnitude
low in this ratio.

Figure 21: Ratio of H$\alpha$ to 12 $\mu$m luminosity for local Type 2 AGNs,
normalized to the average value for star forming galaxies.

This behavior is a product of extinction, as is shown by the much higher
average ratios for the Type 1 AGNs. If the extinction follows a normal
interstellar law and is a foreground screen, the ratio for Pa $\alpha$ would
typically still be a factor of 3–4 weaker relative to star forming galaxies.
It is likely that the extinction is partially due to optically thick
structures (e.g., the circumnuclear torus) and it may be due to relatively
large grains due to the harsh environment around the nucleus, and hence be
more neutral than the typical interstellar behavior; both effects would
increase the difference.

Since both the Pa $\alpha$ and infrared luminosities are indicators of the
star formation rate, this discriminator can be applied by declaring any galaxy
where the SFR indicated by Pa $\alpha$ falls significantly below that
estimated from the infrared flux is a candidate to host an AGN. This test can
be implemented using the FRESCO spectra. At higher redshifts, it may be
possible to use FRESCO spectra showing Pa $\beta$.

We have carried out a preliminary analysis using only galaxies with detected
Pa $\alpha$ by FRESCO. There are ten galaxies with SFRs calculated from the Pa
$\alpha$ luminosity more than three times lower than indicated by the flux
density at 18 $\mu$m; we accept this as the indicator of an AGN based on the
study of obscuration levels in local LIRGs by Alonso-Herrero et al. (2006).
They are listed in Table 2. Additional candidates can probably be identified
among MIRI-detected sources with accurate redshifts in the appropriate range
and upper limits on Pa $\alpha$ from the FRESCO spectra.

Table 2: AGNs identified from weak Pa $\alpha$ MIRI ID | RA | DEC | SFR(18 $\mu$m)/SFR(Pa $\alpha$) | Previous AGN Selectionsaa1 = from IR SED; 2 = from IR colors; 3 = from X-ray luminosity; 4 = from X-ray to radio flux ratio; 5 = from radio loudness; 6 = from optical spectrum; 7 = from variability
---|---|---|---|---
302 | 53.07644 | -27.84873 | 7.5 | 1, 3,4, 5
509 | 53.10938 | -27.83495 | 6.1 |
1183 | 53.14350 | -27.78327 | 3.2 |
1425 | 53.16288 | -27.76723 | 8.3 | 1, 3, 4, 6, 7
1637 | 53.17287 | -27.74456 | 4.3 |