# SMILES Initial Data Release: Unveiling the Obscured Universe with MIRI
Multi-band Imaging

Stacey Alberts Steward Observatory, University of Arizona, 933 North Cherry
Avenue, Tucson, AZ 85719, USA  Jianwei Lyu (吕建伟) Steward Observatory,
University of Arizona, 933 North Cherry Avenue, Tucson, AZ 85719, USA  Irene
Shivaei Centro de Astrobiología (CAB), CSIC-INTA, Ctra. de Ajalvir km 4,
Torrejón de Ardoz, E-28850, Madrid, Spain  George H. Rieke Steward
Observatory, University of Arizona, 933 North Cherry Avenue, Tucson, AZ 85719,
USA  Pablo G. Pérez-González Centro de Astrobiología (CAB), CSIC-INTA, Ctra.
de Ajalvir km 4, Torrejón de Ardoz, E-28850, Madrid, Spain  Nina Bonaventura
Steward Observatory, University of Arizona, 933 North Cherry Avenue, Tucson,
AZ 85719, USA  Yongda Zhu Steward Observatory, University of Arizona, 933
North Cherry Avenue, Tucson, AZ 85719, USA  Jakob M. Helton Steward
Observatory, University of Arizona, 933 North Cherry Avenue, Tucson, AZ 85719,
USA  Zhiyuan Ji Steward Observatory, University of Arizona, 933 North Cherry
Avenue, Tucson, AZ 85719, USA  Jane Morrison  Steward Observatory, University
of Arizona, 933 North Cherry Avenue, Tucson, AZ 85719, USA  Brant E. Robertson
Department of Astronomy and Astrophysics, University of California, Santa
Cruz, 1156 High Street, Santa Cruz, CA 95064, USA  Meredith A. Stone Steward
Observatory, University of Arizona, 933 North Cherry Avenue, Tucson, AZ 85719,
USA  Yang Sun  Steward Observatory, University of Arizona, 933 North Cherry
Avenue, Tucson, AZ 85719, USA  Christina C. Williams NSF’s National Optical-
Infrared Astronomy Research Laboratory, 950 North Cherry Avenue, Tucson, AZ
85719, USA  Steward Observatory, University of Arizona, 933 North Cherry
Avenue, Tucson, AZ 85719, USA  Christopher N. A. Willmer Steward Observatory,
University of Arizona, 933 North Cherry Avenue, Tucson, AZ 85719, USA

###### Abstract

The James Webb Space Telescope (JWST) is revolutionizing our view of the
Universe through unprecedented sensitivity and resolution in the infrared,
with some of the largest gains realized at its longest wavelengths. We present
the Systematic Mid-infrared Instrument (MIRI) Legacy Extragalactic Survey
(SMILES), an eight-band MIRI survey with Near-Infrared Spectrograph (NIRSpec)
spectroscopic follow-up in the GOODS-S/HUDF region. SMILES takes full
advantage of MIRI’s continuous coverage from $5.6-25.5\,\mu$m over a $\sim 34$
arcmin2 area to greatly expand our understanding of the obscured Universe up
to cosmic noon and beyond. This work, together with a companion paper by Rieke
et al., covers the SMILES science drivers and technical design, early results
with SMILES, data reduction, photometric catalog creation, and the first data
release. As part of the discussion on early results, we additionally present a
high-level science demonstration on how MIRI’s wavelength coverage and
resolution will advance our understanding of cosmic dust using the full range
of polycyclic aromatic hydrocarbon (PAH) emission features from
$3.3-18\,\mu$m. Using custom background subtraction, we produce robust
reductions of the MIRI imaging that maximize the depths reached with our
modest exposure times ($\sim 0.6-2.2$ ks per filter). Included in our initial
data release are (1) eight MIRI imaging mosaics reaching depths of
$0.2-18\,\mu$Jy ($5\sigma$) and (2) a $5-25.5\,\mu$m photometric catalog with
over 3,000 sources. Building upon the rich legacy of extensive photometric and
spectroscopy coverage of GOODS-S/HUDF from the X-ray to the radio, SMILES
greatly expands our investigative power in understanding the obscured
Universe.

infrared survey $-$ infrared astronomy $-$ galaxies: evolution $-$ Active
galactic nuclei $-$ data reduction

††software: astropy (Astropy Collaboration et al., 2022), Photutils (Bradley
et al., 2023); scikit-image (van der Walt et al., 2014); eMPT (Bonaventura et
al., 2024); JWST Calibration Pipeline (Bushouse et al., 2023); WebbPSF (Perrin
et al., 2014); Prospector (Johnson et al., 2021); dynesty (Speagle, 2020);
parrot (Alsing et al., 2020; Mathews et al., 2023).

##  1 Introduction

The infrared wavelength regime has proven to provide numerous powerful
diagnostics for understanding galactic ecosystems over cosmic time, despite
often lagging behind the optical in terms of wavelength coverage, sensitivity,
and resolution (e.g. Soifer et al., 2008). The Mid-Infrared Instrument (MIRI;
Rieke et al., 2015; Bouchet et al., 2015; Wright et al., 2023) on the James
Webb Space Telescope (JWST; Rigby et al., 2023a; Gardner et al., 2023)
represents a great leap forward in our ability to utilize the mid-infrared
(mid-IR) in understanding the Universe. JWST’s 6.5 meter aperture affords MIRI
a gain in resolution by a factor of $\sim 7$ in beam diameter $-$ or $\sim 50$
in beam area $-$ compared to the Spitzer Space Telescope (Werner et al., 2004;
Gehrz et al., 2007), cutting through the confusion limit that ultimately set
Spitzer’s sensitivity, particularly for the Multiband Imaging Photometer
(MIPS; Rieke et al., 2004; Dole et al., 2004). MIRI is, in fact, background-
limited across its full wavelength range ($5-25.8\mu$m). At $12\,\mu$m, MIRI’s
sensitivity is a factor of $\sim 1000\times$ better than the confusion limit
of the Wide-field Infrared Survey Explorer (WISE; Wright et al., 2010), the
largest jump in JWST’s capabilities over previous facilities. For an expanded
discussion of these gains in the context of the history of infrared astronomy,
see our companion paper, Rieke et al. (2024).

While the paradigm shifts expected with JWST are just starting to be realized,
MIRI is already far exceeding initial expectations. Surprisingly, this has
included joining JWST’s near-infrared instruments111The near-infrared JWST
instruments Near-Infrared Camera (NIRCam; Rieke et al., 2023) and the Near-
Infrared Spectrograph (NIRSpec; Jakobsen et al., 2022) are referenced in this
paper. in opening up our view of cosmic dawn. MIRI has now detected emission
lines in a galaxy at $z=12.33$ (Zavala et al., 2024) and emission-line boosted
photometry in a galaxy at $z=14.32$ (Helton et al., 2024, submitted to Nature
Astronomy), the current spectroscopically-confirmed highest redshift galaxy
(Carniani et al, 2024, submitted to Nature). NIRCam-dark, MIRI-only detections
(Pérez-González et al., 2024a) have opened up the possibility of a new
population of high-$z$ emission line galaxies accessible to JWST. MIRI’s view
of the rest-frame optical in the first $\sim 500$ million years after the Big
Bang (see also Hsiao et al., 2024; Calabro et al., 2024) is providing
indispensable information on the earliest galaxies.

Figure 1: The SED of an infrared luminous SFG (Kirkpatrick et al., 2015) shown
at three redshifts ($z=0,2,4$) relative to eight MIRI filters at
$5.6-25.5\,\mu$m (bottom, F1130W is not shown). From the local Universe
through cosmic noon ($1<z<3$), the MIRI bands probe at least one strong mid-
infrared dust (PAH) feature plus stellar continuum emission in the rest-frame
near-infrared. In the middle panel, the SFG is normalized to $10\,\mu$Jy at
$21\mu$m (the original sensitivity goal for SMILES, see R24) and we show the
$5\sigma$ detection limits achieved by SMILES (Table 1). We additionally show
a $50\%$ composite AGN spectrum (gray dotted line, normalized at $21\,\mu$m;
Kirkpatrick et al., 2015) for contrast; note the excess infrared emission at
rest-frame $3-5\,\mu$m above the SFG continuum. Beyond cosmic noon ($z\gtrsim
3-4$), MIRI continues to provide an anchor on the stellar continuum and/or
strong optical or near-infrared emission lines.

At later times $-$ the originally envisioned purview of the instrument $-$
MIRI covers a large range of important spectral regimes (Figure 1). At
$3\lesssim z\lesssim 9$, MIRI is providing rest-frame near-infrared and
optical anchors (Figure 1) in studies of dusty (Álvarez-Márquez et al., 2023a;
Rodighiero et al., 2022) and/or optically dark galaxies and the so-called
Little Red Dots (LRDs; Williams et al., 2023a; Wang et al., 2024c; Pérez-
González et al., 2024b); quiescent galaxies (Alberts et al., 2023, Ji et al,
2024, in prep); and emission line galaxies during reionization (e.g. Álvarez-
Márquez et al., 2023b; Caputi et al., 2023; Rinaldi et al., 2023a, b). During
cosmic noon ($1\lesssim z\lesssim 3$) $-$ the peak epoch in star formation and
black hole activity (Madau & Dickinson, 2014; Hickox & Alexander, 2018) $-$
the prevalence of cosmic dust means that MIRI plays a crucial role in
quantifying dust-obscured activity in star-forming galaxies (SFGs; e.g.
Ronayne et al., 2023; Lin et al., 2024; Shivaei et al., 2024; Spilker et al.,
2023) and Active Galactic Nuclei (AGN; e.g. Yang et al., 2023a; Kirkpatrick et
al., 2023; Lyu et al., 2024). Furthermore, MIRI’s coverage of multiple dust
emission features up to this epoch (Figure 2) will place strong constraints on
the properties of small grain dust $-$ namely polycyclic aromatic hydrocarbons
(PAHs) $-$ which are known to be correlated with the (obscured) star formation
rate (e.g. Rujopakarn et al., 2013; Shipley et al., 2016; Shivaei et al.,
2024) and likely molecular gas (e.g. Cortzen et al., 2019).

In this paper, we present the Systematic Mid-infrared Instrument Legacy
Extragalactic Survey (SMILES), the largest and thus far only survey that
includes MIRI imaging in eight bands, providing full wavelength coverage over
the $5.6-25.5\,\mu$m range. This multi-band coverage allows SMILES data to be
applied to many scientific studies not equally accessible to surveys covering
only 2 or 3 bands at wavelengths longer than $8\,\mu$m. SMILES is a blank
field MIRI imaging survey with follow-up NIRSpec spectroscopy optimized for
studying galaxy populations at cosmic noon and beyond in the GOODS-S/HUDF
field (Giavalisco et al., 2004; Beckwith et al., 2006).

Figure 2: A typical PAH spectrum redshifted to $z=0.25$ (black line; Lai et
al., 2020). At low redshift, the MIRI F770W-F2550W bands cover all of the
strong PAH features from $6-18\,\mu$m with $R\sim 5$ resolution. The hatched
regions show the PAH complex boundaries as defined in Draine et al. (2021). We
define the total PAH luminosity $L_{\rm PAH}$ as the sum of these features,
modeled and continuum subtracted through SED fitting. A weak dust feature, the
3.3$\mu$m PAH, is in the F410M filter (inset) and can be measured as color
excess by subtracting flanking continuum bands (Inami et al., 2018; Lai et
al., 2020). Pre-JWST, studies of this feature were primarily limited to very
massive, local galaxies (e.g. Lai et al., 2020).

The historical context and scientific motivations for SMILES and its legacy
among MIRI surveys are presented in a companion paper Rieke et al. (2024); in
this work, we present an overview of the ongoing science with SMILES, the
technical design, data reduction, photometric catalog, and first data release
of SMILES, which includes the MIRI imaging (NIRSpec spectra will be made
public in a future release). In Section 2, we provide an overview of the scope
and early results of SMILES, as well as a small science demonstration on the
power of MIRI in probing PAH features. Sections 3, 4 and 5 describe the data
acquisition, reduction, and photometric extraction of the MIRI imaging,
respectively. In Section 6, we overview the SMILES NIRSpec follow-up
observations to the MIRI imaging. Section 7 concludes by discussing the
properties of the SMILES catalog and describes the data release. All
magnitudes in this work are in AB units (Oke & Gunn, 1983).

Figure 3: The distribution of exposure times as a function of survey area and
filter for current and future MIRI surveys (with MIRI as prime or in
parallel), through Cycle 3 (roughly from deep and narrow to shallow and wide):
MIRI Deep Imaging Survey (MIDIS; PIDs 1283, 6511, PI: G. Ostlin) [right
triangles]; JADES MIRI parallels (Alberts et al., 2024, in prep, Eisenstein et
al., 2023a) and JADES Origin Field (JOF; Eisenstein et al., 2023b) [hexagons];
Abell2744/MACS0416 (PID 5578, PI E. Iani) [pentagons]; CEERS (Yang et al.,
2023b) [upright triangles], SMILES (this work) and SMILES-Grism (PID 4549, PI
G. Rieke) [stars]; MIRI EGS Galaxy and AGN Survey (MEGA; PID 3794, PI A.
Kirkpatrick) [squares]; the MIRI Early Obscured-AGN Wide Survey (MEOW; PID
5407, PI G. Leung) [diamonds]; PRIMER (PID 1837, PI J. Dunlop) [circles];
COSMOS-3D (PID 5893, PI K. Kakiichi) [downward triangles], and COSMOSWeb
(Casey et al., 2022) [left triangle]. SMILES is currently the only survey to
take full advantage of the MIRI wavelength coverage.

##  2 Science with SMILES

A full treatment of the original science cases that motivated the SMILES
design is presented in our companion paper, Rieke et al. (2024) (hereafter
R24). In this section, we provide a brief summary of the scope of the survey,
early results, and a small science demonstration.

###  2.1 Scope and Design

As demonstrated in Figure 1, the MIRI imaging bands cover multiple prominent
spectral features, spanning the rest-frame near- to mid-infrared up to cosmic
noon ($1<z<3$). Up to this epoch, the primary type of infrared source is
massive star-forming galaxies, whose SEDs are dominated by broad PAH features
in MIRI’s wavelength range. At the peak in activity at cosmic noon, Luminous
Infrared Galaxies (LIRGs, $L_{\rm IR}=10^{11-12}\,{L_{\odot}}$) forming stars
in dust-rich environments account for the bulk of the star formation density
(e.g. Le Floc’h et al., 2005; Pérez-González et al., 2005) and commonly reside
on the main sequence of SFGs (Elbaz et al., 2011; Magnelli et al., 2011;
Noeske et al., 2007, e.g.), in sharp contrast to their rarity in the local
Universe (e.g. Sanders et al., 2003). In addition, obscured AGN are a
prominent infrared population at cosmic noon, whose intrinsic near- to mid-IR
SEDs are dominated by emission from warm dust that manifests as infrared
continuum excess above the stellar continuum and/or PAH features (Figure 1,
middle panel). It’s been well established that the bulk of black hole growth
during this epoch is obscured (e.g. Del Moro et al., 2016; Padovani, 2016;
Ananna et al., 2019); however, pre-JWST, our ability to identify obscured AGN
was limited (R24). MIRI is uniquely suited among the JWST instruments to
provide unbiased samples of massive star-forming galaxies and AGN in this
critical era.

Figure 4: (left) The SMILES MIRI imaging footprint (red) in the GOODS-S/HUDF
region relative to a subset of the ancillary datasets available: JADES/NIRCam
Deep and Medium imaging (Rieke & the JADES Collaboration, 2023; Eisenstein et
al., 2023a) and JADES/MIRI Deep parallels (Alberts et al., 2024, in prep),
JEMS NIRCam medium band imaging (Williams et al., 2023b), FRESCO NIRCam grism
and imaging (Oesch et al., 2023), the HST WFC3 F160W footprint (Grogin et al.,
2011), JVLA 6 and 3 GHz imaging out to the half-power radius (Alberts et al.,
2020), and Chandra 7 Ms X-ray imaging (Luo et al., 2017). (right) A zoom-in of
the SMILES field showing the three SMILES/NIRSpec MSA pointings with targets
assigned to slits marked (see Section 6).

SMILES was thus designed to take a census of and investigate the nature of
these luminous infrared populations at $1<z<3$ through MIRI imaging (see R24
for more details) with follow-up NIRSpec Multi-Object Spectroscopy (MOS;
Ferruit et al., 2022; Rawle et al., 2022). SMILES covers $34.5$ arcmin2
centered on the GOODS-S/HUDF field with MIRI imaging in eight bands, with
integration times and achieved sensitivities listed in Table 1. As shown in
Figure 3, SMILES occupies a parameter space similar to other wide, modest
depth MIRI surveys but with the distinction of continuous wavelength coverage
from $5.6-25.5\,\mu$m. To date, the only other blank-field MIRI survey,
current or in Cycle 3, with imaging in more than three bands is CEERS (Yang et
al., 2023b). Equally important (R24) is the ancillary data available; the
SMILES footprint is shown in Figure 4 compared to highly complementary
datasets. GOODS-S/HUDF was chosen to maximize the quality and quantity of
ancillary data needed for the survey goals. This includes ultra-deep NIRCam
imaging from the JWST Advanced Deep Extragalactic Survey (JADES; Rieke & the
JADES Collaboration, 2023; Eisenstein et al., 2023a) and the JWST
Extragalactic Medium-band Survey (JEMS; Williams et al., 2023b), HST imaging
from multiple surveys (e.g. Grogin et al., 2011), deep ALMA imaging and
spectroscopy (Walter et al., 2016; Dunlop et al., 2017; Hatsukade et al.,
2018), ultra-deep imaging surveys in the X-ray with Chandra (Luo et al., 2017)
and radio with VLA (Alberts et al., 2020), and copious spectroscopy (Momcheva
et al., 2016; Bacon et al., 2023; Bunker et al., 2023), including NIRCam grism
spectroscopy from the First Reionization Epoch Spectroscopically Complete
Observations survey (FRESCO; Oesch et al., 2023). Including JWST coverage,
$\gtrsim 30$ bands of high-resolution photometry from the UV$-$radio are
available over the SMILES footprint, as demonstrated in Figure 5. In the next
section, we give an overview of some of the early science results from SMILES,
along with some of the future directions they suggest.

Filter | Ngroups | Nint | Exp Time | Aperture Radius |  $5\sigma$ Limit | ETC v3.0 | Improvement
---|---|---|---|---|---|---|---
|  |  | s | (65% EE) |  $\mu$Jy (AB) |  $\mu$Jy | Factor
(1) | (2) | (3) | (4) | (5) | (6) | (7) | (8)
F560W | 59 | 1 | 654.9 | 0.48′′ | 0.21 (25.6) | 0.46 | 2.2
F770W | 78 | 1 | 865.8 | 0.42′′ | 0.20 (25.7) | 0.52 | 2.6
F1000W | 58 | 1 | 643.8 | 0.36′′ | 0.39 (24.9) | 0.83 | 2.1
F1280W | 68 | 1 | 754.8 | 0.42′′ | 0.62 (24.4) | 1.3 | 2.1
F1500W | 101 | 1 | 1121.1 | 0.49′′ | 0.75 (24.2) | 1.4 | 1.9
F1800W | 68 | 1 | 754.8 | 0.54′′ | 1.8 (23.3) | 3.1 | 1.7
F2100W | 32 | 6 | 2186.7 | 0.60′′ | 2.8 (22.8) | 3.1 | $-$
F2550W | 18 | 4 | 832.5 | 0.72′′ | 17 (20.8) | 18 | $-$
Table 1: The exposure setup and depths of the SMILES mosaics. Notes: Column 4:
Total exposure time over four dithers. Each pointing was observed for a total
of 2.17 hours of science time including all filters. Columns 5-8: The
$5\sigma$ point source sensitivity measured from our data (column 6) compared
to ETC v3.0 predictions (column 7). The point source sensitivity was measured
using randomly placed circular apertures with a radius equal to the $65\%$
encircled energy (EE; column 5) of our PSFs (Section 5.1.2) with aperture
corrections applied. The ETC predictions were obtained using the same size
aperture and a $1-2\arcsec$ ($1.5-2.5\arcsec$ at $\geq 21\mu$m) annulus to
estimate background sky and noise, with the background corresponding to
Dec.$\,15$, 2022 in GOODSS. Column 8: The ratio of our measured $5\sigma$
point source sensitivity to the ETC prediction. Figure 5: A $z=1.76$ galaxy
shown in 38 bands of photometry, from X-ray to radio, in $4\arcsec\times
4\arcsec$ cutouts. The dramatic increase in resolution and sensitivity
provided by MIRI relative to Spitzer/IRAC and MIPS is demonstrated in this
figure; bulge and disk features are clearly seen in the MIRI bands. A line-of-
sight companion grand design spiral at $z\sim 1.3$ to the upper right is
apparent in HST, NIRcam, and MIRI F560W/F770W, then fades into the longer MIRI
bands, indicating low dust content. We additionally include Herschel/PACS 100
and $160\mu$m to demonstrate that this gain has no equivalent in the far-
infrared, leaving a large gap between JWST and ALMA. Figure 6: Cutouts (top)
and SED (bottom) of a $z\sim 0.2$ star-forming galaxy where the MIRI bands
provide excellent sampling of the strong PAH features and the weaker, poorly
studied 3.3PAH is in the NIRCam medium band F410M (solid blue filter). The
medium-band color excess image (middle cutout) shows that the 3.3PAH lights up
in the same spatial regions as the 7.7PAH complex, which is wide enough to
dominate the flux observed in F1000W (solid teal filter). Filters containing
emission features are shown as solid lines, continuum filters with dashed
lines. Errorbars are smaller than the datapoints.

###  2.2 Early Results with SMILES

####  2.2.1 Obscured AGN

A new census of AGN in GOODSS-S has recently been presented in Lyu et al.
(2024), identifying 111 AGN in massive ($M_{*}>10^{9.5}M_{\odot}$) galaxies
with the inclusion of SMILES data, $34\%$ of which were previously
unidentified. This increase is remarkable if one considers the uniquely
extensive datasets in the GOODS-S/HUDF region. Lyu et al. (2024) evaluated the
full range of AGN search techniques, finding largely independent contributions
from infrared SED fitting (e.g. including SMILES); selection via X-ray
luminosity and X-ray to radio ratios; variability; radio loudness and flat
spectrum radio emission; and optical spectroscopy. Through this, they
confirmed previous arguments that a panchromatic approach is necessary to take
a census of AGN with high completeness (e.g. Donley et al., 2008; Delvecchio
et al., 2017; Alberts et al., 2020; Lyu et al., 2022).

Beyond the above, there are a number of new questions raised by Lyu et al.
(2024). First, a comparatively large sample of AGN candidates was found in
low-mass galaxies (see also Yang et al., 2023a). This is reminiscent of the
discovery of low-luminosity AGN in nearby dwarf galaxies (Reines et al., 2013,
2020; Moran et al., 2014) and may have important implications for our
understanding of the role of AGN in galaxy evolution. However, confirming the
nature of these sources will be challenging due to their faint nature and
uncertainties in the intrinsic SEDs of both low-luminosity AGN and their low-
mass, low-metallicity hosts. A second discovery is the identification of a few
dozen AGN candidates at $z>4$ with SMILES, likely mostly obscured and in many
cases very weak in the X-ray. Again confirmation and expansion of this
population in conjunction with other ongoing efforts to study AGN at high-$z$
(e.g. Maiolino et al., 2023; Harikane et al., 2023; Übler et al., 2023) will
be important to constrain AGN evolution.

####  2.2.2 PAH features to cosmic noon and beyond

The multiple PAH features prominent in the mid-infrared (Figures 1$-$2)
provide a wealth of information on small-grain dust properties, are a proxy
for obscured star formation (e.g. Shipley et al., 2016), and constrain the
broader role of dust in the chemistry and energy exchange in the Interstellar
Medium (ISM; e.g. McKinney et al., 2021). An initial study of PAH features in
$0.7<z<2$ SFGS has been presented by Shivaei et al. (2024), which demonstrates
that the MIRI bands are powerful tracers of these features, probing at least
one major PAH feature up to $z\sim 2.5$ if F2100W imaging is available. In
that work, the PAH mass fraction (qPAH), obscured SFR, and obscured fraction
are quantified in 443 galaxies down to SFRs of $<1\,{M_{\odot}}$ yr-1 and
masses $\sim 10^{9}\,{M_{\odot}}$. This SFR limit is over an order of
magnitude lower than what was achievable at cosmic noon with Spitzer (Elbaz et
al., 2011; Magnelli et al., 2011; Reddy et al., 2008; Shipley et al., 2016;
Shivaei et al., 2015, 2017), showing that even at moderate depth, MIRI can
reach galaxies on and below the main sequence during cosmic noon.

This work highlights open questions we can now address. (1) Looking at the
trend of PAH mass fraction with decreasing metallicity via the mass-
metallicity relation (Sanders et al., 2021), Shivaei et al. (2024) found that
the local qPAH-metallicity trend appears to hold at cosmic noon. However, the
modest sample of robust detections at very low metallicity in SMILES motivates
deeper multi-band MIRI surveys combined with spectroscopy to directly measure
metallicities, a goal of our NIRSpec follow-up (Section 6). (2) The mid-
infrared can now be calibrated as a SFR indicator down to sub-LIRG main
sequence galaxies at cosmic noon (Alberts et al., 2024b, in prep). And (3),
JWST opens up avenues for understanding the diagnostic power of tracing
multiple PAH features simultaneously; we revisit this briefly in Section 2.3.

####  2.2.3 Morphology of obscured star formation

The ability of MIRI to spatially resolve mid-infrared emission up to cosmic
noon deserves special mention. Figure 5 demonstrates the gain in resolution in
MIRI, a $\sim 7\times$ improvement over previous facilities and only a factor
of $\sim 2$ coarser resolution at 7.7$\,\mu$m than NIRCam at $4.4\,\mu$m.
Figure 6 shows a particularly spectacular example: spatially resolved PAH
emission in F1000W in a galaxy at $z\sim 0.3$ (see Section 2.3). Taking
advantage of SMILES multi-band imaging, a spatially resolved study of the
6.2$\,\mu$m PAH is now possible up to $z\sim 1.5$. The critical advantage
SMILES here is that multiple bands allow the selection of the filter
containing the PAH band at different redshifts, maintaining the highest
possible diffraction-limited angular resolution (Florian et al., 2024, in
prep). This work is finding that high redshift LIRGs have extended star
formation, as traced by the PAH via MIRI, and less disturbed morphologies (as
traced by the stellar mass distribution via NIRCam) than LIRGs in the local
Universe. This points to a disparate evolution at cosmic noon, where luminous
dusty starbursts are not triggered by major mergers as is the case locally and
as predicted in a common framework for massive galaxy evolution (e.g. Hopkins
et al., 2008). Instead minor interactions and gas accretion from the
Intergalactic Medium (IGM) likely play bigger roles.

####  2.2.4 Optical dark galaxies and little red dots

Pushing past the original design scope of SMILES R24, Williams et al. (2023a)
and Pérez-González et al. (2024b) used SMILES data as part of studies of
optically dark galaxies and Little Red Dots (LRDs; e.g. Matthee et al., 2023;
Labbé et al., 2023; Furtak et al., 2023) at $3\lesssim z\lesssim 7$. At these
redshifts, MIRI provides the rest-frame near-infrared (Figure 1), which is
needed to robustly measure the properties of very red galaxies (Williams et
al., 2023a). For LRDs in particular, NIRCam imaging presented strong evidence
for reddened AGN (e.g. Labbé et al., 2023) and broad-line AGN have been
confirmed in some LRDs (e.g. Greene et al., 2023; Wang et al., 2024a). The
behavior of the LRD SEDs at observed wavelength of 10 $\mu$m and longer with
SMILES, however, very clearly shows a rollover, indicative of substantial
contribution by stellar photospheric emission, both on average via stacking
(Williams et al., 2023a) and in individual galaxies (Pérez-González et al.,
2024b). The latter is required for exploring this unknown and possibly
heterogeneous population; however, SMILES is only deep enough to obtain good
SED constraints for a few examples. A follow-up program to obtain much deeper
MIRI observations is needed.

###  2.3 PAHs at low redshift: a small science demonstration

As discussed in Sections 2.2.2-2.2.3, the MIRI bands are a powerful tracer of
mid-infrared dust emission, probing at least one strong PAH feature up to
$z\sim 2.5$. At low redshift ($z\lesssim 0.3-0.6$), MIRI photometry covers all
strong PAH features at 6.2, 7.7, 11.3$\,\mu$m and even the generally weaker
features at 12.8 and 17$\,\mu$m (Figure 2) with a spectral sampling equivalent
to $R=\lambda/\Delta\lambda\sim 5$. This capability is a powerful complement
to MIRI spectroscopy, which will provide an unmatched look at small grain dust
physics and the role of dust within the ISM (e.g. García-Bernete et al., 2022;
Young et al., 2023) for small samples, given the small FOV and/or limited
wavelength coverage of the MIRI Medium Resolution Spectrometer (MRS; Wells et
al., 2015; Argyriou et al., 2023a) and Low Resolution Spectrometer
($5-14\,\mu$m, LRS; Kendrew et al., 2015).

Here we provide a high-level demonstration of the parameter space opened by
MIRI+NIRCam using 15 $z_{\rm spec}\sim 0.2-0.5$ (log
${M_{\star}/M_{\odot}}=8-11$) star-forming galaxies222AGN have been removed
from the sample using the catalog in Lyu et al. (2024). where MIRI covers the
major PAH features at $5-18\,\mu$m. We fit their HST, NIRCam, and MIRI
photometry using Bagpipes using a non-parametric star formation history,
flexible attenuation curve, and dust emission parameterized based on the
(Draine et al., 2021) models. An example SED and cutouts are shown in Figure 6
for a galaxy at $z\sim 0.2$ where the F1000W filter is dominated by the 7.7PAH
and has a distinctive morphology compared to F560W, which is showing stellar
continuum. The well-sampled mid-IR SED allows us then to estimate the
continuum-subtracted luminosity in these PAH features: $L_{\rm PAH}\equiv
L_{\rm 6.2+7.7+11.3+12.8+17}$.

At this redshift, a weaker, but significant PAH feature, the $3.3\,\mu$m PAH,
can be extracted from the NIRCam medium bands from JADES and JEMS as a color
excess relative to continuum-dominated filters (Figure 2, inset). Due to being
a narrow, weak feature, pre-JWST the 3.3PAH was only accessible in luminous
galaxies in the local Universe and is still poorly understood (Sandstrom et
al., 2023). As a feature composed primarily of very small, neutral grains
(e.g. Maragkoudakis et al., 2020), the 3.3PAH may provide useful constraints
on dust physics when combined with the stronger features and may be
preferentially destroyed in harsh radiation fields, a controversial topic
being explored by multiple local JWST surveys (e.g. Schroetter et al., 2024).
In addition, the 3.3PAH has particular importance to high redshifts studies
with JWST, as it is the only dust emission feature accessible at $z\gtrsim 3$
(Spilker et al., 2023).

In Figure 7, we show the luminosity of the 3.3$\,\mu$m PAH333We measure the
3.3PAH luminosity by interpolating the continuum under the line based on
flanking filters close to medium bands that contain the emission feature based
on spectroscopic redshifts. The relevant medium band is F410M, F460M, or F480M
for our sample. Subtracting the continuum, we measure a color excess, from
which we derive a line flux based on the medium band filter bandwidth and the
throughput at the observed wavelength of the line. relative to $L_{\rm PAH}$
and the stellar mass derived from SED fitting. Our sample extends the linear
correlation seen at $z\sim 0$ in the GOALS sample (Lai et al., 2020) to both
higher redshifts and lower masses than previously possible. Furthermore, in
Figure 6, difference images showing the color excess demonstrate that the
spatial distribution of the 3.3PAH is qualitatively similar to the 7.7PAH in
the MIRI F1000W filter. This high-level demonstration of the SMILES data shows
the parameter space opened by MIRI imaging. A full spatially-resolved analysis
of this sample will be presented in future work. A spectroscopic analysis of
the 3.3PAH using NIRCam grism from the FRESCO survey will be presented in Lyu
et al., 2024, in prep.

Figure 7: The luminosity of the 3.3PAH measured from NIRCam medium-bands as a
function of the total PAH luminosity as estimated from SED fitting of MIRI
photometry, which covers 5 major PAH features at $z\sim 0.2-0.5$ (Figure 6).
Stars (circles) show color excess in the medium bands measured at $>3\sigma$
($<3\sigma$). The squares show the GOALS $z\sim 0$ LIRG sample. The color bar
shows the stellar mass; our demonstration confirms that the 3.3PAH scales with
stronger PAH features in star-forming galaxies at higher redshift and lower
mass than previously possible.

##  3 Technical Design and Data Acquisition

SMILES (PID 1207; PI: G. Rieke) consists of 8 mosaics (filters F560W, F770W,
F1000W, F1280W, F1500W, F1800W, F2100W, and F2550W) built from 15 MIRI
pointings in a $5\times 3$ configuration centered on
$\alpha,\delta$=3:32:34.8,-27:48:32.6 (J2000, Figure 4). The center and the
aperture position angle of 33 degrees were chosen to maximize overlap with the
projected JADES NIRcam imaging (which was successfully achieved) and enable
scheduling during a period of low background. The majority of the MIRI imaging
was obtained in Dec 2022, save for two tiles (2 and 6; see Figure A.1) that
suffered from guide star failures shortly into observations. These pointings
were reobserved in all filters in Jan 2023; see Appendix A for further
details. The final area covered is $\sim 34.5$ arcmin2 with $5\%$ overlap
between tiles, ensuring relatively uniform minimum exposure time across the
mosaics (Figure A.1). As discussed in the previous section, the exposure setup
was aimed at the analysis of AGN and SFGs at cosmic noon, which we projected
would require exposure times ranging from $\sim 650-2100$ s corresponding to
$5\sigma$ limits of 0.25-4 $\mu$Jy via ETC v1.0 for F560W - F2100W (Table 1).
For F2550W, where the thermal background of the telescope is very high, we
aimed at detecting galaxies at $z\sim 1$ down to $25\,\mu$Jy. This resulted in
a total of 2.17 hr of science time per pointing. Due to the relatively short
exposure times in each band, we opted not to add coordinated parallel
observations.

To maximize sensitivity via long ramps, we opted for single integrations
except for F2100W and F2550W where the background would saturate. Standard
4-pt dithers (optimized for point sources to maximize area with uniform
exposure time) were chosen to ensure adequate PSF sampling and minimize
detector artifacts and cosmic ray hits/showers (Section 4.1) and to improve
background subtraction (Section 4.2.2). The recommended readout mode FASTR1
was used and filters were observed in order of increasing wavelength to
mitigate persistence (Dicken et al., 2024). Total exposure times and the
breakdown into groups and integrations are shown in Table 1.

##  4 Data Processing

This section describes the data processing for SMILES, which is broadly
similar to the reductions described in previous works (e.g. Yang et al.,
2023b; Morrison et al., 2023; Pérez-González et al., 2024b). Data reduction is
done using v1.12.5 of the JWST Calibration Pipeline (Bushouse et al., 2023)
with CRDS444Calibration Reference Data System: https://jwst-crds.stsci.edu/
version 11.17.7 and CRDS context 1188. Custom external routines are used for
identifying warm pixels, background subtraction, and astrometry correction at
appropriate stages of the pipeline. We start with the uncalibrated (_uncal)
files obtained from
MAST555https://mast.stsci.edu/portal/Mashup/Clients/Mast/Portal.html.

###  4.1 Stage 1: Detector Processing

Stage 1 (calwebb_detector1) of the JWST pipeline performs detector-level
processing on raw ramp data, including corrections for non-linearity, reset
anomaly, first and last frame effects, dark subtraction, and jump detection
specific to the MIRI imager. Based on pre-flight ground-based and
commissioning data (see also Yang et al., 2021, for MIRI data reduction
simulations), we adopt the default parameters in the pipeline for this step; a
detailed discussion of the non-ideal behaviors and the corrections implemented
in stage 1 of the pipeline is presented in Morrison et al. (2023). As noted in
that work, several artifacts are not currently addressed in the pipeline,
including persistence, the cruciform feature of the PSF at F560W and F770W,
interpixel capacitance, column and row striping, the first exposure effect,
and the brighter-fatter effect (Argyriou et al., 2023b). As noted in Section
3, we observed each of our pointings in order of increasing wavelength to
mitigate persistence. We address the cruciform and row/column stripping in
Sections 4.2.2 and 5.1.2, respectively, and we discuss here the recent
implementation in the pipeline of a correction for large cosmic ray events,
called ‘cosmic ray showers’ due to their ability to affect a large number of
pixels on the MIRI detectors. We expect the other effects to be minimal in our
data.

Figure 8: Examples of uncorrected cosmic ray showers in $10\arcsec\times
10\arcsec$ cutouts from the final F560W mosaic (see Section 4.3).
Approximately ten such artifacts show up across SMILES as $5\sigma$ detections
in our photometric catalog (Section 5.1) after running the correction with
CRDS context 1188.

The correction for cosmic ray showers was implemented in Pipeline v1.8.0 as an
optional part of the Jump Detection step. Cosmic ray showers manifest as a
wide area, low-level excess in counts below the typical detection threshold
for a direct cosmic ray hit. Unlike the near-infrared detector snowballs,
showers in MIRI do not have a saturated core and are not circular, making
detection and removal difficult.

We apply the cosmic ray showers correction to the F560W - F1500W observations,
finding that the correction removes or improves many, but not all visible
shower events. Source detection (Section 5.1.1) plus visual inspection against
NIRCam images finds on order 10 cosmic ray showers are missed that are
significant enough to produce a $5\sigma$ artifact picked up by our photometry
algorithms, a rate of $\sim 0.3$ arcmin2 or $\sim 0.7$ per MIRI FOV. Two
examples are shown in Figure 8.

We do not apply the correction to F1800W-F2550W as testing showed that low-
level arcsec to arcmin scale artifacts were introduced into the science and
error images. This is possibly due to the correction not being optimized in
the regime where the noise is dominated by thermal background from the
telescope. We note that skipping the correction for F1800W-F2550W was later
made the default in CRDS context 1202.

The output of stage 1 is uncalibrated count rate (i.e. the slope or _rate)
images, which are fed into stage 2.

###  4.2 Stage 2: Image Calibration

Stage 2 of the pipeline (calwebb_image2 for imaging) produces calibrated
science images for each individual exposure. This includes associating a WCS
object with each exposure, dividing out a flatfield, applying flux
calibrations, and resampling each exposure into a distortion-corrected 2D
image. We adopt the defaults for this stage in the pipeline minus background
subtraction, then apply additional processing to remove warm pixels (Section
4.2.1), apply custom background subtraction (Section 4.2.2), and correct the
astrometry (Section 4.2.3) on the resulting calibrated _cal files.

Figure 9: F770W (left) and F1800W (right) _cal exposure from stage 2 before
and after super-background subtraction. Note the row/column striping and
curved “tree ring” features in addition to the background gradient (Section
4.2.2). The histograms (top) show that the super-background subtraction
significantly reduces the rms over just subtracting the median sky level.

####  4.2.1 Removing Warm Pixels

The default pipeline contains two levels of outlier detection, in stage 1
during the Jump Detection Step (Section 4.1) and in Stage 3 (Section 4.3).
Early generation of SMILES mosaics through stage 3 revealed that faint
outliers had escaped both detection steps, resulting in noise peaks in the
resampled image with an often distinctive morphology: a central bright pixel
with four bright adjacent cross-pixels. Tracing these noise peaks back to the
individual _cal files identified them as warm pixels not masked by the
pipeline and not flagged as outliers as they are common across all
(contemporary on the order of 1-1.5 months) _cal files in the program. To
manually mask these warm pixels, we median stack all _cal files on a per-
filter basis and then flag pixels via the DQ mask that are $>3\sigma$ above
the median of all pixels of the median-stacked image. This method is robust in
masking real sources as we have 60-66 _cal files over 15 different pointings
in a relatively uncrowded field, ensuring that the stacked median image is
comprised of background sky noise. The percentage of pixels flagged as warm
ranges from 0.03-0.18$\%$ (corresponding to $\sim 300-2000$ pixels) across the
8 filters.

####  4.2.2 Custom Background Subtraction

Backgrounds in MIRI are a combination of sky background (dominated by zodi at
shorter wavelengths, then thermal emission and scattering from the telescope
at $\gtrsim 15\,\mu$m (Figure 10; Glasse et al., 2015; Rigby et al., 2023b)
and detector effects that can cause large-scale gradients, row/column striping
and tree rings that are largely static in detector coordinates for a given
dataset (Morrison et al., 2023). As discussed in (Dicken et al., 2024), these
latter effects are likely residuals from the current flat fielding and/or the
dark/reset anomaly correction in stage 1. Future improvement in these steps
will correctly address these multiplicative effects; however, for now, we
address them using background subtraction. This approach has been found to
result in a $<5\%$ difference in measured fluxes and is sub-dominant to other
flux measurement uncertainties (Dicken et al., 2024).

As an extragalactic field with no extended emission, we didn’t obtain
dedicated background observations for SMILES. In early testing, we opted to
manually create a median combined background from the science images on a per-
pointing basis, under the assumption that the background may significantly
vary spatially, using the strategy described in Dicken et al. (2024). We
found, as that work did, that small dither steps resulted in over-subtraction
forming a ‘halo’ around luminous sources (see their Figure 22). Second, this
technique didn’t fully address the row/column striping or large-scale noise
gradients as described above.

To improve on this, we instead adopted the “super-background” strategy (Pérez-
González et al., 2024b, see also Yang et al. (2023b) for a similar procedure),
which takes advantage of our large number of dithers ($60-66$) over 15
contemporaneous (within 1.5 months) pointings. This strategy works as follows:
we start with a full reduction without background subtraction using the
standard Pipeline to the final Stage 3 mosaics. From this, we create a source
mask with a high ($10\sigma$) threshold using the photutils create_source_mask
routine after performing basic background subtraction using Background2D. This
source mask is then mapped back onto the individual _cal files in detector
space and median filtering in rows and columns and subtraction of large-scale
2D gradients (again using Background2D is performed on the masked _cal files.
These filtered _cal files are then reprocessed through the Pipeline to final
mosaics. This process is repeated two more times with successively lower
source detection thresholds as well as dilation of the segmentation maps to
capture faint, extended emission and extended features of the PSF (see Section
5.1.2).

The final robust source mask is then mapped back to the original, un-filtered
_cal files and the median of each _cal file is subtracted to bring the median
background level to zero. Then for each unfiltered _cal file, a stacked median
‘super-background’ is created from all other zero-medianed, masked _cal files.
This super-background is scaled back to the median of the _cal file being
corrected, and then subtracted. Finally, row/column filtering and subtraction
of any remaining 2D background is done. These background-subtracted and
filtered _cal files are then astrometry-corrected (next section) and fed to
Stage 3.

Figure 9 compares a F770W _cal pre- and post-background subtraction. The pixel
rms is reduced roughly by a factor of two. Figure 10 shows that the background
levels of our mosaics are in excellent agreement with the zodi and thermal
backgrounds measured during commissioning in 2022 (Rigby et al., 2023b). A
discussion of the final mosaic quality will be presented in Section 4.3.

Figure 10: The background level in the SMILES mosaics (stars, observed Dec
2022) compared to low zodi background (orange), high zodi background (green),
and thermal background (blue) measured during commissioning in early 2022
(Rigby et al., 2023b). Figure 11: A F1500W, F1000W, F560W RGB image of SMILES.
The zoomed-in areas show $40\arcsec\times 40\arcsec$ regions in HST F606W,
NIRCam F444W, and matched MIRI and Spitzer bands, [F770W and IRAC Ch 4, top]
and matched MIRI and MIPS bands [F2100W and MIPS24, bottom]. MIRI’s resolution
at $7.7\,\mu$m is only $2\times$ coarser than NIRCam/F444W and $\sim 7\times$
finer than IRAC. This gain at $21\mu$m compared to MIPS allows MIRI to be
background- rather than confusion-limited across its wavelength range. Cutout
sets are shown at the same stretch.

####  4.2.3 Astrometry Correction

Figure 12: (left) The shifts in RA and Dec applied to each tile (represented
by different symbols) and filter (represented by different colors) to correct
their astrometry. The two groups demonstrate that astrometry corrections
should be applied on a per-tile basis. The largest shifts are $\sim 0.2\pm
0.1\arcsec$, within $3\sigma$ of the JWST absolute pointing accuracy
($1\sigma$ is shown by the error bar). (middle, right) The residual offsets
between the MIRI corrected catalog and its reference catalog for F770W, using
F560W as a reference (middle) and F1500W, using F1280W (right). The
astrometric residuals for these two bands are 8-12 mas.

We perform astrometry corrections on the background-subtracted _cal files
prior to stage 3. Due to early limitations in the astrometry correction built
into the Pipeline, we built external routines based on TweakReg that enable
more flexibility; namely, we required the ability to use an external reference
catalog of NIRCam sources, as the low source density of GAIA sources (Gaia
Collaboration et al., 2022) matched to MIRI detections in MIRI’s relatively
small FOV ($\sim 2.3$ arcmin2) makes it difficult-to-impossible to calculate a
robust correction. We note that later versions of the Pipeline include the
ability to use user-supplied external catalogs in stage 3.

We register the SMILES astrometry to the JADES NIRCam catalog, which itself is
astronomically aligned to GAIA as described in Rieke & the JADES Collaboration
(2023). Alignment is done on a per-tile basis, as each tile is a new visit and
subject to the JWST absolute pointing accuracy (0.1″, $1\sigma$, radial),
which is driven by the guide star catalog. Dither positioning uses the Fine
Guidance Sensor and is expected to have an accuracy of $\sim 2-4$ milli-arcsec
(mas) rms per axis with good stability666https://jwst-docs.stsci.edu/jwst-
observatory-characteristics/jwst-pointing-performance; as such, we apply the
same correction to all dithers in a tile.

MIRI is known to have filter-dependent boresight offsets; corrections on order
$2-40$ ($7-160$) mas in the column (row) direction are applied in the
Pipeline. Early tests in applying a single additional astrometry correction
via our reference catalog to all filters identified an additional uncorrected
offset in the SMILES F2100W images. To quantify this, we generated un-
astrometry-corrected mosaics and measured the relative offsets of sources
detected at SNR$>20$, taking the F770W image as the fiducial. We find that the
filter-to-filter offsets (after the boresight correction) are likely
negligible given the scatter for all the filters except for a $\sim 2\sigma$
offset for F2100W (in declination, $55\pm 37$ mas). We note, however, that the
scatter may be inflated due to morphological differences of the sources across
the wide wavelength range. As such, we calculate corrections for each filter
separately.

To perform the final astrometry correction, we generate a source catalog for
each tile (using windowed centroiding and robust deblending, see Section 5),
which is then compared to the reference catalog. We impose a SNR floor for
matches (SN$>20$, except for F2550W which uses SN$>10$ for the source
catalog), but forego any constraints on morphology after testing found that
having a large number of high SNR matches is as good or better than matching
only compact sources. Astrometric corrections are derived based on these
matches, allowing for shifts and rotations. Given the offset at F2100W
discussed above and to measure the astrometric accuracy of our mosaics as well
as possible, we perform the correction on a per-filter basis, using NIRCam
F444W detections in the JADES catalog for the initial correction at F560W and
then marching the correction up the filters (i.e. using a corrected F560W
reference catalog to correct F770W, F770W to F1000W, etc) up to
F1500W777Marching up the bands minimizes potential changes in morphology
mentioned above, which can affect centroiding and inflate the astrometric
scatter.. Due to the decreasing number of matches, the F1500W reference
catalog is used to correct F1800W and F2100W. A lack of robust matches between
F1500W and F2550W (which has a particularly short exposure time given its high
background, see Section 3) results in a poor correction; as we didn’t find a
significant offset between F770W and F2550W, we use the F770W correction
directly for F2550W.

Visual inspection is done to check the correction for each tile and the
Tweakreg tolerance parameter is varied from 0.5-2 to improve problematic
tiles. We find that for the two tiles that experienced guide star failures, we
needed to correct the astrometry separately for each visit to mitigate
different offsets introduced by the telescope absolute pointing. Figure 12
shows the shifts derived for each tile and filter; a typical shift in
declination is $0.2\pm 0.1\arcsec$, or two MIRI native pixels. Our final
astrometric accuracy ranges from $0.01-0.02\arcsec$ rms in RA and Dec for
F560W-F2100W888The astrometric accuracy at F2550W is slightly worse, $\sim
0.02-0.04\arcsec$ rms in RA, Dec, due to the shallowness of the data., 1/10 -
1/5th of a MIRI native pixel (Figure 12), which we confirm by one final
comparison between all filters and the JADES centroids.

###  4.3 Stage 3: Final Mosaics

Stage 3 of the pipeline (calwebb_image3) produces the final mosaics from the
background-subtracted and astrometry-corrected _cal files. Here, we skip the
tweakreg and skymatch steps due to our previous corrections. Outlier detection
and resampling are performed with the default setup with the exception of 1)
setting the pixel scale to $0.06\arcsec$, twice the JADES mosaicked pixel
size999The exact MIRI pixel size is $0.0599894\arcsec$, twice the NIRCam pixel
size which is matched to the effective pixel size of HST HLF mosaics. (Rieke &
the JADES Collaboration, 2023), 2) orienting the mosaics such that North is up
(rotation = 0), and 3) setting the array output size and reference pixel. The
latter ensures alignment of the pixel grid among all filters. We note that the
Pipeline generates an error image that includes Poisson and readout noise, but
not correlated pixel noise. We find that these errors are underestimated by
factors of $2-3$ compared to random aperture uncertainties measured from the
mosaics directly (see Section 5.1.2, see also Yang et al., 2023b, for an
alternative method for generating an rms map).

A final F1500W, F1000W, F560W RGB image is shown in Figure 11, with zoomed
panels showing the vast improvement in sensitivity and resolution in the mid-
infrared compared to previous facilities (see e.g. Ji et al., 2024, in prep
Magnelli et al., 2023; Shen et al., 2023, for spatially resolved studies with
MIRI). The depth of the final mosaics (Table 1) is measured by masking sources
and mosaic edges (70 pixels deep) and measuring the median rms in boxes
($d=3\times\mathrm{FWHM}$) placed in a mesh across the mosaics using the
photutils Background2D routine. Assuming $65\%$ encircled energy (EE)
apertures measured from (semi-)empirical Point Source Functions (PSFs, see
Section 5.1.2) and a factor of $1.5\times$ for correlated pixel noise due to
resampling to smaller pixels101010This factor was derived by measuring the
median standard deviation in 500 randomly placed $d=1\arcsec$ apertures on the
native and 0.06$\arcsec$ resolution mosaics (L. Costanin, private
communication), we reach aperture-corrected $5\sigma$ point source
sensitivities of $\sim 0.2-17\,\mu$Jy [$25.7-20.8$ AB] in F560W$-$F2550W. This
is an improvement of $\sim 2\times$ over the predicted ETC values111111ETC
v3.0 predictions were measured in the same apertures, assuming a background at
the approximate position (HUDF) and time (Dec 15, 2022) of SMILES data
acquisition. at F560W-F1800W (Table 1), indicating the power of robust
background subtraction. The uniformity of the background level and background
rms are discussed in Appendix A.

Filter | N | Compl. | Compl. | Phot. Accuracy
---|---|---|---|---
| $\geq 4\sigma$ |  $50\%$ [$\mu$Jy] |  $80\%$ [$\mu$Jy] | Median [16,$84\%$]
(1) | (2) | (3) | (4) | (5)
F560W | 2,959 | 0.14 | 0.20 | 1.0 [0.7, 1.4]
F770W | 2,407 | 0.13 | 0.18 | 0.99 [0.6, 1.4]
F1000W | 1,254 | 0.26 | 0.36 | 1.0 [0.7, 1.4]
F1280W | 1,050 | 0.38 | 0.54 | 1.0 [0.6, 1.4]
F1500W | 1,020 | 0.45 | 0.63 | 1.0 [0.6, 1.4]
F1800W | 776 | 1.1 | 1.6 | 1.0 [0.7, 1.3]
F2100W | 756 | 1.5 | 2.2 | 0.99 [0.7, 1.2]
F2550W | 337 | 11 | 15 | 1.0 [0.8, 1.3]
Table 2: Column 1: MIRI filter. Column 2: the number of $\geq 4\sigma$
detections in each filter (Section 7.1). Columns 3-4: the 50 and $80\%$
completeness limits. Column 5: the median and 16, $84\%$ percentiles of the
photometric accuracy simulation at the $5\sigma$ detection limit (Table 1).
See Section 5.2.2 more for details.

##  5 Photometric Catalog

###  5.1 Source Detection and Photometry

Source detection and photometry are performed using the JADES photometry
pipeline (Rieke & the JADES Collaboration, 2023; Eisenstein et al., 2023a,
Robertson, in prep), optimized for MIRI imaging.

####  5.1.1 Source Detection

For source detection, first a SNR “detection” image is constructed as the
ratio of the inverse-variance weighted stacks of the F560W and F770W science
and error mosaics. We stack only F560W and F770W to maintain high resolution
for source deblending; see Section 5.2 for a discussion of a full stack of all
bands. Our source detection method is built on Astropy (Astropy Collaboration
et al., 2022), Photutils (Bradley et al., 2023), and scikit-image (van der
Walt et al., 2014) routines and based on algorithms previously developed to
detect low-surface-brightness features in deep imaging (e.g. Borlaff et al.,
2019). Source detection is first performed on the detection image with a very
low threshold (SNR$\,\geq 1.5$). This initial segmentation map is then refined
through a series of custom algorithms that perform iterative filtering and
source deblending and reblending (Rieke & the JADES Collaboration, 2023,
Robertson, in prep) to first isolate satellites of bright sources and then
repair shredding of bright, resolved galaxies. Filtering and deblending
parameters are fine-tuned by hand for optimal results given the MIRI mosaic
properties. The segments of the resulting intermediate segmentation map are
then dilated and this is used as a mask for a final pass to identify isolated,
faint sources removed by previous filtering. We found that the high pass
filtering required to avoid shredding of bright sources needs to be balanced
against an aggressive detection threshold in the final step to recover faint,
but visually confirmed sources. We choose an aggressive source threshold of
SNR$\geq 2.5$ over 4 pixels, at the cost of a significant number of noise
peaks in the final segmentation map. This is discussed further in Section 5.2.

####  5.1.2 Photometry

The segmentation map generated in the last section has its edges masked and
then photometry is performed based on photutils (Bradley et al., 2023) in all
bands. Source centroids are determined using the “windowed” algorithm from
Source Extractor (Bertin & Arnouts, 1996). Circular aperture photometry is
performed in $r=0.25,0.3,0.35,0.5,0.6\arcsec$ apertures, ranging from
approximately the FWHM at F560W and F770W ($0.21\arcsec$ and $0.27\arcsec$,
respectively Dicken et al., 2024) to approximately the $65\%$ encircled energy
radii in the longest bands (Table 1). We additionally measure $2.5\times$
scaled Kron photometry (Kron, 1980) based on windowed centroiding and using
the stacked science F560W+F770W images to define the aperture size, which
captures extended low SNR emission better than the original stacked SNR
detection image. Kron apertures with a long axis less than $r=0.25\arcsec$ are
replaced with circular apertures with that radius, due to Kron photometry
becoming increasingly inaccurate for compact and/or faint sources (Graham &
Driver, 2005). We note that the Kron apertures are based on the surface
brightness profiles of sources at $\sim 5-7\mu$m and do not account for the
larger PSF or differences in morphology at longer wavelengths. We explore this
further in Section 5.2.

The photometric uncertainties are measured by placing apertures (equal to the
circular or Kron aperture in question) randomly across the source-masked
mosaics to capture all sources of noise including correlated pixel noise
(Labbé et al., 2005; Quadri et al., 2007; Whitaker et al., 2011, 2019). Though
our mosaics are fairly uniform (Appendix A), the variation in exposure time is
accounted for by binning the random apertures in exposure time percentiles and
using a power-law scaling between the rms of the counts and aperture size to
determine the sky noise contribution for a given source. We find that this
method results in uncertainties $2-3\times$ larger than uncertainties derived
from the ERR extension provided by the pipeline. Finally, aperture corrections
are applied to the fluxes and uncertainties based on Point Spread Functions
(PSFs) for each band. For F560W and F770W, we adopt high dynamic range
empirical PSFs built from high SNR commissioning data (A. Gaspar, private
communication) that capture the “cruciform”, an extended artifact caused by
internal reflection in the MIRI detectors (Gáspár et al., 2021; Libralato et
al., 2023; Dicken et al., 2024). This artifact is not well modeled in WebbPSF
as of version v1.2.1. Extended emission of the calibration source is detected
out to the edge of the MIRI imager FOV and so our empirical PSFs can be
normalized to one at large radii to produce robust aperture corrections. As
the cruciform becomes weak-to-negligible beyond $\sim 10\,\mu$m, we generate
PSFs with WebbPSF for the remaining bands. We then account for the effects of
mosaicking by injecting the WebbPSFs into blank tiles and running them through
the resample step, which slightly broadens the PSF core (Ji et al., 2023). The
$65\%$ EE radius of our PSFs are listed in Table 1.

###  5.2 Quality Checks

####  5.2.1 False Positive Detections

Visual inspection of the catalog is done to verify the robustness of the
source deblending, centroiding, and Kron apertures. The false positive rate of
the catalog sources is evaluated initially by matching to the JADES
catalog121212https://archive.stsci.edu/hlsps/jades/catalogs/hlsp_jades_jwst_nircam_goods-
s-deep_photometry_v2.0_catalog.fits within $0.3\arcsec$ to identify candidate
spurious detections. The JADES NIRCam imaging at F444W reaches $10\sigma$
depths of 29-30 mag (Eisenstein et al., 2023a), ensuring that all real sources
should be detected, save for rare extreme examples of MIRI-only sources
(Pérez-González et al., 2024a). From the total catalog of 5,712 sources, we
find 2,101 without JADES counterparts, as anticipated by our aggressive source
extraction discussed in the previous section. We discard the 1,809 with
SNR${}_{\rm F560W}<4$ in a $r=0.5\arcsec$ aperture. We visually inspect the
remaining 292 SNR${}_{\rm F560W}\geq 4$ detections, finding that 82 are real
and missing counterparts due to small gaps in the NIRCam coverage or
misidentified JADES counterparts, almost all caused by deblending issues from
nearby bright sources. These are added back into our catalog. Of the remainder
of the SNR $\geq 4$ false positives, $\sim 75\%$ are mischaracterized extended
features of brighter galaxies or noise features at the map edges or on the
Lyot coronagraph. This implies a true false positive rate of $\sim 1-2\%$ at
$>4\sigma$ due to remaining noise artifacts.

As hinted at in Section 4.1, spurious noise peaks (true false positives) are
visually identified as associated with unidentified warm/hot pixels (Section
4.2.1) or uncorrected cosmic ray showers. This is confirmed by a test
estimating the false positive rate by performing source extraction on inverted
mosaics (M. Stone et al. 2024, in prep), which finds $\lesssim 10$ noise peaks
detected with “fluxes” above the $80\%$ completeness limit (roughly the
$5\sigma$ detection limit, see next section) across the mosaics. False
positives caused by cosmic ray showers would not show up in this test. On the
other hand, the large number of “detections” at $<4\sigma$ that lack NIRCam
counterparts suggests that the false positive rate rises rapidly with
decreasing SNR given our aggressive source extraction. This is likely a
combination of cosmic ray showers and true background noise fluctuations. From
here on, we only consider a catalog cut to $\geq 4\sigma$ detections in an
aperture with $r=0.5\arcsec$ in either F560W or F770W (a total of 3,096
sources). No convincing $\geq 4\sigma$ MIRI-only detections at F560W or F770W
were found, likely due to the depth of SMILES relative to the much deeper MIRI
surveys presented in Pérez-González et al. (2024a).

####  5.2.2 Completeness and Photometric Accuracy

The completeness and photometric accuracy of point sources are assessed as a
function of flux density by injecting our MIRI PSFs (Section 5.1.2) into the
real mosaics and performing source detection and photometry as described in
Section 5.1. The source injection is described in more detail in Apprendix B.
In Table 2, we report the resulting 50 and $80\%$ completeness levels for each
filter. Our $80\%$ completeness limits are comparable to our $5\sigma$
detection limits (Table 1), likely due to our aggressive source extraction as
discussed in Section 5.1. The trade-off is the large false positive rate at
low SNR quantified in Section 5.2.1. We mitigate this drawback by matching to
the much deeper JADES NIRCam data to remove spurious sources and by imposing a
SNR cut on the release catalog at $\geq 4\sigma$ in F560W or F770W.

The photometric accuracy (ratio of extracted to input flux density) of
artificial sources is shown for F560W and F1500W in Figure 13; all filters are
shown in Figure B.1 in Appendix B. The median and 16, 84$\%$ confidence
intervals at the $5\sigma$ detection limit are shown in the figures and Table
2. In this case, we impose no criteria for detection, only that there is a
counterpart in the extracted catalog to each fake source within $0.2\arcsec$
of its input position that is not blended with a neighbor of equal or brighter
flux; this is relevant to our release catalog as we impose no SNR cut on bands
longer than F770W. We find no systematic offsets in the recovered flux
densities of fake sources. The $1\sigma$ scatter in the measured-to-input
ratios is $1.5-2\times$ higher than what is implied by the $5\sigma$ detection
limits measured in similar apertures ($r\sim 0.5\arcsec$; Table 1); this
reflects factors like variations in the noise and background subtraction
(Appendix A) and/or variations in the PSFs across the mosaics (Libralato et
al., 2023), minor flux boosting from neighbors, and stochastic effects.

Figure 13: The measured to input flux ratio for fake point sources inserted
into the SMILES F560W (left) and F1500W (right) mosaics using a circular
aperture with $r=0.5\arcsec$. The solid (dotted) black lines show the running
median ($16,84\%$ confidence intervals) as a function of input flux. The
vertical gray dashed line shows the $5\sigma$ detection limit (Table 1) and
the median and confidence intervals at that flux density are listed.

###  5.3 The Effect of using a F560W+F770W Detection Image on Long Wavelength
Photometry

In Section 5.1, we used a stacked F560W+F770W detection image and perform
forced photometry in all bands using the resulting positions and apertures. To
check if this misses sources that are only significantly detected at longer
wavelengths, we make a detection image from a stack of all the MIRI mosaics
and look for significant ($>4\sigma$) detections in at least 2 bands longer
than F770W. We find 9/35 with NIRCam counterparts; these will be investigated
in future analysis. We visually inspect the detections without NIRCam
counterparts and conclude they are random noise spikes coincidentally co-
spatial in two bands. Looking for single band $4\sigma$ detections in
F1000W-F2550W, we find 26 with a NIRCam counterpart, likely strong emission
features in faint galaxies. In general, however, we conclude that we are not
missing a significant number of sources by using a F560W+F770W detection
image.

We also examine whether defining the Kron apertures from the F560W+F770W
detection image significantly alters the photometry at the longer wavelengths
where the PSF is larger. Morphological differences are expected when comparing
bands that cover stellar features to those covering dust features. We compare
the photometry based on the F560W+F770W detection image to that derived from a
stack of all the MIRI filters, finding offsets of $\sim 2\%$ ($8\%$) higher
fluxes in F1800W-F2100W (F2550W) with $1\sigma$ scatter of $<6\%$ ($<20\%$) at
F1800W (F2100W-F2550W) above the $5\sigma$ detection limit. This indicates
that an aperture correction based on individual Kron apertures largely
compensates for the larger PSF.

##  6 SMILES: NIRSpec Follow-up

Figure 14: (left) Three example $R\sim 1000$ spectra from the SMILES NIRSpec
follow-up: a SFG at $z=1.57$ (top), AGN at $z=2.87$ (middle), and a $z=5.39$
[O iii] emitter selected from FRESCO (Oesch et al., 2023) that is part of a
galaxy overdensity (Helton et al., 2024). Major emission lines and the
boundary between G140M and G235M are labeled. (right) Stack of 2D spectra for
our NIRSpec sample in order of increasing redshift from bottom to top. Areas
without data are due to the gap between the NIRSpec detectors.

To maximize the science potential of the SMILES MIRI imaging, we included in
the program 17 hours of follow-up NIRSpec MOS at $\sim 1-3\,\mu$m, which was
observed in August 2023. The main objective of this follow-up is to provide
key information about the ionized gas content of cosmic noon galaxies and AGN.
Therefore, the integration times, filter selection, and grating selection were
motivated by detecting bright rest-frame optical emission lines at $z\sim
1-3$, where MIRI detects warm dust emission. In this section, we give an
overview of the science goals, target selection, and observational setup of
this follow-up. The SMILES NIRSpec spectra will be made publically available
in a future data release.

###  6.1 Science Priorities and Sample

Optical spectroscopy of SMILES MIRI targets is aimed at measuring properties
such as metallicity, excitation properties of ionized gas, and dust extinction
via bright emission lines $-$ [Oii]$\lambda 3726,3729$ ($1.6<z<7.5$);
H$\beta$, [Oiii]$\lambda 4959,5007$ ($1.1\lesssim z\lesssim 5.3$); and
H$\alpha$, [Nii]$\lambda 6585$ ($0.5\lesssim z\lesssim 3.7$) $-$ primarily at
$z\sim 1-2$ where the MIRI bands cover hot dust and multiple PAH features
(Figure 1). We additionally cover the extinction-robust strong Paschen lines
Pa$\alpha$ at $z\lesssim 0.7$ and Pa$\beta$ at $z\lesssim 1.45$. For
relatively low mass/low SFR objects and highly dust-obscured systems at $z\sim
1-2$, NIRSpec MOS is uniquely positioned to observe these lines owing to its
high sensitivity. The main science priorities of these observations are:

####  6.1.1 Star-forming galaxies at cosmic noon

PAH-metallicity relation: MIRI provides a unique opportunity to robustly
characterize the mass fraction of PAH dust grains relative to the total dust
mass budget of galaxies (Shivaei et al., 2024). Local studies have shown that
the PAH fraction is highly dependent on the gas-phase metallicity of galaxies
(see Li, 2020, for a review). On average, this relation has recently been
shown through SMILES to hold up to $z\sim 2$ (Shivaei et al., 2024), albeit
using stellar mass as a proxy for metallicity. To expand these results, We
will directly measure metallicity from strong optical lines in a sample of
MIRI-detected star-forming galaxies that span a range of mass (and hence,
metallicity; e.g. Sanders et al., 2021).

Paschen sources: The footprint of SMILES overlaps significantly with the
FRESCO/GOODS-S NIRCam F444W grism observations (Figure 4 Oesch et al., 2023),
which covers the Pa$\alpha$ (Pa$\beta$) emission line at $z\sim 1-1.7$ ($z\sim
2-3$). Together with our NIRSpec spectroscopy, we can simultaneously observe
multiple hydrogen recombination lines (e.g. Pa$\alpha$ and Pa$\beta$ at $z\sim
1-1.45$; Pa$\beta$ and H$\alpha$ at $z\sim 2-3$) to study the nebular
attenuation curve relative to the warm dust emission traced by the MIRI bands.

MIRI-ALMA sources: Also overlapping SMILES is some of the deepest ALMA data to
date both in continuum and spectroscopy (e.g., Walter et al., 2016; Hatsukade
et al., 2018; Boogaard et al., 2023; Dunlop et al., 2017; Adscheid et al.,
2024), probing molecular gas, cold dust, and obscured SFR down to the main
sequence (Popesso et al., 2023). All of the ALMA sources in the primary ASPECS
(Walter et al., 2016) and ASAGAO (Hatsukade et al., 2018) catalogs have strong
detections in multiple bands of MIRI (Alberts et al., 2024c, in prep) and some
have coverage of the Paschen lines from FRESCO. We prioritized targeting ALMA
sources with both MIRI and FRESCO coverage to obtain Balmer lines; as above,
the combination of Paschen and Balmer lines constrains dust attenuation
relative to the warm and cold dust components from MIRI and ALMA. As a second
priority, we targeted known compact ALMA sources using the $\sim 0.2\arcsec$
native resolution imaging from ASAGAO. For fillers, we also included ALMA
sources with unknown morphologies and/or unconfirmed photometric redshifts.
The addition of attenuation, metallicity, and ionized gas properties to the
existing CO and cold dust continuum emission of “normal” galaxies will shed
light on the interplay of gas, dust, and metals at cosmic noon.

####  6.1.2 AGN across cosmic time

As discussed in Section 2.2.1, Lyu et al. (2024) recently identified a large
number of obscured AGN in SMILES, including many missed in previous surveys.
Spectroscopic follow-up of these AGN candidates can confirm their nature and
characterize key AGN properties that are not accessible through the broad-band
SED analysis. From the full AGN catalog presented in Lyu et al. (2024), we
prioritized obtaining spectra of robust dwarf and high-$z$ AGN candidates as
well as “normal” ($M_{*}>10^{9.5}~{}M_{\odot}$ and $z=0-4$) AGN at cosmic
noon.

Depending on the redshift, the SMILES NIRSpec follow-up covers various
emission line features relevant to AGN. For example, for objects at $z\sim
1-3.7$, we cover H$\alpha$, H$\beta$, [Nii], and [Oiii] simultaneously,
enabling us to identify the source of ionizing radiation in galaxies and AGN
via rest-frame optical emission line diagnostics like the BPT diagram (e.g.
Baldwin et al., 1981). For broad-line AGN, we can constrain black hole masses
through the line widths of Pa$\alpha$,$\beta$ or He i ($z<1.0$), H$\alpha$,
and H$\beta$. All these lines can be also used to search for outflow/inflow
signatures that are typically associated with AGN feedback.

####  6.1.3 Other Targets

In addition to prioritized targets discussed above, we added other
scientifically interesting targets in the SMILES footprint such as massive
(log ${M_{\star}/M_{\odot}}\geq 10$) quiescent galaxies at $z>1$ (Ji et al.,
2024), photometric members of high-redshift ($z\sim 5$) overdensities (Helton
et al., 2024), line emitters at $z>3$ from the FRESCO grism data (Hainline et
al., 2024), and radio sources from ultra-deep, high resolution ($\sim
0.45\arcsec$) 6 GHz imaging (Alberts et al., 2020).

###  6.2 NIRSpec Observational Setup

For the science goals outlined in the previous section, we dedicated three MSA
masks with the same settings: we observed in two filter/grating settings
G140M/F100LP and G235M/F170LP, providing continuous spectra from
$0.97-3.17\,\mu$m with $R\sim 1000$. The medium resolution gratings are
necessary to deblend closely spaced line pairs like H$\alpha$ and [Nii]. For
the exposure setup, we observed 16 groups per integration over 2 integrations
for each filter, resulting in a total science time of 3.89 hours per mask. We
opened 3-shutter slitlets for all the objects and adopted 3-shutter-slitlet
nod pattern.

For the mask design, we adopted the eMPT software (Bonaventura et al., 2023).
The eMPT is a supplement to the JWST Astronomer’s Proposal Tool (APT) that can
serve as a replacement for its MSA Planning Tool (MPT) for designing MSA
observations. Its advantage is enabling advanced functionality for ambitious
science goals, including assigning multiple priorities to different categories
of sources such that the maximum number of highest priority targets is
observed simultaneously. We gave our most important and/or rare sources the
highest priority (e.g., certain AGN and star-forming galaxies with rich
ancillary data such as ALMA or FRESCO Paschen lines, to ensure a high fraction
of slit allocations. The rest of the sample were assigned priorities from 2 to
6, depending on the number count, source concentration, and science driver. We
employed various tests with eMPT and different priority allocations to ensure
the most efficient slit allocation strategy among our three masks.

In the final design, 168 unique sources were successfully allocated slits,
with roughly $40\%$ and $20\%$ in the SFG (Section 6.1.1) and AGN (Section
6.1.2) categories, respectively. Figure 14 shows an example spectra for each,
as well as a $z>5$ source selected via FRESCO as an [O iii] emitter and the
stacked 2D spectra of the full sample.

Figure 15: The distribution of the source fluxes as a function of redshift for
the SMILES release catalog. Detections at $\geq 4\sigma$ are shown in the
colored points and contours and lower SNR fluxes are shown in gray. The number
of $\geq 4\sigma$ detections is listed in each panel. The dashed line shows
the $1\sigma$ limit.

##  7 SMILES Catalog and Data Release

In this section, we present a brief overview of the demographics of the SMILES
photometric catalog and describe the first SMILES data release.

###  7.1 Catalog Properties

In order to quantify the basic demographics of the SMILES photometric release
catalog (Section 7.2), the catalog is first matched to a compilation of
available spectroscopic redshifts in the literature131313The spectroscopic
redshift campaigns include (in order of priority for matching), JADES NIRSpec
(Bunker et al., 2023; D’Eugenio et al., 2024), MUSE-Wide (Urrutia et al.,
2019), MUSE-HUDF (Bacon et al., 2023), other surveys based on the CDFS
spectroscopic redshift compilation (N. Hathi, private communication), FRESCO
(Oesch et al., 2023), and ASPECS (Walter et al., 2016). (Shivaei et al., 2024)
and photometric redshifts from JADES (Hainline et al., 2024). 1,400 ($45\%$)
galaxies in the SMILES photometric catalog have a spectroscopic redshift.
Sources without a spectroscopic redshift match are assigned a photometric
redshift based on fitting the HST/ACS and NIRCam photometry with the EAZY
photo-$z$ fitting code (Brammer et al., 2008; Hainline et al., 2024). Stellar
mass estimates are obtained for the MIRI sources from SED modeling (J. Helton,
private communication) done using the JADES HST/ACS + NIRCam catalog via the
Bayesian fitting code Prospector141414Prospector was run with nest sampler
dynesty (Speagle, 2020) and neural net emulator parrot (Alsing et al., 2020;
Mathews et al., 2023). (Johnson et al., 2021), following the methodology
outlined in Wang et al. (2023, 2024b) for the Prospector-$\beta$ physical
model. Informed observational priors were adopted based on e.g. empirical
stellar mass functions, the evolution of the cosmic star-formation rate
density, and the stellar mass-stellar metallicity relation.

The flux distribution and stellar masses of the SMILES catalog sources as a
function of redshift are shown in Figures 15 and 16, respectively. As
expected, the distribution of detections is weighted toward low redshift due
to the prominent dust features in the MIRI bands up to cosmic noon and the
moderate exposure times. Nevertheless, SMILES detects $\sim 90$ galaxies at
$z>5$, 21 with confirmed spectroscopic redshifts. The SMILES catalog
additionally includes $\sim 550$ dwarf galaxies with log
${M_{\star}/M_{\odot}}<9$. MIRI number counts in all SMILES bands plus the
ultra-deep JADES parallel at 7.7$\,\mu$m (Alberts et al., 2024, in prep) $-$
spanning 4-5 orders of magnitude in flux at $\sim 8$ and $\sim 24\mu$m when
joined with Spitzer surveys $-$ will be presented in Stone et al., 2024, ApJ
(submitted).

Figure 16: The redshift and stellar mass distribution of the SMILES release
catalog. Spectroscopic (photometric) redshifts are shown in red open circles
and solid contours (dark blue points and dot-dashed contours). The top
histogram shows the distribution of spectroscopic redshifts (hatched) and
photometric redshifts (solid).

###  7.2 SMILES First Data Release

The first SMILES data release includes eight MIRI mosaics and a photometric
catalog with 3,096 sources detected at $\geq 4\sigma$ in either F560W or F770W
and with forced photometry in all bands, providing limits in the case of non-
detections. The data can be accessed at https://archive.stsci.edu/hlsp/smiles.
Data products from the NIRSpec spectroscopic follow-up of the SMILES MIRI
imaging (Section 6) will be made public in a future release.

Mosaics: The MIRI mosaics in F560W, F770W, F1000W, F1280W, F1500W, F1800W,
F2100W, and F2550W cover $\sim 34.5$ arcmin2 in the GOODS-S/HUDF field and
reach $5\sigma$ point source sensitivities of $\sim 0.2-18$ $\mu$Jy (25.6-20.9
AB; Table 1). The design and reduction of the MIRI mosaics are presented in
Sections 3-4. The data release mosaics each include the following extensions:
science image, error image, exposure time map, and weight map.

Catalog: The creation and verification of the photometric catalog is described
in Section 5. The release catalog consists of sources detected at $\geq
4\sigma$ in either F560W or F770W. As described in Section 5.2.1, the catalog
sources are vetted by matching to the deeper NIRCam catalog and then by visual
inspection to recover real sources that are missing NIRCam coverage or where
the NIRCam counterpart is mistakenly blended in the JADES catalog. The flux
distribution and number of detections in each band is shown in Figure 15 and
listed in Table 2; the number of $\geq 4\sigma$ detections ranges from 2,959
at F560W to 1,254 at F1000W to 756 at F2100W, out of the total of 3,096.
Photometry is carried out in apertures defined by a F560W+F770W detection
image and a measurement is provided for each source in every band, regardless
of significance (with at least F560W or F770W detected at $\geq 4\sigma$).
Aperture-corrected photometry is provided in 5 circular apertures
$r=0.25,0.3,0.35,0.5,0.6\arcsec$ and in a $2.5\times$-scaled Kron aperture.
Photometric uncertainties provided are derived from placing random apertures,
as described in Section 5.1.

Ancillary JWST Data: As seen in Figure 4, SMILES has near-complete NIRCam
imaging coverage from JADES (Rieke & the JADES Collaboration, 2023; Eisenstein
et al., 2023a) in 9 photometric bands as well as partial coverage from JEMS
(Williams et al., 2023b) in four NIRCam medium-bands. JADES NIRCam mosaics and
catalogs and JADES NIRSpec spectroscopy (Bunker et al., 2023; D’Eugenio et
al., 2024) are available at https://archive.stsci.edu/hlsp/jades and include
spectroscopic and photometric redshifts. JEMS is available at
https://archive.stsci.edu/hlsp/jems. SMILES additionally has significant
overlap with NIRCam grism spectroscopy and imaging from the FRESCO survey
(Oesch et al., 2023), available at https://archive.stsci.edu/hlsp/fresco.

In conclusion, MIRI is emerging as an even more powerful tool in the JWST
arsenal than dreamed by many. We hope that this data release will allow the
broader community to explore the full potential of multi-band MIRI surveys at
cosmic noon and beyond.

The SMILES team extends their sincerest gratitude to the MIRI instrument team
for their tireless work in designing, building, testing, and commissioning
MIRI. The authors thank Andras Gaspar for his work constructing empirical MIRI
PSFs, Mihai Cara for assistance in building astrometry correction software,
and Nimisha Kumari for help in designing and implementing the SMILES NIRSpec
observations. The authors further thank the JADES team for their hard work in
creating and publishing the JADES mosaics, spectra, and catalogs. SA, JL, GHR,
JM, and MS acknowledge support from the JWST Mid- Infrared Instrument (MIRI)
Science Team Lead, grant 80NSSC18K0555, from NASA Goddard Space Flight Center
to the University of Arizona. IS acknowledges funding support from the
Atraccíon de Talento program, Grant No. 2022-T1/TIC-20472, of the Comunidad de
Madrid, Spain. BER and CNAW acknowledges support from the NIRCam Science Team
contract to the University of Arizona, NAS5-02015, and BER additional
acknowledges JWST Program 3215.

##  Appendix A Expanded Mosaic Characteristics

In this appendix, we give further details about the SMILES MIRI mosaics in
filters F560W, F770W, F1000W, F1280W, F1500W, F1800W, F2100W, and F2550W. The
exposure time map for F1280W and pointing order for SMILES can be seen in
Figure A.1 (left); the relative exposure time per pixel for F1280W is
representative of all of the longer wavelength filters. The majority of the
areas in these mosaics has 4 dithers and a total exposure time as listed in
Table 1. Areas of overlap, such as where the Lyot coronagraph falls interior
to the mosaic have double the exposure time. Tiles 2 and 6 suffered from guide
star failures (Section 3); prior to failure observations in F560W, F770W, and
F1000W (4, 4, and 2 dithers, respectively) were obtained for tile 2. Only
F560W (2 dithers) was obtained for tile 6. Tiles 2 and 6 were re-observed on
Jan 1 and Jan 28, 2023 at aperture $\mathrm{PA}=52$ and $76$ degrees,
respectively. Zoom-ins of the affected area are show for F560W, F770W, and
F1000W are shown in the right panels of Figure A.1. The relative exposure time
per pixel for the rest of the mosaic in these filters is similar to that shown
in the left panel, with very minor differences due to the increasing dither
pattern size with increasing wavelength.

Figure A.1: (left) The exposure time map for F1280W, representing all longer
bands in terms of spatial distribution of the relative exposure time per
pixel. Total exposure times over 4 dithers can be seen in Table 1. The
exposure time maps are largely uniform, save for where the Lyot chronograph
overlaps an adjacent pointing. The tiles are labeled in order of acquisition.
Due to guide star failures, Tiles 2 and 6 were re-observed at a later date and
different PA, see text for details. (right) Zoom-ins of the exposure time maps
for F560W, F770W, and F1000W, which obtained 0-4 exposures before guide star
failures in Tiles 2 and 6.  Figure A.2: The variation in the sky background of
the super-background-subtracted mosaics (Section 4.2.2) averaged over $65\%$
EE apertures for each filter (see Table 1). Sources have been masked. These
background images are $5\sigma$-clipped to make the variation more visible;
the 16 and 84$\%$ confidence intervals (prior to $\sigma$-clipping) are
listed. Higher residuals after background subtraction can be seen mainly in
Tile 2 and Tile 6 $-$ which were re-observed in Jan 2024 due to guide-star
failures (Section 3) $-$ at a level of a few to tens of nano-Janskys, well
below the typical rms (see Figure A.3).

In Sections 4.2.2 and 4.3, we describe our custom background subtraction via
the construction of super-backgrounds and the resulting final mosaics. In
Figure A.2, we show the variation in the average background after super-
background subtraction, with sources and mosaic edges masked and averaged over
apertures matched to the $65\%$ EE (Table 1) for each MIRI filter. The
distribution of sky pixel values after background subtraction is centered on
zero (see also Figure 9) and largely uniform to first order, demonstrating
proper subtraction. Deviations outside the $1\sigma$ confidence intervals can
be seen to peak in Tile 2 and 6 (Figure A.1), where exposures were obtained
spread over a 1-1.5 month period after guide star failures. These deviations
are minimal, however, on order of a few to a few tens of nJy. In Figure A.3,
the rms is shown for comparison to be on order a few tens to hundreds of nJy,
measured in the same size apertures. Tiles 2 and 6 have higher rms for
F770W-F2550W where no exposures were obtained prior to guide star failure.
According to ETC, the background rms from Dec 15, 2022 to Jan 15, 2023 in
GOODSS rises by $8-2\%$ from F560W to F1280W, likely due to rising zodi (Rigby
et al., 2023b). The thermal background levels are expected to be stable over
the timescale relevant to these observations (Rigby et al., 2023b, see also
Figure 10) . Here we are seeing increases of $\sim 10-60\%$ in typical rms in
tiles 2 and 6, with tile 6 (which was re-observed last, nearly a month after
original observations) seeing the largest increase. This suggests a time-
dependent factor that is not being wholly addressed by the super-background
subtraction.

Figure A.3: The rms after background subtraction (Section 4.2.2), averaged
over $65\%$ EE apertures (Table 1) for the SMILES mosaics. No aperture
correction has been applied and sources have been masked. As in the residual
sky background images (Figure A.2), the rms is highest in Tile 2 and 6, except
for Tile 2 F560W - F1000W and Tile 6 in F560W which have $>4$ dithers as some
data was obtained before the guide star failure.

##  Appendix B Photometric Accuracy in SMILES

Completeness and photometric accuracy testing were performed on all eight MIRI
mosaics by injecting fake point sources, represented by the PSFs described in
Section 5.1.2. The PSFs are scaled to a randomly chosen total flux in 43 flux
bins with widths of 0.1 dex ranging from $0.01-200$ $\mu$Jy. To avoid changing
the source density and noise properties of the mosaics, we limit the number of
injected PSFs to 150 ($\sim 5\%$ of the total source catalog) placed in random
positions. We repeat this for 10 simulations, totalling 1,500 fake sources per
filter, per bin. Source detection is run as described in Section 5.1.1. The
resulting measured-to-input flux ratios are presented in Figure B.1 for all
bands in a $r=0.5\arcsec$ aperture, on average representative of the $65\%$ EE
apertures used to measure the rms of the mosaics in Section 4.3. The median
and [$16,84]\%$ confidence intervals are shown in each figure for the
$5\sigma$ detection limit.

Figure B.1: The photometric accuracy represented as the measured flux over the
input flux of fake point sources inserted into the F560W-F1280W mosaics (left)
and F1500W-F2550W mosaics (right). Flux measurements are using a circular
aperture with $r=0.5\arcsec$.

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