# COSMOS-Web: MIRI Data Reduction and Number Counts at 7.7

$\mu$

m using JWST

Santosh Harish, Jeyhan S Kartaltepe, Daizhong Liu, Anton M Koekemoer, Caitlin M Casey, Maximilien Franco, Hollis B Akins, Olivier Ilbert, Marko Shuntov, Nicole E Drakos, et al.

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Santosh Harish, Jeyhan S Kartaltepe, Daizhong Liu, Anton M Koekemoer, Caitlin M Casey, et al. COSMOS-Web: MIRI Data Reduction and Number Counts at 7.7

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COSMOS-Web: MIRI Data Reduction and Number Counts at 7.7 µm Using JWST

Santosh Harish¹ ID, Jeyhan S. Kartaltepe¹ ID, Daizhong Liu² ID, Anton M. Koekemoer³ ID, Caitlin M. Casey⁴,⁵,⁶ ID,
Maximilien Franco⁵,⁷ ID, Hollis B. Akins⁵ ID, Olivier Ilbert⁸ ID, Marko Shuntov⁶,⁹ ID, Nicole E. Drakos¹⁰ ID, Mike Engesser³ ID,
Andreas L. Faisst¹¹ ID, Ghassem Gozalias¹²,¹³ ID, Crystal L. Martin⁴ ID, Michaela Hirschmann¹⁴,¹⁵ ID, Vasily Kokorev¹⁶ ID,
Erini Lambrides¹⁷,<fnref n="22" /> ID, Henry Joy McCracken¹⁸ ID, Jed McKinney¹⁹,<fnref n="23" /> ID, Louise Paquereau¹⁸ ID, Jason Rhodes²⁰ ID, and
Brant E. Robertson²¹ ID

¹ Laboratory for Multiwavelength Astrophysics, School of Physics and Astronomy, Rochester Institute of Technology, 84 Lomb Memorial Drive, Rochester, NY 14623, USA

² Purple Mountain Observatory, Chinese Academy of Sciences, 10 Yuanhua Road, Nanjing 210023, People's Republic of China

³ Space Telescope Science Institute, 3700 San Martin Dr., Baltimore, MD 21218, USA

⁴ Department of Physics, University of California, Santa Barbara, CA 93109, USA

⁵ The University of Texas at Austin, 2515 Speedway Blvd Stop C1400, Austin, TX 78712, USA

⁶ Cosmic Dawn Center (DAWN), Denmark

⁷ Université Paris-Saclay, Université Paris Cité, CEA, CNRS, AIM, 91191 Gif-sur-Yvette, France

⁸ Aix Marseille Univ., CNRS, CNES, LAM, Marseille, France

⁹ Niels Bohr Institute, University of Copenhagen, Jagtvej 128, DK-2200, Copenhagen, Denmark

¹⁰ Department of Physics and Astronomy, University of Hawaii, 200 W Kawili St, Hilo, HI 96720, USA

¹¹ Caltech/IPAC, MS 314-6, 1200 E. California Blvd., Pasadena, CA 91125, USA

¹² Department of Computer Science, Aalto University, P.O. Box 15400, FI-00076 Espoo, Finland

¹³ Department of Physics, University of Helsinki, P.O. Box 64, FI-00014 Helsinki, Finland

¹⁴ Institute of Physics, GalSpec, École Polytechnique Federale de Lausanne, Observatoire de Sauverny, Chemin Pegasi 51, 1290 Versoix, Switzerland

¹⁵ INAF, Astronomical Observatory of Trieste, Via Tiepolo 11, 34131 Trieste, Italy

¹⁶ Kapteyn Astronomical Institute, University of Groningen, PO Box 800, 9700 AV Groningen, The Netherlands

¹⁷ NASA Goddard Space Flight Center, Code 662, Greenbelt, MD 20771, USA

¹⁸ Institut d'Astrophysique de Paris, UMR 7095, CNRS, and Sorbonne Université, 98 bis boulevard Arago, F-75014 Paris, France

¹⁹ Department of Astronomy, The University of Texas at Austin, 2515 Speedway Blvd Stop C1400, Austin, TX 78712, USA

²⁰ Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 91001, USA

²¹ Department of Astronomy and Astrophysics, University of California, Santa Cruz, 1156 High Street, Santa Cruz, CA 95064, USA

Received 2025 April 4; revised 2025 July 30; accepted 2025 August 8; published 2025 October 3

Abstract

The COSMOS-Web survey is the largest JWST Cycle 1 General Observer program, covering a contiguous ~0.54 deg² area with NIRCam imaging in four broadband filters and a noncontiguous ~0.2 deg² with parallel MIRI imaging in a single broadband filter, F770W. Here we present a comprehensive overview of the MIRI imaging observations, the data reduction procedure, the COSMOS-Web MIRI photometric catalog, and the first data release including the entire COSMOS-Web MIRI coverage. Data reduction primarily relies on the JWST Science Calibration Pipeline, supplemented by custom background subtraction to mitigate prominent instrumental artifacts and sky background within the MIRI images. We reach 5σ (point source) limiting depths ($m_{F770W}$ ~ 25.51 based on $r \sim 0.′.3$ circular apertures) that are significantly better than initial expectations. Using the images provided in this release, we construct a photometric catalog and compare the F770W flux densities to the Spitzer/IRAC CH4 measurements from the COSMOS2020 catalog (for CH4 detections with signal-to-noise ratio S/N > 5). We find that these are in reasonable agreement with a small median offset of <0.05 mag. We also derive robust 7.7 µm number counts spanning five orders of magnitude in flux (~0.2–2300 µJy)—making COSMOS-Web the only JWST survey to date to sample such a large flux range— which are in good agreement with estimates from other JWST and IRAC surveys.

*Unified Astronomy Thesaurus concepts: James Webb Space Telescope (2291); Galaxy evolution (594); Astronomy data reduction (1861); Infrared photometry (792); Infrared sources (793); Galaxy counts (588)*

1. Introduction

Mid-infrared (MIR) imaging plays a critical role in extra-
galactic astronomy by providing a unique perspective on distant
 galaxies and their intricate structures. In the context of star
formation, much of the UV/optical emission is absorbed by

dust in the interstellar medium and reradiated at longer
wavelengths, particularly in the IR regime (8–1000 µm).
Additionally, even for galaxies with modest dust extinction,
the strength of polycyclic aromatic hydrocarbon (PAH;
A. Leger & J. L. Puget 1984; R. Genzel et al. 1998) emission
(3–12 µm) is considered a good indicator of obscured star
formation (e.g., H. Wu et al. 2005) for galaxies up to z ~ 2
(J. R. Houck et al. 2005; C. Papovich et al. 2006).

IR measurements are essential for deriving accurate stellar
masses at higher redshifts, where they probe the rest-frame
optical to near-infrared (NIR) emission of galaxies, tracing the
older stellar population. The MIR wavelength regime is also
crucial for identifying obscured active galactic nuclei (AGN)

<figure><img src="image_3.png" /></figure>

Original content from this work may be used under the terms
of the [Creative Commons Attribution 4.0 licence](https://creativecommons.org/licenses/by/4.0/). Any further
distribution of this work must maintain attribution to the author(s) and the title
of the work, journal citation and DOI.

<footnote n="22">²² NPP Fellow.</footnote>

<footnote n="23">²³ NASA Hubble Fellow.</footnote>
because the emission from the hot dusty torus of an AGN peaks in this spectral regime, and is distinct from the host galaxy emission at these wavelengths (see review by R. C. Hickox & D. M. Alexander 2018). For galaxies with high dust and gas content, MIR-based selection techniques (e.g., A. Kirkpatrick et al. 2012) are efficient at identifying hidden AGN populations that are missed at other wavelengths, especially those blueward of the UV/optical wavelengths.

The lack of sensitivity of ground-based telescopes to MIR emission coupled with strong absorption in the terrestrial atmosphere means that space-based telescopes are the most efficient means of mapping the Universe in the MIR. Several IR missions including the Infrared Astronomical Satellite (G. Neugebauer et al. 1984), Infrared Space Observatory (M. F. Kessler et al. 1996), Spitzer Space Telescope (Spitzer; M. W. Werner et al. 2004), AKARI (H. Murakami et al. 2007), and Wide-field Infrared Survey Explorer (E. L. Wright et al. 2010) have successfully explored a variety of dusty star-forming galaxy (SFG) populations including luminous infrared galaxies ($L_{IR} > 10^{11} L_{\odot}$) and ultraluminous infrared galaxies ($L_{IR} > 10^{12} L_{\odot}$), which are rare in the local Universe but increasingly important at $z > 1$ (D. B. Sanders & I. F. Mirabel 1996; E. Le Floc'h et al. 2005). Notable among these is the Spitzer mission, which performed several deep-field surveys (e.g., D. B. Sanders et al. 2007; M. L. N. Ashby et al. 2009, 2013) to probe the faint IR populations, especially using the Infrared Array Camera (IRAC) instrument (G. G. Fazio et al. 2004). IRAC has played a crucial role in confirming some of the highest-redshift objects identified using the Hubble Space Telescope (HST), including the well-studied object GN-z11 (P. A. Oesch et al. 2016), and constraining their stellar masses, ages, and star formation histories (e.g., D. P. Stark et al. 2007; I. Labbé et al. 2010, 2013).

Building on the rich legacy of previous space-based IR missions, the successful launch of the James Webb Space Telescope (JWST; J. P. Gardner et al. 2006) has enabled unprecedented observations in the NIR and MIR, with sensitivity and resolution far superior to its predecessors. In particular, the Mid-Infrared Instrument (MIR; G. H. Rieke et al. 2015; G. S. Wright et al. 2023) represents a significant advancement in its ability to probe the 5–28 µm range through imaging, spectroscopy, and coronagraphy. Because longer wavelengths are less affected by dust extinction, MIRI offers several key advantages, including: (1) characterization of stellar populations and star formation histories for galaxies at $z > 5$ (e.g., C. Papovich et al. 2023), because the ability to detect evolved stellar populations improves at redder wavelengths, (2) detection of Hα emission-line galaxies at $z > 6.6$ (e.g., P. Rinaldi et al. 2023), which is a valuable probe of star formation and AGN in the epoch of reionization, (3) detection of massive starbursts at $z > 4$ (e.g., J. Álvarez-Márquez et al. 2023), because MIRI can probe the rest-NIR ($\lambda > 1$ µm), (4) detection of AGN in massive galaxies at $z > 1$ (e.g., J. Lyu et al. 2024), with MIRI uniquely able to observe emission from the hot dusty torus that dominates the spectral energy distribution (SED) in the rest-NIR ($\lambda > 1$ µm).

The MIRI 7.7 µm filter has been used by several surveys, including CEERS (S. L. Finkelstein et al. 2025), MEGA (B. E. Backhaus et al. 2025), PRIMER, and SMILES (G. H. Rieke et al. 2024). Many previous studies have made use of data from the sensitive F770W filter, including the detection of galaxies at very high redshift and more accurate

stellar mass measurements (e.g., C. Papovich et al. 2023; T. Wang et al. 2025a). It is particularly useful for identifying dusty starbursts at a range of redshifts (e.g., H. B. Akins et al. 2023) and can be used to identify sources with PAH emission (K. Ronayne et al. 2024). It has also been used to identify exotic objects, such as an NIR-dark, MIR-only detection (P. G. Pérez-González et al. 2024b), and provides critical information in the MIR for the so-called “little red dots” (H. B. Akins et al. 2023, 2025; P. G. Pérez-González et al. 2024a; B. Wang et al. 2025b), enabling accurate stellar mass estimates and characterization of their dust properties.

The COSMOS-Web survey (PID 1727; PIs: Kartaltepe & Casey; C. M. Casey et al. 2023) is the largest JWST Cycle 1 GO program, both in terms of area on the sky and total prime-time allocation, and maps the central region of the COSMOS field (P. Capak et al. 2007; A. M. Koekemoer et al. 2007; N. Scoville et al. 2007), covering a large contiguous area of $0.54 \text{ deg}^2$ using four Near-Infrared Camera (NIRCam; M. J. Rieke et al. 2005, 2023) filters (F115W, F150W, F277W, and F444W) and a noncontiguous area of $0.2 \text{ deg}^2$ using a single MIRI filter (F770W). An in-depth overview of the survey design, primary science goals, and comparison with other deep-field programs from JWST’s first year of observations is presented by C. M. Casey et al. (2023).

The COSMOS-Web MIRI F770W coverage represents the largest area observed with MIRI observations to date, enabling several key science goals. The F770W filter was primarily chosen in order to provide accurate stellar mass measurements for massive galaxies at $z > 4$ (see M. Shuntov et al. 2025). The large area allows for the detection of extremely rare massive galaxies into the epoch of reionization. It also enables the direct detection of dusty SFGs across a wide redshift range and an analysis of their star formation rates and star formation histories, in combination with their full multiwavelength SEDs. The F770W coverage is also crucial for distinguishing between dusty SFG and high-redshift quiescent galaxy candidates (e.g., S. Alberts et al. 2024a). Finally, the exquisite resolution achieved at this wavelength for the first time allows for detailed NIR and MIR morphological analyses in the nearby Universe.

This paper describes the MIRI imaging component of COSMOS-Web, including details of the completed observations in Section 2, an overview of the data reduction using the JWST science calibration pipeline and the survey depth in Section 3. The construction of a COSMOS-Web MIRI source catalog based on F770W detections is described in Section 4 together with estimates of source completeness. We present a comparison with the IRAC CH4 photometry, the photometric redshift distribution of the 7.7 µm sources, and the number counts at 7.7 µm, including comparisons with other estimates based on JWST and Spitzer observations as well as model predictions in Section 5, followed by a summary in Section 6.

## 2. Observations

The principal design of COSMOS-Web was driven by the NIRCam observations, which consist of a contiguous $41.5 \times 46.6$ rectangular mosaic (oriented at an angle $20^\circ$ relative to north) centered at R.A. = 10:00:27.92, decl. = +02:12:03.5, using the aforementioned NIRCam filters (discussed in a separate paper: M. Franco et al. 2025), and complemented by parallel MIRI imaging using the F770W filter. The F770W filter was chosen to accurately constrain stellar masses at $z > 4$
due to its sensitivity and the greater wavelength separation it
provides over F444W, offering better differentiation than
filters like F560W. A typical MIRI visit consisting of eight
exposures was obtained through two separate executions of the
4TIGHT dither pattern at the same mosaic position (for more
details, see C. M. Casey et al. 2023). The MIRI observations
consist of 160 distinct visits with each visit containing
anywhere from two to eight exposures (263 s per exposure),
depending on whether the visit was fully or partially observed.

Observations were taken over four different epochs between
2023 January and 2024 May. For the most part, the observing
program was executed as planned; however, a small fraction of
the total visits (~10%) were either fully skipped or partially
observed due to issues with guide star acquisition, and those
visits were reobserved in the final two epochs. This resulted in
a slightly larger area covered than initially proposed, with
some parts of the mosaic having fewer exposures due to the
nature of parallel observations obtained at different position
angles.

Considering the field of view of both the main imager and
the Lyot coronagraphic imager (which has the same optical
path as the main imager<sup>24</sup>), the total area coverage of MIRI per
visit is ~4.5 arcmin<sup>2</sup>, and therefore the total area covered with
MIRI (accounting for both additional coverage from repeat
visits and reduced coverage due to skipped/failed visits) is
722 arcmin<sup>2</sup> or ~0.2 deg<sup>2</sup>. Figure A1 in the Appendix shows a
map of all the completed MIRI visits from the observing
program, and information regarding these visits is provided in
Table A1 in the Appendix.

3. Data Reduction

The MIRI imaging data were reduced using the standard JWST Science Calibration Pipeline<fnref n="25" /> (v1.12.5; H. Bushouse et al. 2023), hereafter referred to as the pipeline, employing calibrations based on the JWST Calibration Reference Data System<fnref n="26" /> Pipeline Context version 1130. The reduction procedure, starting from raw uncalibrated data to the creation of final mosaics through three different stages, including the implementation of a custom routine for improved background subtraction, is detailed in the following subsections.

3.1. Stage 1: Detector Processing

The *Detector1Pipeline*, also known as the *CALWEBB-DETECTOR1*, performs detector-level processing that reduces raw nondestructively read ramps to uncalibrated slope (*rate*) images. This primarily includes data quality initialization, reference pixel correction, jump detection, and slope fitting, along with MIRI-specific steps such as corrections for reset anomaly, first and last frame (all due to transient effects), nonlinearity, “reset switch charge decay,” and dark subtraction. For a typical visit, the default Stage 1 parameters are sufficient without the need for modifications. However, we note that the reference pixel and reset corrections are not applied by default in recent versions of the pipeline, as these effects are already incorporated into the dark reference file (M. Regan 2025, private communication). These corrections will therefore be turned off in Stage 1 in a future data release.

Several artifacts arising due to certain nonideal behaviors of
the detector (see J. E. Morrison et al. 2023 for a detailed
discussion) remain unaddressed in the pipeline. A visual
inspection of the *rate* images indicates the presence of some
of these artifacts, albeit in a smaller number of visits. The most
commonly observed artifact is called a “cosmic-ray shower,”
which is the result of large clusters of energetic particles
produced by cosmic rays, and affects a large number of pixels
on the MIRI detectors, typically below the detection threshold
for a direct cosmic-ray hit. In contrast to the “snowball”
artifact observed in the case of NIRCam, the cosmic-ray
showers are noncircular in nature and lack a well-defined
saturated core. Improvements to the pipeline to correct these
artifacts are currently under development, and at the time of
this writing, we note that a first-order correction for cosmic-ray
showers is included in the jump detection step of the pipeline,
which will be implemented in a future data release.

3.2. Stage 2: Image Processing

Following the initial corrections applied in Stage 1, we
further processed the *rate* images using *Image2Pipeline*,
also known as CALWEBB_IMAGE2, which was executed with
the default settings. This crucial stage involves the assignment
of World Coordinate System (WCS) information, flat-fielding,
and photometric calibration, culminating in fully calibrated
individual exposures. Thus, each rate map from Stage 1 is
transformed into a calibrated science (*cal*) image, with units
converted from counts per second to MJy sr⁻¹.

Although most MIRI visits have coverage with four or more
exposures, a small but significant fraction (~13%) of them have
only two exposures. As reported by M. B. Bagley et al. (2023)
in the CEERS program, the background levels in the final
mosaics are significantly improved if the *SkyMatch* step is run
on each *cal* image individually before proceeding with the final
stage of the pipeline. The *SkyMatch* step computes the sky
values in a given image and subtracts the measured background
(typically the median value) from said image. For this step, we
set *skymethod* = 'local' and *subtract* = True to
subtract a constant sky background calculated for each input
image.

Upon inspecting the background-subtracted *cal* image, we
noted significant residuals in the background (see Figure 1),
similar to those reported in other JWST MIRI observations
(e.g., G. Yang et al. 2023; S. Alberts et al. 2024b; P. G. Pére-
z-González et al. 2024a). In the following text, we discuss our
custom background subtraction procedure to mitigate this
issue, which is similar in principle to those implemented by
these other surveys.

3.2.1. Master Background Subtraction

More than two years into science operations, backgrounds in
MIRI are fairly well characterized and are expected to be a
combination of sky background and detector effects. The
former includes emission with astronomical origins (mainly
zodiacal dust and Milky Way interstellar medium) predomi-
nantly for wavelengths ~5–13 µm, while redder wavelengths
are dominated by thermal self-emission and stray light from
the observatory sunshield (A. Glasse et al. 2015; J. Rigby
et al. 2023). The detector effects include artifacts arising from
row–column effects, persistence, the cruciform effect, and
“tree-rings” (for more details see J. E. Morrison et al. 2023;

<footnote n="24">²⁴ https://jwst-docs.stsci.edu/jwst-mid-infrared-instrument/miri-observing-modes/miri-imaging#MIRIImaging-Imagingfieldofview</footnote>

<footnote n="25">²⁵ https://github.com/spacetelescope/jwst</footnote>

<footnote n="26">²⁶ https://jwst-crds.stsci.edu/</footnote>
<figure><img src="image_4.png" /><figcaption>**Figure 1.** Example MIRI F770W image based on the default pipeline output (left) and the same with our *master background subtraction* procedure applied (right) as detailed in Section 3.3. The stretch in both images is the same.</figcaption></figure>

D. Dicken et al. 2024), to name a few. At the time of this writing, some workarounds are available<fnref n="27" /> to mitigate some of these issues (as additional steps outside of the main pipeline processing); however, we found that these have minimal impact in terms of removing the large-scale gradients in our image backgrounds. Therefore, we address this issue using a custom routine called *master background subtraction*<fnref n="28" /> (similar to the “super-background” strategy adopted by other JWST surveys, e.g., G. Yang et al. 2023; S. Alberts et al. 2024b; P. G. Pérez-González et al. 2024a), which helps produce final mosaic images with a uniform sky background.

As an initial step, we perform a full reduction using the pipeline producing the final Stage 3 mosaic image (see Section 3.3) for each individual MIRI visit. Subsequently, we used Source Extractor (SE; E. Bertin & S. Arnouts 1996, version 2.28.0) to detect source emission in the mosaic image and create a source mask—called the “galaxy seed image”— for each MIRI visit, including all detections with $S/N \ge 3$. We then apply this source mask to each *rate* image in the corresponding MIRI visit, producing a “source-masked rate” image. Fortunately, most of the COSMOS-Web visits were observed in contiguous blocks in time (except for the repeat visits from 2024 April–May), where each block contained at least five or more visits observed in succession. Taking advantage of this, we construct a unique “merged source-masked rate” image by combining the “source-masked rate” images from all exposures within the same visit block, except the exposure at hand, as well as exposures from at least two additional contemporaneous visits. By excluding the exposure being processed and incorporating all others, this approach avoids the risk of the exposure subtracting itself during sky subtraction due to overlapping pixel values in source-free regions. This ensures that the large-scale temporal variation of the 2D background is well captured by merging multiple exposures/visits observed contemporaneously. Care was taken to ensure that holes (where source emission was masked)

caused due to dithering are replaced with median values while merging. Specifically, each pixel within a hole region in the “merged source-masked rate” image is assigned the median of the corresponding pixel values across the individual “source-masked rate” images. We then rerun Stage 2 on the original *rate* image, where the corresponding “merged source-masked rate” image is subtracted using the `Image2Pipeline.bkg_subtract` routine to produce a *master background-subtracted cal* image. This procedure was repeated set-by-set for every exposure in each MIRI visit in a set.

Finally, the resulting *master background-subtracted cal* images are fed to the next stage of pipeline processing (Stage 3). Figure 1 shows an example F770W image before and after applying our *master background subtraction* procedure.

### 3.3. Stage 3: Mosaic Creation

The final stage of the reduction, `Image3Pipeline`, also known as CALWEBB_IMAGE3, combines the calibrated data from Stage 2 from multiple exposures into a single, distortion-corrected science-ready mosaic image. This stage also includes some additional steps performed on the individual exposures, such as astrometric alignment and outlier rejection, before they are resampled and combined to produce a mosaic image. We briefly describe our adopted strategy with respect to these steps in the following text.

First is the astrometric correction performed by *TweakReg*. Typically, this step attempts to align the astrometry to the Gaia DR3 astrometric reference frame (Gaia Collaboration et al. 2023); however, the relatively small field of view of the MIRI imager (~2.3 arcmin²) implies extremely few or almost zero matches with Gaia objects in each MIRI pointing. The pipeline, however, includes an option to align the astrometry based on a user-provided external reference catalog. For this purpose, a reference catalog was derived based on a mosaic constructed from an updated version of the COSMOS HST/F814W image (A. M. Koekemoer et al. 2007), covering the entire COSMOS-Web region. The astrometry of this HST mosaic was predominantly aligned to the Gaia DR3 reference frame (Gaia Collaboration et al. 2023) with some adjustments included to align with the COSMOS2020 catalog (J. R. Weaver et al. 2022) as well as to ensure greater

<footnote n="27">²⁷ https://jwst-docs.stsci.edu/known-issues-with-jwst-data/miri-known-issues/miri-imaging-known-issues#MIRIImagingKnownIssues-summarySummaryofCommonIssuesandWorkarounds</footnote>

<footnote n="28">²⁸ https://github.com/1054/Crab Tool Kit.JWST/blob/main/docs/reprocessing_MIRI_with_ImageForImageBackgroundSubtraction.md</footnote>
<figure><img src="image_5.png" /><figcaption>**Figure 2.** Astrometric offsets between MIRI F770W detections ($S/N \ge 5$) and their HST/ACS F814W counterparts, which are aligned to Gaia DR3, assuming a cross-match radius of $1''$. Shown for reference are dashed circles with radii $0''.1, 0''.2$, and $0''.3$ centered on (0, 0), depicting the relative positional offsets of all MIRI sources. The median offsets in R.A. and decl. are 0.35 and 6 mas respectively (MIRI native pixel scale is 110 mas) with a median absolute deviation of 28 mas in both directions.</figcaption></figure>

astrometric accuracy. Care was taken to exclude stars from this reference catalog that could bias the astrometry due to proper motion. However, we note that there might be a faint population of field stars remaining in the catalog that might exhibit low-level bulk proper motion, whose proper motion is unknown if they are below the limits of the GAIA DR3 catalog. In this step, we also generate our own source catalogs using SE for all MIRI exposures in a single visit, which are aligned relative to each other and eventually merged to create one combined source catalog. This combined catalog is then cross-matched and fit to the aforementioned COSMOS reference catalog, the results of which are propagated back to all the individual MIRI exposures, with the relative alignment between exposures remaining intact. Figure 2 shows the differences in R.A. and decl. between MIRI F770W and HST F814W for the cross-matched sources. The median offsets are 0.53 and 6 mas in R.A. and decl. with a median absolute deviation (MAD) of 28 mas in each. We note that the MAD values are higher than the median offsets, which might be due to the low-level bulk proper motion of stars mentioned earlier, given the long time baseline between JWST and HST observations.

Even though we have already performed sky subtraction using *SkyMatch* as an additional step in Stage 2, we run *SkyMatch* again to normalize sky values with a median background level of zero for all MIRI exposures by setting `skymethod = 'global+match'` and `subtract = True`. This is followed by the *OutlierDetection* step—where bad pixels or cosmic-ray hits missed in Stage 1 or other artifacts carried over from previous calibrations are flagged—for which we use the default settings provided in the pipeline.

The final step is *Resampling*, which resamples individual calibrated exposures and combines them to produce a single mosaic image. In order to overcome the computational

challenges in handling the full NIRCam data set, the COSMOS-Web region was divided into 20 smaller subregions, referred to as *tiles*, to streamline the processing. The dimensions and coordinates of these tiles are provided in the corresponding NIRCam data reduction paper (M. Franco et al. 2025). However, the locations of the MIRI visits relative to the *tiles* are shown in Figure A1. These *tiles* have a fixed WCS solution that is aligned to the central coordinates of the COSMOS field (as adopted in previous works, e.g., A. M. Koekemoer et al. 2007; C. Laigle et al. 2016; J. R. Weaver et al. 2022). The mosaics are also oriented $20^\circ$ relative to north. The native pixel resolution of MIRI is 110 mas, but the *tile* mosaics are sampled at finer pixel scales of 30 and 60 mas, matching the NIRCam *tile* mosaics. Alternatively, because several visits fall outside the coverage of our NIRCam data, we also generate individual mosaic images for each MIRI visit, using the same setup applied to the *tile* mosaics.

Figure 3 shows the full COSMOS-Web MIRI mosaic, including a comparison to IRAC CH4 from different areas of the mosaic. Our MIRI images are publicly available<fnref n="29" /> in two configurations: *tiles* (overlapping with the NIRCam-based tiles) and *individual* (all unique 160 MIRI visits). These are provided as FITS multiextension format images known as i2d files, containing the standard set of extensions,<fnref n="30" /> as well as individual images for the three main extensions: science (SCI), error (ERR), and weight (WHT) images.

### 3.4. Survey Depth

The average $5\sigma$ (point-source) depths as a function of the number of exposures are given in Table 1. Our depth estimates were derived using the *ImageDepth* function of PHOTUTILS (L. Bradley et al. 2023). For each MIRI visit, we derive a *source mask* by detecting sources based on a median background-subtracted convolved image (with a detection threshold of $3\sigma$), and the image depth is then calculated by placing 200 random circular (nonoverlapping) apertures on the blank regions of the image, which is derived based on the input *source mask*. Here we adopt an aperture radius equal to the FWHM of the F770W point-spread function (PSF) i.e., $0''.27$, based on in-flight measurements from commissioning data.<fnref n="31" /> Aperture corrections are applied based on empirical PSF estimates from M. Libralato et al. (2024) as discussed in Section 4.1. As reported by C. M. Casey et al. (2023), the measured depths are significantly greater than predictions from the exposure time calculator (ETC; v2.0), with differences ranging from ~0.7 to 0.8 mag for the different numbers of exposures.

## 4. Source Catalog

The MIRI F770W photometric measurements were derived in two different ways to serve different scientific goals. Model-based photometry using empirical F770W PSFs (derived using PSFex, E. Bertin 2011) as a part of the multiwavelength catalog based on COSMOS-Web and overlapping archival ground- and space-based observations is provided in the

<footnote n="29">²⁹ https://cosmos.astro.caltech.edu/page/cosmosweb-dr</footnote>

<footnote n="30">³⁰ https://jwst-pipeline.readthedocs.io/en/latest/jwst/data_products/science_products.html#resampled-2-d-data-i2d-and-s2d</footnote>

<footnote n="31">³¹ https://jwst-docs.stsci.edu/jwst-mid-infrared-instrument/miri-performance/miri-point-spread-functions#gsc.tab=0; See Table 1.</footnote>
<figure><img src="image_6.png" /><figcaption>**Figure 3.** The COSMOS-Web MIRI F770W mosaic. The zoomed-in areas show 45" × 45" matched regions in both MIRI F770W and IRAC CH4 from different parts of the mosaic. These cutouts highlight the significant improvement in sensitivity (~50×) and resolution (~7×) of MIRI observations compared to IRAC. The montage of cutouts is shown with the same stretch for direct comparison.</figcaption></figure>

COSMOS-Web catalog (M. Shuntov et al. 2025). This catalog is primarily based on source detection using deep NIRCam imaging from COSMOS-Web, with photometry derived using the light profile-fitting method in SOURCEXTRACTOR++ (SE++; E. Bertin et al. 2020) for imaging data in 34 photometric bands in the COSMOS field. In this work, however, we use F770W photometry from the COSMOS-Web MIRI catalog that is derived using SE based on just the F770W imaging, as discussed in the following text. Since this catalog is primarily based on

source detection in the MIR, it is also used to find potential MIRI-only sources in COSMOS-Web, which will be explored in future work.

### 4.1. Source Extraction and Photometry

We performed source detection and photometry using SE with the main parameter settings listed in Table 2, while adopting default values for the remaining parameters. In order to limit the number of spurious objects, we masked all bright,
<table><caption>**Table 1**
Survey Depths as a Function of Number of Exposures</caption><thead><tr><th rowspan="2">No. of Exposures</th><th rowspan="2">Area (arcmin<sup>2</sup>)</th><th colspan="2">Depth (AB)</th></tr><tr><th>Actual</th><th>ETC</th></tr></thead><tbody><tr><td>2</td><td>89.0</td><td>25.15</td><td>25.33</td></tr><tr><td>4</td><td>436.9</td><td>25.51</td><td>25.70</td></tr><tr><td>6</td><td>29.4</td><td>25.55</td><td>25.76</td></tr><tr><td>8</td><td>127.8</td><td>25.85</td><td>25.98</td></tr></tbody></table>

**Note.** The actual values are aperture-corrected depths estimated using 0.27 radius apertures, as discussed in Section 3.4. The ETC estimates are based on 0.3 radius apertures, with no aperture corrections applied.

<table><caption>**Table 2**
Main SE Configuration Parameters Used for MIRI F770W Photometry in the COSMOS-Web MIRI Catalog</caption><thead><tr><th>Parameter</th><th>Value</th></tr></thead><tbody><tr><td><strong>Extraction</strong></td><td></td></tr><tr><td>DETECT_MINAREA</td><td>10 pixels</td></tr><tr><td>DETECT_THRESHOLD</td><td>1.5σ</td></tr><tr><td>ANALYSIS_THRESHOLD</td><td>1.5σ</td></tr><tr><td>FILTER_NAME</td><td>Gauss_3.0_5x5erb</td></tr><tr><td><strong>Photometry</strong></td><td></td></tr><tr><td>PHOT_AUTOPARAMS</td><td>2.5, 2.0</td></tr><tr><td>MAG_ZEROPOINT</td><td>26.581<sup>a</sup></td></tr><tr><td>PIXEL_SCALE</td><td>0.06</td></tr><tr><td><strong>Star/galaxy separation</strong></td><td></td></tr><tr><td>SEEING_FWHM</td><td>0.27<sup>b</sup></td></tr><tr><td><strong>Background</strong></td><td></td></tr><tr><td>BACK_TYPE</td><td>AUTO</td></tr><tr><td>BACK_SIZE</td><td>32 pixels</td></tr><tr><td>BACK_FILTERSIZE</td><td>3 pixels</td></tr><tr><td>BACKPHOTO_TYPE</td><td>LOCAL</td></tr><tr><td><strong>Deblending</strong></td><td></td></tr><tr><td>DEBLEND_NTHRESH</td><td>32</td></tr><tr><td>DEBLEND_MINCONT</td><td>0.008</td></tr></tbody></table>

**Notes.** All other parameters are set to program defaults.

<sup>a</sup> The magnitude zero-point (in units of AB) of images, converting from the JWST default of MJy sr⁻¹ to µJy pixel⁻¹ for a *PIXEL SCALE* of 0.06.

<sup>b</sup> *SEEING_FWHM* is set to the PSF FWHM of F770W based on in-flight measurements.

saturated sources (stars and galaxies) with diffraction spikes in each MIRI visit. Subsequently, we ran SE in dual-image mode with the masked image as the detection image and photometry measured using the original MIRI image. We adopt the photometry given by *FLUX_AUTO* as the total measured flux in our source catalog. These flux measurements are based on an adaptively scaled aperture, commonly known as *Kron* photometry; it is typically measured using elliptical apertures with a certain scaling factor called the Kron parameter (*K*). As shown in Table 2, we set *K* = 2.5 with the minimum value of the Kron radius set to 2.0. For compact and/or faint objects, especially those in regions of higher background, Kron photometry can be unreliable due to artificially inflated apertures (A. W. Graham & S. P. Driver 2005). Therefore, we also measured photometry using fixed (circular) apertures

with diameters 0.3, 0.6, 1.0, 1.5, and 2.0, which are mainly useful for point sources.

SE-derived photometric uncertainties typically account for photon and detector noise only, and not the pixel-to-pixel correlated noise. In order to account for this uncertainty, we derive an empirical noise estimate based on the random empty-aperture method. Similar to the procedure mentioned in Section 3.4, we placed 1500 random circular apertures with aperture diameters in the range 0.3–3" across each MIRI visit (using noise-equalized images). For each aperture diameter, we measured the noise by fitting a Gaussian to the negative side of the flux distribution, thereby avoiding any flux from real sources. Since the measured noise is dependent on the number of pixels in a given aperture, we fit a power law to this distribution following S. L. Finkelstein et al. (2022) and M. J. Rieke et al. (2023) as follows:

$$ \sigma_N = \sigma_1 \alpha N^\beta, \quad (1) $$

where $\sigma_N$ is the noise in an aperture with *N* pixels, $\sigma_1$ is the pixel-to-pixel noise measured per image as the sigma-clipped standard deviation of all pixels excluding source detections, and $\alpha$, $\beta$ are free parameters that we fit for using Python's *curve_fit* function. Based on these functional form fits, we derive the total photometric uncertainties for each source in the catalog using the number of pixels in the respective Kron aperture (area = $\pi \times A\_IMAGE \times B\_IMAGE \times KRON\_RADIUS^2$, where $A\_IMAGE$ and $B\_IMAGE$ are the semimajor and semiminor axes of the elliptical apertures), with the SE measured uncertainties added in quadrature.

For both Kron and fixed-aperture photometry, we calculate aperture corrections based on empirical PSF (ePSF) from M. Libralato et al. (2024) derived using MIRI imaging from multiple publicly available JWST (Commissioning, Cycle 1, and GO) programs. This PSF version takes into account effects such as the cruciform artifact seen in the bluest MIRI filters, F560W and F770W. We compute the encircled flux (EEF) of the ePSF over different radii, and the aperture corrections are derived as 1/EEF(*r*), where *r* is the radius of the circular aperture for fixed-aperture photometry, and *r* = $KRON\_RADIUS \times \sqrt{A\_IMAGE \times B\_IMAGE}$ in the case of Kron apertures.

## 4.2. Comparison with Model-based Photometric Catalog

Figure 4 compares the AUTO photometry for F770W from the COSMOS-Web MIRI catalog with the model-based SE++ photometry from the COSMOS-Web galaxy catalog. Only sources that were cross-matched between the two catalogs were included in this comparison. Since the AUTO fluxes are measured using Kron-like elliptical apertures that scale according to the brightness of the sources and include PSF-based aperture corrections, they are a good proxy for the total flux estimates and therefore a fair comparison with the SE++ model fluxes. Overall, there is good agreement between the two measurements with a running median offset ($mag_{MODEL} - mag_{AUTO}$) between -0.04 and 0.06 mag with a median scatter of ~0.22 mag. Due to the paucity of sources at the bright end, the median offset and the scatter are slightly higher; however, for the rest of the source distribution, the observed median offset shows that these measurements are largely consistent within the $\pm 1\sigma$ combined photometric uncertainties.
<figure><img src="image_7.png" /><figcaption>**Figure 4.** Photometric comparison between SE++ model magnitudes and SE AUTO aperture magnitudes for MIRI F770W. The median offset along with the 3σ-clipped standard deviation is shown by the white dashed lines. Also shown for reference are the ±1σ and ±3σ photometric uncertainties (cyan dotted lines) combined in quadrature from the two photometric measurements.</figcaption></figure>

### 4.3. Completeness Simulations

A measure of source completeness—the fraction of objects detected at a given flux density given the image noise properties—is imperative to accurately estimate the statistical properties of a given sample. For this purpose, we employ the ComEst<fnref n="32" /> package (I. Chiu et al. 2016) to derive the source completeness in our images. The fundamental idea behind ComEst is to simulate synthetic point sources and galaxies on the observed “source-free” image using the image simulation toolkit, GALSIM (B. T. P. Rowe et al. 2015), and then run SE on the same image to derive the source detection rate as a function of flux (or magnitude). Initially, ComEst runs SE to generate a first-order source catalog along with various check-images (which are diagnostic images output by SE), and thereafter removes the detected sources from the observed image and replaces those pixel values by randomly sampling the background values (assuming a normal distribution with mean and standard deviation in the BACKGROUND and BACKGROUND_RMS check-images provided by SE), thereby producing the source-free image (SFI). This is followed by the injection of “fake” sources, simulated using GalSim, on the SFI to create a set of simulated images on which SE is again run to detect the simulated sources. Finally, the completeness as a function of source flux (or magnitude) is derived by comparing SE catalogs of simulated images with the true catalog that is used as an input to the simulation.

For source injection on the SFI, we chose to simulate only point sources since a majority of the objects that are resolved in F770W are either bright or extended or both (e.g., the half-light radius, $r_{50}$, for ~80% of the sources with $m_{F770W} < 21$ is greater than the FWHM of the F770W PSF), while most of the faint sources—the regime of higher source incompleteness—are typically unresolved (or marginally resolved at best) and appear compact in our observations. For simulation of point sources, ComEst simply convolves each source with a Moffat

<figure><img src="image_8.png" /><figcaption>**Figure 5.** Completeness fraction as a function of MIRI F770W flux. The uncertainties (shaded region) shown here are purely Poisson errors. The 80% completeness limit (horizontal dashed line) as well as the median 5σ limiting depth (vertical dotted line), including the 1σ scatter (gray shaded region), are shown for reference. Our MIRI source sample achieves ≥80% completeness for all sources detected with S/N ≥ 5σ.</figcaption></figure>

PSF ($\beta = 4.5$) using the user-defined FWHM, which in this case is equal to the F770W's FWHM value of 0.27. The number density of the simulated sources per arcmin² is set to 30, and the source flux distribution ranges from 0.01 to 2500 µJy. ComEst can simulate multiple images with the same configuration scheme in parallel, so we generate 20 simulated images for each unique MIRI visit in our program. ComEst eventually produces two merged catalogs for each simulation: one based on the SE catalogs from all simulated images, and one based on the input true catalogs for those simulated images. The resulting completeness estimates as a function of source flux are shown in Figure 5. As shown in the figure, we achieve 80% completeness, corresponding to the median 5σ depths measured in our images, i.e., ~0.2 µJy.

## 5. Results and Discussion

### 5.1. Comparison of JWST/MIRI and Spitzer/IRAC Photometry at 7.7 µm

We compare our MIRI F770W fluxes with the IRAC CH4 (8 µm) fluxes from the COSMOS2020 catalog (J. R. Weaver et al. 2022) as shown in Figure 6. The reported 5σ depth for IRAC CH4 is 22.5 mag, so we restrict our comparison to only source detections with $S/N_{CH4} \ge 5$ and $m_{CH4} \le 22.5$ mag. Based on the binned median offsets ($\Delta_{mag} = m_{F770W} - m_{CH4}$), the overall agreement is reasonable with offsets that are fairly small ($\Delta_{mag} \le 0.05$ mag) with a 3σ-clipped standard deviation of 0.26 mag for the bulk of the distribution in the range $19.5 < m_{F770W} < 22$. Previous studies (e.g., G. Yang et al. 2023) have shown that this level of offset (<0.1 mag) is reasonable, especially given the differences in filter transmission for MIRI and IRAC and also the fact that these observations are calibrated independently. However, the offsets become larger toward the faint end ($m_{F770W} > 22$), as we approach the limiting depth for CH4, which might be partly driven by large uncertainties in the CH4 photometry. At the bright end, $m_{F770W} < 19.5$, there exists a systematic brightening of the CH4 photometry ($\Delta_{mag} > 0.2$) compared to F770W. In the following text, we postulate probable reasons

<footnote n="32">³² https://github.com/inonchiu/ComEst</footnote>
<figure><img src="image_9.png" /><figcaption>**Figure 6.** Photometric comparison of MIRI F770W detections with IRAC CH4 measurements from the COSMOS2020 catalog. For the sake of accuracy, we compare MIRI photometry with IRAC CH4 only for sources with $S/N_{CH4} \ge 5$ and $\text{mag}_{CH4} \le 22.3$ in the COSMOS2020 catalog (brown-black points). Left panel: a 1:1 comparison with the identity line (diagonal dashed-dotted line) and the 5$\sigma$ limiting depth for IRAC CH4 (horizontal dotted line) shown for reference. For the matched sample, the magnitude offset ($\Delta_{\text{mag}} = \text{F770W} - \text{CH4}$) as a function of the MIRI F770W photometry is shown in the right panel. Also shown are median offsets per magnitude bin (orange-red squares) derived by binning sources based on their MIRI F770W photometry along with their 3$\sigma$-clipped standard deviation. Some of the bins at the bright end ($m_{\text{F770W}} < 19.5$) have asymmetric widths because we required each bin to contain a minimum of 10 sources.</figcaption></figure>

for the observed offsets between the two photometric measurements.

The IRAC CH4 photometry from the COSMOS2020 catalog (J. R. Weaver et al. 2022) is model-based, and is jointly derived using the *izYJHKs* imaging at optical/NIR wavelengths, where IRAC fluxes are obtained by convolving the model with the spatially varying CH4 PSF across the entire coverage. If the source properties such as the size or shape vary significantly as a function of wavelength, then this might result in uncertain flux measurements, particularly for objects that are faint in the modeled bands but bright in the MIR. We note that most of our bright sources ($m_{\text{F770W}} < 19.5$) are at low redshifts, $z < 0.5$, which might result in systematic offsets for objects at a given brightness due to morphological differences between the modeled bands and CH4. In contrast, the F770W photometry is directly based on high-resolution images at MIR wavelengths. Also, because of an order-of-magnitude broader PSF in the case of CH4, the IRAC photometry is significantly affected by source blending compared to MIRI, especially for bright sources. This effect has also been observed in other studies comparing MIRI and IRAC photometry with F770W (G. Yang et al. 2023) and F560W (L. Sajkov et al. 2024; G. Östlin et al. 2025) filters.

## 5.2. Photometric Redshifts

Using a modified version of the template-fitting code LePHARE (S. Arnouts et al. 2002; O. Ilbert et al. 2006), photometric redshifts are provided in the COSMOS-Web catalog based on multiband photometry including MIRI F770W where available. Compared to previous versions of the COSMOS catalog (C. Laigle et al. 2016; J. R. Weaver et al. 2022), the current version of redshift fits is based on a

diverse and representative library of galaxy templates (stellar and AGN) as well as a wide range of dust attenuation parameters, especially with the inclusion of F770W imaging where PAH emissions from low-redshift sources could contribute significantly to the galaxy’s SED. For more information on the quality of photo-z fits and their performance with respect to the spectroscopic redshifts, we refer the reader to our COSMOS-Web catalog paper (M. Shuntov et al. 2025).

Figure 7 shows the redshift distribution of the 7.7 µm MIRI-selected sources. The distribution spans a wide redshift range from $z \sim 0$ through $z \sim 8$ with ~80% of the sources lying at $z \le 3$. Sources with $z > 4$ constitute ~5% of our sample. As seen in Figure 7, the majority of bright sources ($m_{\text{F770W}} < 22$) are dominated by low-redshift objects with $z \le 2$, whereas the higher-redshift sources are more prevalent at fainter magnitudes as expected. The defined peaks in the distribution at $z \sim 1-2$ are an observational effect caused by galaxies with increasing stellar continuum emission dominating the 7.7 µm wavelengths and potentially the contributions from broad 3.3 µm PAH emission from star-forming galaxies at these redshifts. Using the best-fit $\chi^2$ values for various templates, the Bzk color criteria (E. Daddi et al. 2004), and source properties such as the effective radius $R_{\text{eff}}$ derived from the LePHARE measurements, sources in the COSMOS-Web catalog are classified into stars, galaxies, and AGN. For the MIRI-selected sources, we find a diverse population of galaxies and AGN spanning a wide redshift range, with the relative fraction of AGN increasing at $z \ge 3.5$. However, we note that the AGN classification is somewhat less robust. For a more detailed and accurate determination of photo-z for AGN, we refer interested readers to the work by M. Salvato et al. (2011).
<figure><img src="image_10.png" /><figcaption>**Figure 7.** Normalized photometric redshift distribution of the 7.7 µm MIRI-selected sources from the COSMOS-Web catalog. Left: redshift distribution for the subsamples based on two different brightness thresholds. The bright end ($m_{F770W} < 22$) is dominated by low-redshift sources ($z < 2$) whereas higher-redshift sources ($z > 2$) become more prevalent as we move toward the faint end. Right: redshift distribution for the subsamples based on the source classification from LePHARE measurements. Each source class is normalized independently to highlight relative redshift distributions.</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>**Figure 8.** Cumulative number counts at 7.7 µm from COSMOS-Web in comparison with model predictions (left) and other JWST and IRAC number counts from the literature (right). Uncertainties for the COSMOS-Web number counts are a combination of Poisson and cosmic variance errors added in quadrature. The dotted vertical line in each panel represents the average $5\sigma$ limiting depth achieved in the COSMOS-Web MIRI observations, and also corresponds to the 80% completeness limit as mentioned in Section 4.3. In the left panel, we compare our source counts with a number of model predictions at 8 µm including those based on the semianalytical models GALFORM+GRASIL (W. I. Cowley et al. 2018) and SHARK (C. d. P. Lagos et al. 2019), the SPRITZ simulations (L. Bisigello et al. 2021), and the semiempirical models of M. Rowan-Robinson (2009, 2024) and V. I. Kokorev et al. (2021). We also compare our number counts with estimates from recent JWST (MIRI F770W) studies including the CEERS (G. Yang et al. 2023), JADES, and SMILES (M. A. Stone et al. 2024) programs, as well as Spitzer (IRAC CH4) counts from the SDWFS (M. L. N. Ashby et al. 2009) program.</figcaption></figure>
<table><caption>**Table 3**
Cumulative Number Counts at 7.7 µm</caption><thead><tr><th>Flux<br>(µJy)</th><th>N(&gt;S<sub>ν</sub>)<sup>a</sup><br>(10<sup>-6</sup> sr<sup>-1</sup>)</th></tr></thead><tbody><tr><td>0.22</td><td>705.76 ± 3.4</td></tr><tr><td>0.37</td><td>605.72 ± 3.2</td></tr><tr><td>0.58</td><td>517.079 ± 2.9</td></tr><tr><td>0.85</td><td>437.75 ± 2.7</td></tr><tr><td>1.21</td><td>366.04 ± 2.5</td></tr><tr><td>1.89</td><td>287.30 ± 2.2</td></tr><tr><td>2.96</td><td>221.30 ± 1.9</td></tr><tr><td>4.64</td><td>165.63 ± 1.7</td></tr><tr><td>7.28</td><td>116.06 ± 1.4</td></tr><tr><td>11.41</td><td>75.30 ± 1.1</td></tr><tr><td>17.88</td><td>43.29 ± 0.8</td></tr><tr><td>28.03</td><td>20.21 ± 0.6</td></tr><tr><td>43.94</td><td>9.12 ± 0.4</td></tr><tr><td>68.88</td><td>4.67 ± 0.3</td></tr><tr><td>107.98</td><td>2.54 ± 0.2</td></tr><tr><td>169.27</td><td>1.44 ± 0.2</td></tr><tr><td>265.34</td><td>0.78 ± 0.1</td></tr><tr><td>415.96</td><td>0.40 ± 0.1</td></tr><tr><td>652.06</td><td>0.18 ± 0.1</td></tr><tr><td>1022.17</td><td>0.08 ± 0.04</td></tr><tr><td>1602.37</td><td>0.03 ± 0.02</td></tr></tbody></table>

**Note.**

<sup>a</sup> The reported counts are based on the COSMOS-Web MIRI catalog and include completeness corrections, and the reported errors include Poisson as well cosmic variance uncertainties.

## 5.3. Number Counts

We derive the MIRI 7.7 µm cumulative number counts (completeness-corrected) based on the COSMOS-Web MIRI catalog described in Section 4. Given that we consider only sources in flux bins with completeness fractions ≥80% toward the estimation of number counts, ~85% of the total MIRI coverage reaches imaging depths that are at least ~25.5 mag or better, which is comparable to the average 5σ limiting depths reported in Section 3.4. Therefore, we assume that our coverage is largely uniform across visits, so the total effective area as a function of flux bins remains the same. Figure 8 shows our 7.7 µm number counts that are completeness-corrected based on the simulations presented in Section 4.3, along with Poisson errors that purely reflect statistical uncertainties. The corresponding values are provided in Table 3.

For comparison, the 7.7 µm number counts based on the model-based photometry from the COSMOS-Web catalog are also shown in Figure 8, and are overall in good agreement with those based on the COSMOS-Web MIRI catalog (within 1σ uncertainties), except for a small but negligible overprediction at the bright end; this might be partly due to the larger offsets observed between the two photometric measurements for bright sources, as discussed in Section 4.2.

### 5.3.1. Cosmic Variance

In most extragalactic surveys, especially with smaller coverage, number or density measurements derived from galaxy populations as a whole are prone to cosmic variance uncertainties since the Universe is inhomogeneous on small scales (S. P. Driver & A. S. G. Robotham 2010). The impact of cosmic variance is typically lower for surveys with larger sky

coverage and those including multiple distinct sightlines. Also, cosmic variance is higher for massive high-redshift populations and brighter sources in general (B. P. Moster et al. 2011).

Since COSMOS-Web has a large areal coverage, our reported 7.7 µm number counts will be minimally impacted by cosmic variance compared to similar measurements from JWST surveys of smaller areas (e.g., JADES and SMILES). Given that it is a contiguous field, the cosmic variance uncertainties might be significant at the bright end of the number counts, however. As discussed in Section 5.3.2, the small offset observed between IRAC and MIRI number counts could be partially due to cosmic variance, among other things such as filter differences (Section 5.1) and the presence of stars in the IRAC estimates. Using the empirical expression given by S. P. Driver & A. S. G. Robotham (2010), we estimate the potential impact of cosmic variance<fnref n="33" /> on the observed COSMOS-Web 7.7 µm number counts as follows. Based on the observed redshift distribution, more than 70% of the sources are at redshifts $z \le 3$; since the number counts are dominated by galaxies at these redshifts, we derive the cosmic variance uncertainties assuming a source distribution with $0.01 < z < 3$. While sources at $z > 3$ are included in the number counts, they primarily contribute to the faintest flux bins and represent a small fraction of the sample; their impact on cosmic variance is expected to be minor, particularly given the large survey area (M. Trenti & M. Stiavelli 2008). For a total area of ~0.2 deg² (assuming cosmological parameters based on Planck Collaboration et al. 2020), the expected cosmic variance is ~6.5%, which is added in quadrature to the Poisson errors mentioned in Section 5.3.

### 5.3.2. Comparison with Other Observations

As mentioned earlier, a number of recent studies have derived number counts at different mid-IR wavelengths (5.6–25.5 µm) using MIRI observations from various JWST programs (e.g., C.-T. Ling et al. 2022; C. K. W. Wu et al. 2023; G. Yang et al. 2023; L. Sajkov et al. 2024; M. A. Stone et al. 2024). Figure 8 (right) includes a comparison of our number counts at 7.7 µm with corresponding estimates from (1) the CEERS program given by G. Yang et al. (2023), and (2) the SMILES and JADES programs given by M. A. Stone et al. (2024). Overall, up to the 80% completeness flux limits, there exists a good overlap with these other estimates within the 1σ uncertainties. At the faint end ($S_ν \lesssim 1$ µJy), a small offset exists between estimates based on SMILES observations and other programs that is attributed to potential effects of cosmic variance as reported by M. A. Stone et al. (2024); this offset is the likely cause of typical variance in observed counts between different surveys. However, toward the bright end ($S_ν \gtrsim 20$ µJy), small differences exist between various estimates. This is mostly driven by the fact that most of these other programs probe significantly smaller areas (~10–35 arcmin²), thereby resulting in large scatter in the brightest flux bins. The power of COSMOS-Web really shines in this regime thanks to the large areal coverage of ~0.2 deg², which is 30–70 times the area observed by other programs.

We also compare our number counts to those based on IRAC CH4 detections from the Spitzer Deep Wide Field Survey (SDWFS; M. L. N. Ashby et al. 2009), which observed

<footnote n="33">³³ https://cosmocalc.icrar.org/</footnote>
a large ~10 deg² area in the Boötes field with an average 5σ limiting depth of 20.18 mag (~30 µJy). We derived the CH4 number counts based on the 8 µm source flux measurements from the 8 µm-selected catalog provided by M. L. N. Ashby et al. (2009). As shown in Figure 8 (right), there is a good agreement between estimates at the bright end of this comparison (within 1σ uncertainties); however, the CH4-based number counts are systematically higher than the MIRI-based estimates except for a limited overlap with CEERS and SMILES. The IRAC CH4 filter bandwidth is slightly higher and extends to slightly longer wavelengths (~9.3 µm) than F770W (~8.7 µm). Therefore, in the case of a small fraction of sources at low redshifts, the presence of a bright PAH feature that is redshifted out of F770W's bandpass, but still detectable in CH4, will result in slightly brighter fluxes in CH4 than in F770W (e.g., see Figure 7 of G. Yang et al. 2023); however, this is expected to have minimal effect on the measured number counts when comparing MIRI to IRAC counts. In summary, our measured number counts are consistent with counts derived from other JWST observations (within 1σ uncertainties) but slightly underpredict compared to counts based on Spitzer observations. COSMOS-Web is the only JWST-based survey to date to have sampled a wide flux range over a large area, thereby providing a comprehensive estimate of the number counts at 7.7 µm.

### 5.3.3. Comparison with Models

Typically, it has been challenging for galaxy simulations to reproduce the observed number counts (e.g., see R. S. Somerville et al. 2012; C. G. Lacey et al. 2016) since galaxies with different masses and from different cosmic epochs contribute to this observable, and it is hard for simulations to predict accurately the expected galaxy counts across the entire history of the Universe and also over a wide wavelength range (e.g., far-UV to far-IR). Therefore, reconciling the observed number counts with predictions from theoretical and/or semianalytical models of galaxy formation and evolution is an important exercise for improving our modeling efforts. Figure 8 (left) shows predictions of number counts based on several different models in comparison with our observed counts. In the following text, we provide a brief overview of these different models along with a discussion on the conformity of the observed counts with model predictions.

The predictions of W. I. Cowley et al. (2018) are based on a model combining a dark matter-only N-body simulation, the semianalytical model, GALFORM (S. Cole et al. 2000), and the dust radiative transfer models from GRASIL (L. Silva et al. 1998), which account for interstellar dust absorption and reemission of stellar radiation in computing the galaxy SEDs. Apart from the fiducial model, they also provide another version that includes evolving supernova feedback as a function of redshift, which is able to better match the reionization redshift as inferred from the Planck cosmic microwave background data (Planck Collaboration et al. 2016) than the fiducial model. In this work, we compare our counts with the “evolving feedback” model that is in better agreement with observational data from both low and high redshifts.

The predictions of C. d. P. Lagos et al. (2019) utilize a combination of the semianalytical model, SHARK (C. d. P. Lagos et al. 2018), and radiative transfer estimates from the EAGLE simulations (J. W. Trayford et al. 2017) for dust attenuation along with Spitzer-based IR templates

(D. A. Dale et al. 2014) to account for dust reemission. These predictions have achieved considerable success in reproducing the observed number counts from UV to the far-IR (assuming a universal initial mass function) owing to accurate estimates of galaxy properties including, but not limited to, dust masses and temperature, gas metallicities, galaxy sizes and gas content, to name a few.

Using a novel panchromatic SED fitting tool, STARDUST, V. I. Kokorev et al. (2021) provide a multiparameter catalog containing infrared properties of ~5000 star-forming galaxies from the Great Observatories Origins Deep Survey North (GOODS-N; M. Dickinson et al. 2001) and COSMOS fields, including photometric predictions for the MIRI F770W band. Apart from considering the three components—stellar, AGN and star-forming-IR templates—in the fitting process, an important highlight of STARDUST is that it treats the stellar and dust emission components independently without imposing any energy balance between the UV/optical absorption and emission in the IR.

Model predictions from L. Bisigello et al. (2021) are based on the SPRITZ simulation, which is built from a set of observed luminosity functions at IR wavelengths (Herschel and K-band) as well as a galaxy stellar mass function, covering different galaxy populations. The limitations of semianalytical and hydrodynamical simulations in reproducing the observed number density of luminous IR galaxies are addressed in SPRITZ by properly accounting for the contribution of nebular emission arising from star formation as well as AGN to the resulting IR emission.

The counts predicted by M. Rowan-Robinson (2024) are derived based on the semiempirical model given by M. Rowan-Robinson (2001, 2009), which assumes that the main features of the infrared galaxy population are largely captured by four different source templates (quiescent, starbursts, extreme starbursts, and AGN), as detailed by M. Rowan-Robinson (2001).

Overall, the predictions based on SHARK and SPRITZ models tend to be in good agreement with each other as well as with our observed counts. The models of W. I. Cowley et al. (2018) and M. Rowan-Robinson (2024) appear to slightly overpredict fainter objects when compared to the SHARK and SPRITZ models as well as the observed counts, while the predictions from V. I. Kokorev et al. (2021) underestimate the counts below $S_ν \sim 10$ µJy in comparison with all other models/observations.

With increasing small-number statistics toward the bright end of the observed counts, the model predictions are almost always higher in comparison. As noted in previous studies (e.g., G. Yang et al. 2023; M. A. Stone et al. 2024), galaxy SEDs in the wavelength range probed by MIRI are strongly affected by PAH features, which are represented schematically in the models of W. I. Cowley et al. (2018) and M. Rowan-Robinson (2024). However, the SHARK and SPRITZ models do include an accurate representation of PAH emission and absorption features based on Spitzer InfraRed Spectrograph (IRS) spectra, which might play a critical role in the resulting agreement of model predictions with the observed counts.

Considering that estimates of number counts are based on sources across a wide range of redshifts and with different physical properties, a reasonable agreement between different model predictions and observations is promising, especially given the simplistic assumptions and differing prescriptions of galaxy properties adopted by various modeling approaches. It
is worth noting that such a comparison with new and powerful JWST observations, covering a wide range in flux and wavelength, can help better constrain the models of galaxy formation and evolution.

## 6. Summary and Conclusions

In this work, we present an overview of the observations and data reduction of MIRI F770W images from the largest Cycle 1 (GO) JWST program, COSMOS-Web. We performed our imaging reduction using the standard JWST Calibration Pipeline with an additional custom step to mitigate the characteristic effect of MIRI background as detailed in Section 3.3. The measured average 5σ image depths across MIRI visits are significantly better than ETC predictions, thanks to our careful background subtraction procedure. We derive a source catalog using SE based on the F770W observations alone and conduct simulations to measure the completeness of our sample as a function of source flux.

We compare the JWST/MIRI F770W fluxes with archival Spitzer/IRAC CH4 photometry and find them to be largely consistent across most of the sample, with a small offset primarily due to differences in broadband transmissions and calibration methods. We also derive the MIRI number counts at 7.7 µm and compare them to other MIRI F770W and IRAC CH4 estimates from previous studies as well as theoretical/ semianalytical model predictions from the literature. Our findings show strong agreement with the estimates from various JWST programs, though there is a slight under- prediction when compared to IRAC-based estimates. Notably, our number counts span an impressive range of fluxes—five orders of magnitude, from approximately 0.2 to 2300 µJy, which is a testament to the preeminence of COSMOS-Web.

## Acknowledgments

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 (MAST) 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 #1727, which can be found on MAST: DOI: 10.17909/ ph8h-qf05. Support for this work was provided by NASA through grant JWST-GO-01727 awarded by the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS 5-26555. This work was made possible by utilizing the CANDIDE cluster at the Institut d'Astrophysique de Paris, which was funded through grants from the PNCG, CNES, DIM-ACAV, and the Cosmic Dawn Center and maintained by S. Rouberol. The French contingent of the COSMOS team is partly supported by the Centre National d'Etudes Spatiales (CNES). This research made use of Photutils, an Astropy package for detection and photometry of astronomical sources (L. Bradley et al. 2023).

**Facility:** JWST (MIRI).

**Software:** Astropy (Astropy Collaboration et al. 2013, 2018, 2022), ComEst (I. Chiu et al. 2016), matplotlib (J. D. Hunter 2007), NumPy (C. R. Harris et al. 2020), photutils (L. Bradley et al. 2023), SciPy (P. Virtanen et al.

2020), Source Extractor (E. Bertin & S. Arnouts 1996), SourceXtractor++ (E. Bertin et al. 2020).

## Appendix

### Summary of COSMOS-Web MIRI Visits

Here we provide information on the 165 visits with MIRI F770W data that make up the COSMOS-Web MIRI coverage. Table A1 lists each individual visit along with its reference position, number of exposures, and position angle (P.A.). These include the original visits as well as the repeats that were taken for visits that either failed or were skipped due to guide star failures. Since MIRI observations were executed in parallel mode, a different P.A. for a particular repeat visit compared to the original visit meant a different region of sky coverage in MIRI. Figure A1 shows the exposure map of all MIRI visits listed in Table A1.

<table><caption>Table A1
Summary of COSMOS-Web MIRI Visits</caption><thead><tr><th>Obs. No.</th><th>R.A.</th><th>Decl.</th><th>N<sub>exp</sub></th><th>P.A.<br>(deg)</th></tr></thead><tbody><tr><td>1</td><td>09:59:23.315</td><td>+02:32:04.48</td><td>8</td><td>293</td></tr><tr><td>3</td><td>09:59:31.518</td><td>+02:31:19.75</td><td>8</td><td>293</td></tr><tr><td>4</td><td>09:59:23.594</td><td>+02:25:53.69</td><td>8</td><td>293</td></tr><tr><td>5</td><td>09:59:39.710</td><td>+02:30:35.08</td><td>8</td><td>293</td></tr><tr><td>6</td><td>09:59:31.793</td><td>+02:25:08.97</td><td>8</td><td>293</td></tr><tr><td>7</td><td>09:59:47.919</td><td>+02:29:50.30</td><td>8</td><td>293</td></tr><tr><td>8</td><td>09:59:40.008</td><td>+02:24:24.26</td><td>8</td><td>293</td></tr><tr><td>10</td><td>09:59:48.203</td><td>+02:23:39.46</td><td>8</td><td>293</td></tr><tr><td>11</td><td>10:00:04.309</td><td>+02:28:20.92</td><td>8</td><td>293</td></tr><tr><td>12</td><td>09:59:56.403</td><td>+02:22:54.72</td><td>8</td><td>293</td></tr><tr><td>13</td><td>10:00:12.513</td><td>+02:27:36.19</td><td>8</td><td>293</td></tr><tr><td>14</td><td>10:00:04.592</td><td>+02:22:10.08</td><td>8</td><td>293</td></tr><tr><td>15</td><td>10:00:20.721</td><td>+02:26:51.40</td><td>8</td><td>293</td></tr><tr><td>16</td><td>10:00:12.792</td><td>+02:21:25.37</td><td>8</td><td>293</td></tr><tr><td>17</td><td>10:00:28.920</td><td>+02:26:06.66</td><td>8</td><td>293</td></tr><tr><td>18</td><td>10:00:20.776</td><td>+02:20:32.97</td><td>6</td><td>293</td></tr><tr><td>19</td><td>10:00:36.891</td><td>+02:25:14.46</td><td>6</td><td>293</td></tr><tr><td>20</td><td>10:00:29.187</td><td>+02:19:55.93</td><td>8</td><td>293</td></tr><tr><td>21</td><td>10:00:45.307</td><td>+02:24:37.30</td><td>8</td><td>293</td></tr><tr><td>22</td><td>10:00:36.738</td><td>+02:18:48.47</td><td>2</td><td>293</td></tr><tr><td>23</td><td>10:00:53.507</td><td>+02:23:52.57</td><td>8</td><td>293</td></tr><tr><td>24</td><td>10:00:45.586</td><td>+02:18:26.47</td><td>8</td><td>293</td></tr><tr><td>25</td><td>10:01:01.716</td><td>+02:23:07.77</td><td>8</td><td>293</td></tr><tr><td>26</td><td>10:00:53.792</td><td>+02:17:41.68</td><td>8</td><td>293</td></tr><tr><td>27</td><td>10:01:09.906</td><td>+02:22:23.15</td><td>8</td><td>293</td></tr><tr><td>28</td><td>10:01:01.982</td><td>+02:16:57.04</td><td>8</td><td>293</td></tr><tr><td>29</td><td>10:01:18.116</td><td>+02:21:38.34</td><td>8</td><td>293</td></tr><tr><td>30</td><td>10:01:10.182</td><td>+02:16:12.31</td><td>8</td><td>293</td></tr><tr><td>31</td><td>10:01:26.311</td><td>+02:20:53.63</td><td>8</td><td>293</td></tr><tr><td>32</td><td>10:01:18.378</td><td>+02:15:27.60</td><td>8</td><td>293</td></tr><tr><td>33</td><td>10:01:34.510</td><td>+02:20:08.91</td><td>8</td><td>293</td></tr><tr><td>34</td><td>10:01:26.586</td><td>+02:14:42.80</td><td>8</td><td>293</td></tr><tr><td>35</td><td>10:01:42.700</td><td>+02:19:24.26</td><td>8</td><td>293</td></tr><tr><td>36</td><td>10:01:34.782</td><td>+02:13:58.06</td><td>8</td><td>293</td></tr><tr><td>37</td><td>10:01:50.906</td><td>+02:18:39.45</td><td>8</td><td>293</td></tr><tr><td>38</td><td>10:01:42.981</td><td>+02:13:13.34</td><td>8</td><td>293</td></tr><tr><td>39</td><td>09:59:07.488</td><td>+02:21:12.21</td><td>8</td><td>293</td></tr><tr><td>40</td><td>09:58:59.571</td><td>+02:15:46.13</td><td>8</td><td>293</td></tr><tr><td>41</td><td>09:59:15.687</td><td>+02:20:27.49</td><td>8</td><td>293</td></tr><tr><td>42</td><td>09:59:07.761</td><td>+02:15:01.45</td><td>8</td><td>293</td></tr><tr><td>43</td><td>09:59:23.878</td><td>+02:19:42.85</td><td>8</td><td>293</td></tr><tr><td>44</td><td>09:59:15.970</td><td>+02:14:16.65</td><td>8</td><td>293</td></tr><tr><td>45</td><td>09:59:32.079</td><td>+02:18:58.27</td><td>8</td><td>293</td></tr><tr><td>46</td><td>09:59:24.171</td><td>+02:13:31.91</td><td>8</td><td>293</td></tr></tbody></table>
<table><caption>**Table A1**
(Continued)</caption><thead><tr><th>Obs. No.</th><th>R.A.</th><th>Decl.</th><th>N<sub>exp</sub></th><th>P.A.<br>(deg)</th></tr></thead><tbody><tr><td>47</td><td>09:59:40.283</td><td>+02:18:13.35</td><td>8</td><td>293</td></tr><tr><td>48</td><td>09:59:32.367</td><td>+02:12:47.21</td><td>8</td><td>293</td></tr><tr><td>49</td><td>09:59:48.473</td><td>+02:17:28.70</td><td>8</td><td>293</td></tr><tr><td>50</td><td>09:59:40.557</td><td>+02:12:02.59</td><td>8</td><td>293</td></tr><tr><td>51</td><td>09:59:56.672</td><td>+02:16:43.99</td><td>8</td><td>293</td></tr><tr><td>52</td><td>09:59:48.106</td><td>+02:10:55.16</td><td>2</td><td>293</td></tr><tr><td>53</td><td>10:00:04.871</td><td>+02:15:59.26</td><td>8</td><td>293</td></tr><tr><td>54</td><td>09:59:56.961</td><td>+02:10:33.09</td><td>8</td><td>293</td></tr><tr><td>55</td><td>10:00:13.080</td><td>+02:15:14.46</td><td>8</td><td>293</td></tr><tr><td>56</td><td>10:00:05.161</td><td>+02:09:48.34</td><td>8</td><td>293</td></tr><tr><td>57</td><td>10:00:21.276</td><td>+02:14:29.73</td><td>8</td><td>293</td></tr><tr><td>58</td><td>10:00:13.350</td><td>+02:09:03.70</td><td>8</td><td>293</td></tr><tr><td>59</td><td>10:00:29.467</td><td>+02:13:45.08</td><td>8</td><td>293</td></tr><tr><td>60</td><td>10:00:21.555</td><td>+02:08:18.91</td><td>8</td><td>293</td></tr><tr><td>61</td><td>10:00:37.666</td><td>+02:13:00.36</td><td>8</td><td>293</td></tr><tr><td>62</td><td>10:00:29.097</td><td>+02:07:11.53</td><td>2</td><td>293</td></tr><tr><td>63</td><td>10:00:45.862</td><td>+02:12:15.63</td><td>8</td><td>293</td></tr><tr><td>64</td><td>10:00:37.951</td><td>+02:06:49.47</td><td>8</td><td>293</td></tr><tr><td>65</td><td>10:00:54.062</td><td>+02:11:30.91</td><td>8</td><td>293</td></tr><tr><td>66</td><td>10:00:46.138</td><td>+02:06:04.82</td><td>8</td><td>293</td></tr><tr><td>67</td><td>10:01:02.259</td><td>+02:10:46.20</td><td>8</td><td>293</td></tr><tr><td>69</td><td>10:01:10.468</td><td>+02:10:01.41</td><td>8</td><td>293</td></tr><tr><td>70</td><td>10:01:02.535</td><td>+02:04:35.37</td><td>8</td><td>293</td></tr><tr><td>71</td><td>10:01:18.013</td><td>+02:08:53.98</td><td>2</td><td>293</td></tr><tr><td>72</td><td>10:01:10.731</td><td>+02:03:50.64</td><td>8</td><td>293</td></tr><tr><td>74</td><td>10:01:18.939</td><td>+02:03:05.85</td><td>8</td><td>293</td></tr><tr><td>75</td><td>10:01:35.058</td><td>+02:07:47.25</td><td>8</td><td>293</td></tr><tr><td>76</td><td>10:01:27.134</td><td>+02:02:21.13</td><td>8</td><td>293</td></tr><tr><td>78</td><td>09:59:19.483</td><td>+02:17:44.78</td><td>8</td><td>107</td></tr><tr><td>79</td><td>09:59:35.599</td><td>+02:22:26.18</td><td>8</td><td>107</td></tr><tr><td>80</td><td>09:59:27.688</td><td>+02:16:59.98</td><td>8</td><td>107</td></tr><tr><td>81</td><td>09:59:43.793</td><td>+02:21:41.47</td><td>8</td><td>107</td></tr><tr><td>82</td><td>09:59:36.367</td><td>+02:16:38.92</td><td>2</td><td>107</td></tr><tr><td>83</td><td>09:59:52.165</td><td>+02:21:04.51</td><td>6</td><td>107</td></tr><tr><td>84</td><td>09:59:44.078</td><td>+02:15:30.64</td><td>8</td><td>107</td></tr><tr><td>85</td><td>10:00:00.165</td><td>+02:20:12.16</td><td>8</td><td>107</td></tr><tr><td>86</td><td>09:59:52.272</td><td>+02:14:45.90</td><td>8</td><td>107</td></tr><tr><td>87</td><td>10:00:08.397</td><td>+02:19:27.21</td><td>8</td><td>107</td></tr><tr><td>88</td><td>10:00:00.478</td><td>+02:14:01.11</td><td>8</td><td>107</td></tr><tr><td>89</td><td>10:00:16.589</td><td>+02:18:42.57</td><td>8</td><td>107</td></tr><tr><td>90</td><td>10:00:08.669</td><td>+02:13:16.45</td><td>8</td><td>107</td></tr><tr><td>91</td><td>10:00:24.782</td><td>+02:17:57.84</td><td>8</td><td>107</td></tr><tr><td>92</td><td>10:00:16.873</td><td>+02:12:31.66</td><td>8</td><td>107</td></tr><tr><td>93</td><td>10:00:32.981</td><td>+02:17:13.10</td><td>8</td><td>107</td></tr><tr><td>94</td><td>10:00:25.072</td><td>+02:11:46.93</td><td>8</td><td>107</td></tr><tr><td>95</td><td>10:00:41.177</td><td>+02:16:28.38</td><td>4</td><td>107</td></tr><tr><td>96</td><td>10:00:33.258</td><td>+02:11:02.27</td><td>8</td><td>107</td></tr><tr><td>97</td><td>10:00:49.385</td><td>+02:15:43.59</td><td>8</td><td>107</td></tr><tr><td>98</td><td>10:00:41.463</td><td>+02:10:17.49</td><td>8</td><td>107</td></tr><tr><td>99</td><td>10:00:57.572</td><td>+02:14:58.96</td><td>8</td><td>107</td></tr><tr><td>100</td><td>10:00:49.662</td><td>+02:09:32.76</td><td>8</td><td>107</td></tr><tr><td>101</td><td>10:01:05.777</td><td>+02:14:14.15</td><td>8</td><td>107</td></tr><tr><td>102</td><td>10:00:57.859</td><td>+02:08:48.04</td><td>8</td><td>107</td></tr><tr><td>103</td><td>10:01:13.967</td><td>+02:13:29.51</td><td>8</td><td>107</td></tr><tr><td>104</td><td>10:01:06.046</td><td>+02:08:03.39</td><td>8</td><td>107</td></tr><tr><td>105</td><td>10:01:22.166</td><td>+02:12:44.69</td><td>8</td><td>107</td></tr><tr><td>106</td><td>10:01:14.250</td><td>+02:07:18.60</td><td>8</td><td>107</td></tr><tr><td>107</td><td>10:01:30.362</td><td>+02:11:59.95</td><td>8</td><td>107</td></tr><tr><td>108</td><td>10:01:22.444</td><td>+02:06:33.89</td><td>8</td><td>107</td></tr><tr><td>109</td><td>10:01:38.567</td><td>+02:11:15.17</td><td>8</td><td>107</td></tr><tr><td>110</td><td>10:01:30.641</td><td>+02:05:49.15</td><td>8</td><td>107</td></tr><tr><td>111</td><td>10:01:46.759</td><td>+02:10:30.41</td><td>8</td><td>107</td></tr><tr><td>112</td><td>10:01:38.840</td><td>+02:05:04.32</td><td>8</td><td>107</td></tr></tbody></table>

<table><caption>**Table A1**
(Continued)</caption><thead><tr><th>Obs. No.</th><th>R.A.</th><th>Decl.</th><th>N<sub>exp</sub></th><th>P.A.<br>(deg)</th></tr></thead><tbody><tr><td>113</td><td>10:01:54.956</td><td>+02:09:45.84</td><td>8</td><td>107</td></tr><tr><td>114</td><td>10:01:45.821</td><td>+02:04:30.10</td><td>8</td><td>104</td></tr><tr><td>115</td><td>09:59:11.568</td><td>+02:12:18.67</td><td>8</td><td>107</td></tr><tr><td>116</td><td>09:59:03.652</td><td>+02:06:52.54</td><td>8</td><td>107</td></tr><tr><td>117</td><td>09:59:19.764</td><td>+02:11:33.97</td><td>8</td><td>107</td></tr><tr><td>118</td><td>09:59:11.866</td><td>+02:06:07.67</td><td>8</td><td>107</td></tr><tr><td>119</td><td>09:59:27.963</td><td>+02:10:49.24</td><td>8</td><td>107</td></tr><tr><td>120</td><td>09:59:20.057</td><td>+02:05:23.04</td><td>8</td><td>107</td></tr><tr><td>121</td><td>09:59:36.159</td><td>+02:10:04.51</td><td>8</td><td>107</td></tr><tr><td>122</td><td>09:59:28.250</td><td>+02:04:38.32</td><td>8</td><td>107</td></tr><tr><td>123</td><td>09:59:44.359</td><td>+02:09:19.77</td><td>8</td><td>107</td></tr><tr><td>124</td><td>09:59:36.451</td><td>+02:03:53.63</td><td>8</td><td>107</td></tr><tr><td>125</td><td>09:59:52.554</td><td>+02:08:35.07</td><td>8</td><td>107</td></tr><tr><td>126</td><td>09:59:44.648</td><td>+02:03:08.88</td><td>8</td><td>107</td></tr><tr><td>127</td><td>10:00:00.750</td><td>+02:07:50.36</td><td>8</td><td>107</td></tr><tr><td>128</td><td>09:59:52.843</td><td>+02:02:24.16</td><td>8</td><td>107</td></tr><tr><td>129</td><td>10:00:08.949</td><td>+02:07:05.64</td><td>8</td><td>107</td></tr><tr><td>130</td><td>10:00:01.042</td><td>+02:01:39.29</td><td>8</td><td>107</td></tr><tr><td>131</td><td>10:00:17.153</td><td>+02:06:20.84</td><td>8</td><td>107</td></tr><tr><td>132</td><td>10:00:09.227</td><td>+02:00:54.80</td><td>8</td><td>107</td></tr><tr><td>133</td><td>10:00:25.349</td><td>+02:05:36.10</td><td>8</td><td>107</td></tr><tr><td>134</td><td>10:00:17.430</td><td>+02:00:09.98</td><td>8</td><td>107</td></tr><tr><td>135</td><td>10:00:33.545</td><td>+02:04:51.37</td><td>8</td><td>107</td></tr><tr><td>136</td><td>10:00:25.631</td><td>+01:59:25.13</td><td>8</td><td>107</td></tr><tr><td>137</td><td>10:00:41.731</td><td>+02:04:06.74</td><td>8</td><td>107</td></tr><tr><td>138</td><td>10:00:33.825</td><td>+01:58:40.52</td><td>8</td><td>107</td></tr><tr><td>139</td><td>10:00:49.940</td><td>+02:03:21.93</td><td>8</td><td>107</td></tr><tr><td>140</td><td>10:00:42.021</td><td>+01:57:55.79</td><td>8</td><td>107</td></tr><tr><td>141</td><td>10:00:58.126</td><td>+02:02:37.31</td><td>8</td><td>107</td></tr><tr><td>142</td><td>10:00:50.209</td><td>+01:57:11.17</td><td>8</td><td>107</td></tr><tr><td>143</td><td>10:01:06.321</td><td>+02:01:52.56</td><td>8</td><td>107</td></tr><tr><td>144</td><td>10:00:58.401</td><td>+01:56:26.38</td><td>8</td><td>107</td></tr><tr><td>145</td><td>10:01:14.518</td><td>+02:01:07.84</td><td>8</td><td>107</td></tr><tr><td>146</td><td>10:01:05.656</td><td>+01:55:50.11</td><td>8</td><td>105</td></tr><tr><td>147</td><td>10:01:22.713</td><td>+02:00:23.13</td><td>8</td><td>107</td></tr><tr><td>148</td><td>10:01:14.792</td><td>+01:54:57.01</td><td>8</td><td>107</td></tr><tr><td>149</td><td>10:01:30.917</td><td>+01:59:38.26</td><td>8</td><td>107</td></tr><tr><td>150</td><td>10:01:22.988</td><td>+01:54:12.29</td><td>8</td><td>107</td></tr><tr><td>151</td><td>10:01:39.113</td><td>+01:58:53.59</td><td>8</td><td>107</td></tr><tr><td>152</td><td>10:01:31.183</td><td>+01:53:27.57</td><td>8</td><td>107</td></tr><tr><td colspan="5">Repeat visits</td></tr><tr><td>153</td><td>09:59:28.769</td><td>+02:22:57.38</td><td>8</td><td>110</td></tr><tr><td>154</td><td>09:59:36.367</td><td>+02:16:38.92</td><td>4</td><td>107</td></tr><tr><td>155</td><td>09:59:18.022</td><td>+02:07:46.70</td><td>4</td><td>289</td></tr><tr><td>156</td><td>10:00:07.665</td><td>+02:03:13.85</td><td>8</td><td>288</td></tr><tr><td>157</td><td>09:59:52.165</td><td>+02:21:04.51</td><td>4</td><td>107</td></tr><tr><td>158</td><td>09:59:51.153</td><td>+02:39:29.11</td><td>8</td><td>107</td></tr><tr><td>159</td><td>10:00:56.748</td><td>+02:33:31.31</td><td>4</td><td>107</td></tr><tr><td>160</td><td>10:01:12.869</td><td>+02:38:12.71</td><td>4</td><td>107</td></tr><tr><td>161</td><td>10:01:13.147</td><td>+02:32:01.88</td><td>8</td><td>107</td></tr><tr><td>162</td><td>10:00:24.513</td><td>+02:24:08.52</td><td>8</td><td>107</td></tr><tr><td>163</td><td>10:01:05.501</td><td>+02:20:24.92</td><td>8</td><td>107</td></tr><tr><td>164</td><td>10:01:30.092</td><td>+02:18:10.77</td><td>8</td><td>107</td></tr><tr><td>165</td><td>10:01:54.410</td><td>+02:22:07.43</td><td>8</td><td>107</td></tr><tr><td>166</td><td>10:02:02.605</td><td>+02:21:22.72</td><td>8</td><td>107</td></tr><tr><td>167</td><td>09:59:36.367</td><td>+02:16:38.92</td><td>4</td><td>107</td></tr></tbody></table>

Note. Visits #2, #68, #73, and #77 were fully skipped, hence no corresponding entries in this table. Repeat visits #154 and #167 have the same reference positions as visit #82, and repeat visit #167 has same reference position as visit #83 since these were overlapping with the corresponding original visit.
<figure><img src="image_12.png" /></figure>

**Figure A1.** Exposure map of completed MIRI visits from the COSMOS-Web program. Shown for reference are (a) the HST ACS F814W mosaic outline (maroon) of the COSMOS field (A. M. Koekemoer et al. 2007; N. Scoville et al. 2007), (b) the overall COSMOS-Web NIRCam mosaic outline as well as the tiles layout (blue; M. Franco et al. 2025), and (c) the PRIMER MIRI coverage (red; JWST GO PID 1837).

## ORCID iDs

Santosh Harish https://orcid.org/0000-0003-0129-2079

Jeyhan S. Kartaltepe https://orcid.org/0000-0001-9187-3605

Daizhong Liu https://orcid.org/0000-0001-9773-7479

Anton M. Koekemoer https://orcid.org/0000-0002-6610-2048

Caitlin M. Casey https://orcid.org/0000-0002-0930-6466

Maximilien Franco https://orcid.org/0000-0002-3560-8599

Hollis B. Akins https://orcid.org/0000-0003-3596-8794

Olivier Ilbert https://orcid.org/0000-0002-7303-4397

Marko Shuntov https://orcid.org/0000-0002-7087-0701

Nicole E. Drakos https://orcid.org/0000-0003-4761-2197

Mike Engesser https://orcid.org/0000-0003-0209-674X

Andreas L. Faisst https://orcid.org/0000-0002-9382-9832

Ghassem Gozaliasl https://orcid.org/0000-0002-0236-919X

Crystal L. Martin https://orcid.org/0000-0001-9189-7818

Michaela Hirschmann https://orcid.org/0000-0002-3301-3321

Vasily Kokorev https://orcid.org/0000-0002-5588-9156

Erini Lambrides https://orcid.org/0000-0003-3216-7190

Henry Joy McCracken https://orcid.org/0000-0002-9489-7765

Jed McKinney https://orcid.org/0000-0002-6149-8178

Louise Paquereau https://orcid.org/0000-0003-2397-0360

Jason Rhodes https://orcid.org/0000-0002-4485-8549

Brant E. Robertson https://orcid.org/0000-0002-4271-0364

## References

Akins, H. B., Casey, C. M., Allen, N., et al. 2023, ApJ, 956, 61

Akins, H. B., Casey, C. M., Lambrides, E., et al. 2025, ApJ, 991, 37

Alberts, S., Lyu, J., Shivaei, I., et al. 2024b, ApJ, 976, 224

Alberts, S., Williams, C. C., Helton, J. M., et al. 2024a, ApJ, 975, 85

Álvarez-Márquez, J., Crespo Gómez, A., Colina, L., et al. 2023, A&A, 671, A105

Arnouts, S., Moscardini, L., Vanzella, E., et al. 2002, MNRAS, 329, 355

Ashby, M. L. N., Stern, D., Brodwin, M., et al. 2009, ApJ, 701, 428

Ashby, M. L. N., Willner, S. P., Fazio, G. G., et al. 2013, ApJ, 769, 80

Astropy Collaboration, Price-Whelan, A. M., Lim, P. L., et al. 2022, ApJ, 935, 167

Astropy Collaboration, Price-Whelan, A. M., Sipőcz, B. M., et al. 2018, AJ, 156, 123

Astropy Collaboration, Robitaille, T. P., Tollerud, E. J., et al. 2013, A&A, 558, A33

Backhaus, B. E., Kirkpatrick, A., Yang, G., et al. 2025, arXiv:2503.19078

Bagley, M. B., Finkelstein, S. L., Koekemoer, A. M., et al. 2023, ApJL, 946, L12

Bertin, E. 2011, in ASP Conf. Ser. Astronomical Data Analysis Software and Systems XX, ed. I. N. Evans et al. (San Francisco, CA: ASP), 435

Bertin, E., & Arnouts, S. 1996, A&AS, 117, 393

Bertin, E., Schefer, M., Apostolakos, N., et al. 2020, in ASP Conf. Ser. 527, Astronomical Data Analysis Software and Systems XXIX, ed. R. Pizzo et al. (San Francisco, CA: ASP), 461

Bisigello, L., Gruppioni, C., Feltre, A., et al. 2021, A&A, 651, A52
Bradley, L., Sipocz, B., Robitaille, T., et al. 2023, astropy/photutils: v1.9.0, Zenodo,
doi:10.5281/zenodo.8248020

Bushouse, H., Eisenhamer, J., Dencheva, N., et al. 2023, JWST Calibration
Pipeline, v1.12.5, Zenodo, doi:10.5281/zenodo.10022973

Capak, P., Aussel, H., Ajiki, M., et al. 2007, ApJS, 172, 99

Casey, C. M., Kartaltepe, J. S., Drakos, N. E., et al. 2023, ApJ, 954, 31

Chiu, I., Desai, S., & Liu, J. 2016, A&C, 16, 79

Cole, S., Lacey, C. G., Baugh, C. M., & Frenk, C. S. 2000, MNRAS, 319, 168

Cowley, W. I., Baugh, C. M., Cole, S., Frenk, C. S., & Lacey, C. G. 2018,
MNRAS, 474, 2352

Daddi, E., Cimatti, A., Renzini, A., et al. 2004, ApJ, 617, 746

Dale, D. A., Helou, G., Magdis, G. E., et al. 2014, ApJ, 784, 83

Dicken, D., Marín, M. G., Shivaei, I., et al. 2024, A&A, 689, A5

Dickinson, M., Giavalisco, M. & GOODS Team 2001, in The Mass of Galaxies at Low and High Redshift: Proc. ESO and Universitäts-Sternwarte München Workshop, ed. R. Bender & A. Renzini (Berlin: Springer)

Driver, S. P., & Robotham, A. S. G. 2010, MNRAS, 407, 2131

Fazio, G. G., Hora, J. L., Allen, L. E., et al. 2004, ApJS, 154, 10

Finkelstein, S. L., Bagley, M., Song, M., et al. 2022, ApJ, 928, 52

Finkelstein, S. L., Bagley, M. B., Arrabal Haro, P., et al. 2025, ApJL, 983, L4

Franco, M., Casey, C. M., Koekemoer, A. M., et al. 2025, arXiv:2506.03256

Gaia Collaboration, Vallenari, A., Brown, A. G. A., et al. 2023, A&A, 674, A1

Gardner, J. P., Mather, J. C., Clampin, M., et al. 2006, SSRv, 123, 485

Genzel, R., Lutz, D., Sturm, E., et al. 1998, ApJ, 498, 579

Glasse, A., Rieke, G. H., Bauwens, E., et al. 2015, PASP, 127, 686

Graham, A. W., & Driver, S. P. 2005, PASA, 22, 118

Harris, C. R., Millman, K. J., van der Walt, S. J., et al. 2020, Natur, 585, 357

Hickox, R. C., & Alexander, D. M. 2018, ARA&A, 56, 625

Houck, J. R., Soifer, B. T., Weedman, D., et al. 2005, ApJL, 622, L105

Hunter, J. D. 2007, CSE, 9, 90

Ilbert, O., Arnouts, S., McCracken, H. J., et al. 2006, A&A, 457, 841

Kessler, M. F., Steinz, J. A., Anderegg, M. E., et al. 1996, A&A, 315, L27

Kirkpatrick, A., Pope, A., Alexander, D. M., et al. 2012, ApJ, 759, 139

Koekemoer, A. M., Aussel, H., Calzetti, D., et al. 2007, ApJS, 172, 196

Kokorev, V. I., Magdis, G. E., Davidzon, I., et al. 2021, ApJ, 921, 40

Labbé, I., González, V., Bouwens, R. J., et al. 2010, ApJL, 716, L103

Labbé, I., Oesch, P. A., Bouwens, R. J., et al. 2013, ApJL, 777, L19

Lacey, C. G., Baugh, C. M., Frenk, C. S., et al. 2016, MNRAS, 462, 3854

Lagos, C. d. P., Robotham, A. S. G., Trayford, J. W., et al. 2019, MNRAS,
489, 4196

Lagos, C. d. P., Tobar, R. J., Robotham, A. S. G., et al. 2018, MNRAS, 481, 3573

Laigle, C., McCracken, H. J., Ilbert, O., et al. 2016, ApJS, 224, 24

Le Floc'h, E., Papovich, C., Dole, H., et al. 2005, ApJ, 632, 169

Leger, A., & Puget, J. L. 1984, A&A, 137, L5

Libralato, M., Argyriou, I., Dicken, D., et al. 2024, PASP, 136, 034502

Ling, C.-T., Kim, S. J., Wu, C. K. W., et al. 2022, MNRAS, 517, 853

Lyu, J., Alberts, S., Rieke, G. H., et al. 2024, ApJ, 966, 229

Morrison, J. E., Dicken, D., Argyriou, I., et al. 2023, PASP, 135, 075004

Moster, B. P., Somerville, R. S., Newman, J. A., & Rix, H.-W. 2011, ApJ,
731, 113

Murakami, H., Baba, H., Barthel, P., et al. 2007, PASJ, 59, S369

Neugebauer, G., Habing, H. J., van Duinen, R., et al. 1984, ApJL, 278, L1

Oesch, P. A., Brammer, G., van Dokkum, P. G., et al. 2016, ApJ, 819, 129

Östlin, G., Pérez-González, P. G., Melinder, J., et al. 2025, A&A, 696, A57

Papovich, C., Cole, J. W., Yang, G., et al. 2023, ApJL, 949, L18

Papovich, C., Moustakas, L. A., Dickinson, M., et al. 2006, ApJ, 640, 92

Pérez-González, P. G., Barro, G., Rieke, G. H., et al. 2024a, ApJ, 968, 4

Pérez-González, P. G., Rinaldi, P., Caputi, K. I., et al. 2024b, ApJL, 969, L10

Planck Collaboration, Ade, P. A. R., Aghanim, N., et al. 2016, A&A, 594, A13

Planck Collaboration, Aghanim, N., Akrami, Y., et al. 2020, A&A, 641, A6

Rieke, G. H., Alberts, S., Shivaei, I., et al. 2024, ApJ, 975, 83

Rieke, M. J., Kelly, D., & Horner, S. 2005, Proc. SPIE, 5904, 1

Rieke, M. J., Kelly, D. M., Misselt, K., et al. 2023, PASP, 135, 028001

Rieke, G. H., Wright, G. S., Böker, T., et al. 2015, PASP, 127, 584

Rigby, J., Perrin, M., McElwain, M., et al. 2023, PASP, 135, 048001

Rinaldi, P., Caputi, K. I., Costantin, L., et al. 2023, ApJ, 952, 143

Ronayne, K., Papovich, C., Yang, G., et al. 2024, ApJ, 970, 61

Rowan-Robinson, M. 2001, ApJ, 549, 745

Rowan-Robinson, M. 2009, MNRAS, 394, 117

Rowan-Robinson, M. 2024, MNRAS, 527, 10254

Rowe, B. T. P., Jarvis, M., Mandelbaum, R., et al. 2015, A&C, 10, 121

Sajkov, L., Sajina, A., Pope, A., et al. 2024, ApJ, 977, 115

Salvato, M., Ilbert, O., Hasinger, G., et al. 2011, ApJ, 742, 61

Sanders, D. B., & Mirabel, I. F. 1996, ARA&A, 34, 749

Sanders, D. B., Salvato, M., Aussel, H., et al. 2007, ApJS, 172, 86

Scoville, N., Aussel, H., Brusa, M., et al. 2007, ApJS, 172, 1

Shuntov, M., Akins, H. B., Paquereau, L., et al. 2025, arXiv:2506.03243

Silva, L., Granato, G. L., Bressan, A., & Danese, L. 1998, ApJ, 509, 103

Somerville, R. S., Gilmore, R. C., Primack, J. R., & Domínguez, A. 2012,
MNRAS, 423, 1992

Stark, D. P., Ellis, R. S., Richard, J., et al. 2007, ApJ, 663, 10

Stone, M. A., Alberts, S., Rieke, G. H., et al. 2024, ApJ, 972, 62

Trayford, J. W., Camps, P., Theuns, T., et al. 2017, MNRAS, 470, 771

Trenti, M., & Stiavelli, M. 2008, ApJ, 676, 767

Virtanen, P., Gommers, R., Oliphant, T. E., et al. 2020, NatMe, 17, 261

Wang, B., de Graaff, A., Davies, R. L., et al. 2025b, ApJ, 984, 121

Wang, T., Sun, H., Zhou, L., et al. 2025a, ApJL, 988, L35

Weaver, J. R., Kauffmann, O. B., Ilbert, O., et al. 2022, ApJS, 258, 11

Werner, M. W., Roellig, T. L., Low, F. J., et al. 2004, ApJS, 154, 1

Wright, E. L., Eisenhardt, P. R. M., Mainzer, A. K., et al. 2010, AJ, 140, 1868

Wright, G. S., Rieke, G. H., Glasse, A., et al. 2023, PASP, 135, 048003

Wu, C. K. W., Ling, C.-T., Goto, T., et al. 2023, MNRAS, 523, 5187

Wu, H., Cao, C., Hao, C.-N., et al. 2005, ApJL, 632, L79

Yang, G., Papovich, C., Bagley, M. B., et al. 2023, ApJL, 956, L12