# The PHANGS-JWST Treasury Survey:
Star Formation, Feedback, and Dust Physics at High Angular resolution in
Nearby GalaxieS

Janice C. Lee Gemini Observatory/NSF’s NOIRLab, 950 N. Cherry Avenue, Tucson,
AZ, 85719, USA  Steward Observatory, University of Arizona, 933 N Cherry
Ave,Tucson, AZ 85721, USA  Karin M. Sandstrom Center for Astrophysics and
Space Sciences, Department of Physics, University of California,
San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA  Adam K. Leroy
Department of Astronomy, The Ohio State University, 140 West 18th Avenue,
Columbus, Ohio 43210, USA  David A. Thilker Department of Physics and
Astronomy, The Johns Hopkins University, Baltimore, MD 21218, USA  Eva
Schinnerer Max-Planck-Institut für Astronomie, Königstuhl 17, D-69117,
Heidelberg, Germany  Erik Rosolowsky Department of Physics, University of
Alberta, Edmonton, AB T6G 2E1, Canada  Kirsten L. Larson AURA for the European
Space Agency (ESA), Space Telescope Science Institute, 3700 San Martin Drive,
Baltimore, MD 21218, USA  Oleg V. Egorov Astronomisches Rechen-Institut,
Zentrum für Astronomie der Universität Heidelberg, Mönchhofstraße 12-14,
D-69120 Heidelberg, Germany  Sternberg Astronomical Institute, Lomonosov
Moscow State University, Universitetsky pr. 13, 119234 Moscow, Russia  Thomas
G. Williams Max-Planck-Institut für Astronomie, Königstuhl 17, D-69117,
Heidelberg, Germany  Judy Schmidt Astrophysics Source Code Librar y, Michigan
Technological University, 1400 Townsend Drive, Houghton, MI 49931  Eric
Emsellem European Southern Observatory, Karl-Schwarzschild Straße 2, D-85748
Garching bei München, Germany  Univ Lyon, Univ Lyon 1, ENS de Lyon, CNRS,
Centre de Recherche Astrophysique de Lyon UMR5574,
F-69230 Saint-Genis-Laval, France  Gagandeep S. Anand Space Telescope Science
Institute, 3700 San Martin Drive, Baltimore, MD 21218, USA  Ashley T. Barnes
Argelander-Institut für Astronomie, Universität Bonn, Auf dem Hügel 71, 53121
Bonn, Germany  Francesco Belfiore INAF – Osservatorio Astrofisico di Arcetri,
Largo E. Fermi 5, I-50157, Firenze, Italy  Ivana Bešlić Argelander-Institut
für Astronomie, Universität Bonn, Auf dem Hügel 71, 53121 Bonn, Germany  Frank
Bigiel Argelander-Institut für Astronomie, Universität Bonn, Auf dem Hügel 71,
53121 Bonn, Germany  Guillermo A. Blanc Observatories of the Carnegie
Institution for Science, 813 Santa Barbara Street, Pasadena, CA 91101, USA
Departamento de Astronomía, Universidad de Chile, Camino del Observatorio
1515, Las Condes, Santiago, Chile  Alberto D. Bolatto Department of Astronomy
and Joint Space-Science Institute, University of Maryland, College Park, MD
20742, USA  Médéric Boquien Centro de Astronomía (CITEVA), Universidad de
Antofagasta, Avenida Angamos 601, Antofagasta, Chile  Jakob den Brok
Argelander-Institut für Astronomie, Universität Bonn, Auf dem Hügel 71, 53121,
Bonn, Germany  Yixian Cao Max-Planck-Institut für Extraterrestrische Physik
(MPE), Giessenbachstr. 1, D-85748 Garching, Germany  Rupali Chandar University
of Toledo, 2801 W. Bancroft St., Mail Stop 111, Toledo, OH, 43606  Jérémy
Chastenet Sterrenkundig Observatorium, Universiteit Gent, Krijgslaan 281 S9,
B-9000 Gent, Belgium  Mélanie Chevance Universität Heidelberg, Zentrum für
Astronomie, Institut für Theoretische Astrophysik, Albert-Ueberle-Str 2,
D-69120 Heidelberg, Germany  Cosmic Origins Of Life (COOL) Research DAO,
coolresearch.io  I-Da Chiang (江宜達) Institute of Astronomy and Astrophysics,
Academia Sinica, No. 1, Sec. 4, Roosevelt Road, Taipei 10617, Taiwan  Enrico
Congiu Departamento de Astronomía, Universidad de Chile, Camino del
Observatorio 1515, Las Condes, Santiago, Chile  Daniel A. Dale Department of
Physics and Astronomy, University of Wyoming, Laramie, WY 82071, USA  Sinan
Deger California Institute of Technology, 1200 E. California Blvd., MC 249-17,
Pasadena, CA 91125, USA  The Oskar Klein Centre for Cosmoparticle Physics,
Department of Physics, Stockholm University, AlbaNova, Stockholm, SE-106 91,
Sweden  Cosima Eibensteiner Argelander-Institut für Astronomie, Universität
Bonn, Auf dem Hügel 71, 53121 Bonn, Germany  Christopher M. Faesi University
of Connecticut, Department of Physics, 196A Auditorium Road, Unit 3046,
Storrs, CT, 06269  Simon C. O. Glover Universität Heidelberg, Zentrum für
Astronomie, Institut für Theoretische Astrophysik, Albert-Ueberle-Str 2,
D-69120 Heidelberg, Germany  Kathryn Grasha Research School of Astronomy and
Astrophysics, Australian National University, Canberra, ACT 2611, Australia
Brent Groves International Centre for Radio Astronomy Research, University of
Western Australia, 35 Stirling Highway, Crawley, WA 6009, Australia  Research
School of Astronomy and Astrophysics, Australian National University,
Canberra, ACT 2611, Australia  Hamid Hassani Department of Physics, University
of Alberta, Edmonton, AB T6G 2E1, Canada  Kiana F. Henny Department of Physics
and Astronomy, University of Wyoming, Laramie, WY 82071, USA  Jonathan D.
Henshaw Astrophysics Research Institute, Liverpool John Moores University, 146
Brownlow Hill, Liverpool L3 5RF, UK  Max-Planck-Institut für Astronomie,
Königstuhl 17, D-69117, Heidelberg, Germany  Nils Hoyer Donostia International
Physics Center, Paseo Manuel de Lardizabal 4, E-20118 Donostia-San Sebastián,
Spain  Max-Planck-Institut für Astronomie, Königstuhl 17, D-69117, Heidelberg,
Germany  Universität Heidelberg, Seminarstrasse 2, D-69117 Heidelberg, Germany
Annie Hughes CNRS, IRAP, 9 Av. du Colonel Roche, BP 44346, F-31028 Toulouse
cedex 4, France  Université de Toulouse, UPS-OMP, IRAP, F-31028 Toulouse cedex
4, France  Sarah Jeffreson Center for Astrophysics $\mid$ Harvard &
Smithsonian , 60 Garden Street, Cambridge, MA 02138, USA  María J. Jiménez-
Donaire Observatorio Astronómico Nacional (IGN), C/Alfonso XII, 3, E-28014
Madrid, Spain  Centro de Desarrollos Tecnológicos, Observatorio de Yebes
(IGN), 19141 Yebes, Guadalajara, Spain  Jaeyeon Kim Universität Heidelberg,
Zentrum für Astronomie, Institut für Theoretische Astrophysik, Albert-Ueberle-
Str 2, D-69120 Heidelberg, Germany  Hwihyun Kim Gemini Observatory/NSF’s
NOIRLab, 950 N. Cherry Avenue, Tucson, AZ, USA  Ralf S. Klessen Universität
Heidelberg, Zentrum für Astronomie, Institut für Theoretische Astrophysik,
Albert-Ueberle-Str 2, D-69120 Heidelberg, Germany  Universität Heidelberg,
Interdisziplinäres Zentrum für Wissenschaftliches Rechnen, Im Neuenheimer Feld
205, D-69120 Heidelberg, Germany  Eric W. Koch Center for Astrophysics $\mid$
Harvard & Smithsonian , 60 Garden Street, Cambridge, MA 02138, USA  Kathryn
Kreckel Astronomisches Rechen-Institut, Zentrum für Astronomie der Universität
Heidelberg, Mönchhofstraße 12-14, D-69120 Heidelberg, Germany  J. M. Diederik
Kruijssen Cosmic Origins Of Life (COOL) Research DAO, coolresearch.io  Jing Li
Astronomisches Rechen-Institut, Zentrum für Astronomie der Universität
Heidelberg, Mönchhofstraße 12-14, 69120 Heidelberg, Germany  Daizhong Liu Max-
Planck-Institut für extraterrestrische Physik, Giessenbachstraße 1, D-85748
Garching, Germany  Laura A. Lopez Department of Astronomy, The Ohio State
University, 140 West 18th Avenue, Columbus, Ohio 43210, USA  Center for
Cosmology and Astroparticle Physics, 191 West Woodruff Avenue, Columbus, OH
43210, USA  Flatiron Institute, Center for Computational Astrophysics, NY
10010, USA  Daniel Maschmann Steward Observatory, University of Arizona, 933 N
Cherry Ave,Tucson, AZ 85721, USA  Sorbonne Université, LERMA, Observatoire de
Paris, PSL university, CNRS, F-75014, Paris, France  Ness Mayker Chen
Department of Astronomy, The Ohio State University, 140 West 18th Avenue,
Columbus, Ohio 43210, USA  Center for Cosmology and Astroparticle Physics, 191
West Woodruff Avenue, Columbus, OH 43210, USA  Sharon E. Meidt Sterrenkundig
Observatorium, Universiteit Gent, Krijgslaan 281 S9, B-9000 Gent, Belgium
Eric J. Murphy National Radio Astronomy Observatory, 520 Edgemont Road,
Charlottesville, VA 22903, USA  Justus Neumann Max-Planck-Institut für
Astronomie, Königstuhl 17, D-69117, Heidelberg, Germany  Nadine Neumayer Max-
Planck-Institut für Astronomie, Königstuhl 17, D-69117, Heidelberg, Germany
Hsi-An Pan Department of Physics, Tamkang University, No.151, Yingzhuan Road,
Tamsui District, New Taipei City 251301, Taiwan  Ismael Pessa Max-Planck-
Institut für Astronomie, Königstuhl 17, D-69117, Heidelberg, Germany  Leibniz-
Institut für Astrophysik Potsdam (AIP), An der Sternwarte 16, 14482 Potsdam,
Germany  Jérôme Pety Institut de Radioastronomie Millimétrique (IRAM), 300 Rue
de la Piscine, F-38406 Saint Martin d’Hères, France  Observatoire de Paris,
PSL Research University, CNRS, Sorbonne Universités, 75014 Paris  Miguel
Querejeta Observatorio Astronómico Nacional (IGN), C/Alfonso XII, 3, E-28014
Madrid, Spain  Francesca Pinna Max-Planck-Institut für Astronomie, Königstuhl
17, D-69117, Heidelberg, Germany  M. Jimena Rodríguez Steward Observatory,
University of Arizona, 933 N Cherry Ave,Tucson, AZ 85721, USA  Instituto de
Astrofísica de La Plata, CONICET–UNLP, Paseo del Bosque S/N, B1900FWA La
Plata, Argentina  Toshiki Saito National Astronomical Observatory of Japan,
2-21-1 Osawa, Mitaka, Tokyo, 181-8588, Japan  Patricia Sánchez-Blázquez
Departamento de Física de la Tierra y Astrofísica, Universidad Complutense de
Madrid, E-28040 Madrid, Spain  Instituto de Física de Partículas y del Cosmos,
Universidad Complutense de Madrid, E-28040 Madrid, Spain  Francesco Santoro
Max-Planck-Institut für Astronomie, Königstuhl 17, D-69117, Heidelberg,
Germany  Amy Sardone Department of Astronomy, The Ohio State University, 140
West 18th Avenue, Columbus, Ohio 43210, USA  Center for Cosmology and
AstroParticle Physics, 191 West Woodruff Avenue, Columbus, OH 43210, USA
Rowan J. Smith Jodrell Bank center for Astrophysics, Department of Physics and
Astronomy, University of Manchester, Oxford Road, Manchester M13 9PL, UK
Mattia C. Sormani Universität Heidelberg, Zentrum für Astronomie, Institut für
Theoretische Astrophysik, Albert-Ueberle-Str 2, D-69120 Heidelberg, Germany
Fabian Scheuermann Astronomisches Rechen-Institut, Zentrum für Astronomie der
Universität Heidelberg, Mönchhofstraße 12-14, D-69120 Heidelberg, Germany
Sophia K. Stuber Max-Planck-Institut für Astronomie, Königstuhl 17, D-69117,
Heidelberg, Germany  Jessica Sutter Center for Astrophysics & Space Sciences,
Department of Physics, University of California, San Diego, 9500 Gilman Drive,
San Diego, CA 92093, USA  Jiayi Sun (孙嘉懿) Department of Physics and Astronomy,
McMaster University, 1280 Main Street West, Hamilton, ON L8S 4M1, Canada
Canadian Institute for Theoretical Astrophysics (CITA), University of Toronto,
60 St George Street, Toronto, ON M5S 3H8, Canada  Yu-Hsuan Teng Center for
Astrophysics and Space Sciences, Department of Physics, University of
California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA  Robin G.
Treß Institute of Physics, Laboratory for galaxy evolution and spectral
modelling, EPFL, Observatoire de Sauverny, Chemin Pegais 51, 1290 Versoix,
Switzerland.  Antonio Usero Observatorio Astronómico Nacional (IGN), C/Alfonso
XII, 3, E-28014 Madrid, Spain  Elizabeth J. Watkins Astronomisches Rechen-
Institut, Zentrum für Astronomie der Universität Heidelberg, Mönchhofstraße
12-14, D-69120 Heidelberg, Germany  Bradley C. Whitmore Space Telescope
Science Institute, 3700 San Martin Drive, Baltimore, MD 21218, USA  Alessandro
Razza Departamento de Astronomía, Universidad de Chile, Camino del
Observatorio 1515, Las Condes, Santiago, Chile

(Received 03-Dec-2022; Accepted XXX)

###### Abstract

The PHANGS collaboration has been building a reference dataset for the multi-
scale, multi-phase study of star formation and the interstellar medium in
nearby galaxies. With the successful launch and commissioning of JWST, we can
now obtain high-resolution infrared imaging to probe the youngest stellar
populations and dust emission on the scales of star clusters and molecular
clouds ($\sim$5-50 pc). In Cycle 1, PHANGS is conducting an 8-band imaging
survey from 2-21$\mu$m of 19 nearby spiral galaxies. CO(2–1) mapping, optical
integral field spectroscopy, and UV-optical imaging for all 19 galaxies have
been obtained through large programs with ALMA, VLT/MUSE, and Hubble. PHANGS-
JWST enables a full inventory of star formation, accurate measurement of the
mass and age of star clusters, identification of the youngest embedded stellar
populations, and characterization of the physical state of small dust grains.
When combined with Hubble catalogs of $\sim$10,000 star clusters, MUSE
spectroscopic mapping of $\sim$20,000 HII regions, and $\sim$12,000 ALMA-
identified molecular clouds, it becomes possible to measure the timescales and
efficiencies of the earliest phases of star formation and feedback, build an
empirical model of the dependence of small dust grain properties on local ISM
conditions, and test our understanding of how dust-reprocessed starlight
traces star formation activity, all across a diversity of galactic
environments. Here we describe the PHANGS-JWST Treasury survey, present the
remarkable imaging obtained in the first few months of science operations, and
provide context for the initial results presented in the first series of
PHANGS-JWST publications.

star formation — interstellar medium — star clusters — spiral galaxies —
surveys

††journal: ApJL††software: Python associated: Astropy (and its affiliated
packages) (Astropy Collaboration et al., 2018, 2022). SAOImage DS9
(Smithsonian Astrophysical Observatory, 2000; Joye & Mandel, 2003). CIGALE
(Boquien et al., 2019b).

zz

##  1 Introduction

The discovery of infrared emission in the Orion Nebula revealed that the
earliest phases of star formation occur in the densest, dust-enshrouded cores
of molecular clouds which are not observable in the optical (Becklin &
Neugebauer, 1967; Kleinmann & Low, 1967). Beginning with IRAS in the 1980s
(Neugebauer et al., 1984), a series of cryogenic infrared space missions
including ISO, Spitzer, and Herschel (Kessler et al., 2003; Werner et al.,
2004; Pilbratt et al., 2010) mapped emission from star formation and dust with
increasing resolution and coverage across the mid- and far-infrared (e.g.
Soifer et al., 1987, 2008; Kennicutt & Evans, 2012, and references therein).
These missions demonstrated that infrared observations are a requisite
component for understanding the process of star formation in galaxies and the
physics of the interstellar medium. With the transformative infrared
capabilities of JWST, studies of star formation and dust in galaxies are again
entering a new era. New breakthroughs in this field will rely on JWST together
with the collective effort of the astronomy community to conduct observations
across the electromagnetic spectrum which capture all major stages of the star
formation cycle, from gas to stars.

For nearby galaxies, JWST is finally extending the high spatial resolution
infrared studies of star formation and dust previously only possible in the
Milky Way and Local Group to a more representative sample of galaxies within a
few tens of Mpc. Here, we introduce the PHANGS-JWST Cycle 1 Treasury survey,
which was designed to use JWST’s order-of-magnitude improvement in sensitivity
and angular resolution to map the infrared emission from the youngest stellar
populations and dusty interstellar medium across the disks of galaxies on
scales of tens of parsecs. These are the physical scales where gas
fragmentation is expected (e.g. due to the Jeans instability), and where the
key physical processes that drive, regulate, and extinguish star formation can
be investigated for individual star clusters ($\sim$5 pc), HII regions
($\sim$10 pc), and molecular clouds ($\sim$50 pc).

Imaging in 8-bands from 2-21$\mu$m is being obtained of 19 nearby (d$<$20 Mpc)
spiral galaxies. All have UV-optical imaging from HST (Lee et al., 2022),
optical integral field spectroscopy from VLT/MUSE (Emsellem et al., 2022), and
CO(2–1) mapping from ALMA (Leroy et al., 2021), through survey programs led by
the PHANGS collaboration (Schinnerer et al., 2019a). All data are publicly
available from the respective observatory’s archives, and HST catalogs of
$\gtrsim 12{,}000$ star clusters and associations (Thilker et al., 2022; Deger
et al., 2022; Whitmore et al., 2021; Larson et al., 2022), MUSE catalogs of
$\gtrsim 20{,}000$ H II regions (Santoro et al., 2022; Groves et al., subm.,
Congiu et al., subm.), and ALMA catalogs of $\gtrsim 10{,}000$ molecular
clouds (Sun et al., 2018; Rosolowsky et al., 2021; Sun et al., 2022) have been
produced.111www.phangs.org

The PHANGS-JWST sample covers the range of star formation, gas, dust,
structural, and dynamical properties found in present-day massive galaxies on
the star-forming main sequence, enabling studies that connect small-scale gas
and star formation physics to the broader context of galactic structure and
galaxy evolution. The combined PHANGS multiwavelength observational programs
yield the first dataset that accesses all key stages of the star formation
cycle at the resolution of individual star-forming regions across a sample
that reflects the galactic environments where the majority of star formation
occurs at $z=0$ (e.g. Brinchmann et al., 2004; Salim et al., 2007; Saintonge
et al., 2017).

PHANGS targets were among the first to be observed soon after after the
successful completion of JWST commissioning in June 2022. Within the first two
months of science operations, data for four galaxies (NGC 7496, IC 5332, NGC
628, NGC 1365) were obtained. As a Treasury program, the PHANGS-JWST imaging
data have no exclusive access period. The release of the data, shortly after
observation, revealed the architecture of the dusty ISM in exquisite detail,
capturing the attention of not only the science community, but also of the
general public and press.

Analysis by the PHANGS collaboration is underway, and focuses on the following
science goals:

  1. 1.

characterization of the youngest embedded stellar populations that are
inaccessible at optical-UV wavelengths, completion of the inventory of newborn
stars and definitive measurement of star cluster mass functions and formation
efficiencies, to probe the early evolution of stars and star cluster
populations;

  2. 2.

characterization of ISM bubble and shell features, and measurement of the
duration of dust embedded star formation and star formation efficiencies to
identify the sources and timescales of star formation feedback;

  3. 3.

determination of how local interstellar conditions influence the properties,
evolution, and processing of the smallest dust grains, in particular the
polycyclic aromatic hydrocarbons (PAHs), to enable a physically robust
interpretation of dust emission in nearby galaxies;

  4. 4.

development of new dust-based high resolution tracers of the neutral gas that
are complementary to HI, CO, CI, and C+ mapping, to resolve the multi-scale
filamentary structure of the ISM;

  5. 5.

establishment and calibration of robust mid-IR diagnostics of star formation
activity that account for the youngest stars, to compute star formation rates
from cloud- to galaxy scales.

This paper is intended to provide a general reference for the parameters of
the survey, as well as an introduction for the papers in this Issue, which
showcases some of the science possible based on PHANGS-JWST data for the first
four galaxies observed. The remaining sections are organized as follows. In
Section 2, we describe the galaxy sample and provide a brief summary of the
supporting multi-wavelength PHANGS datasets. Our observing strategy with
NIRCam and MIRI is presented in Section 3. Next in Section 4, we describe our
early data reduction efforts to enable first science, focusing on
modifications made to the standard STScI JWST Science Calibration Pipeline,
and note some limitations for the use of these early data. In Section 5, we
present the imaging for NGC 7496, IC 5332, NGC 628, and NGC 1365. In Section
6, we describe the scientific goals that motivate the PHANGS-JWST survey, and
highlight results presented in this Issue. In Section 7, we describe the image
products that will be released by the PHANGS team, and provide examples of
additional JWST high level science products which would be valuable to be
developed from the ensemble of panchromatic PHANGS datasets. We conclude with
a short summary in Section 7.

##  2 Galaxy Sample

The PHANGS-JWST Treasury Survey targets the 19 PHANGS galaxies that have the
full complement of UV-optical imaging from PHANGS–HST (Lee et al., 2022),
optical spectral mapping from PHANGS–MUSE (Emsellem et al., 2022), and
millimeter-wave spectral mapping from PHANGS–ALMA (Leroy et al., 2021). In
addition to these major survey datasets from JWST, HST, VLT-MUSE, and ALMA, a
wealth of additional supporting data from the PHANGS collaboration has been
and continues to be obtained, e.g., Astrosat far-ultraviolet/near-ultraviolet
imaging (PI: E. Rosolowsky), HST H$\alpha$ narrowband imaging (PI: R.
Chandar), ground-based wide-field H$\alpha$ narrowband imaging (PI: G. Blanc,
I-T. Ho), and H I 21-cm observations from team programs with the VLA and
MeerKAT. A complete listing of PHANGS observations and data products is
provided at www.phangs.org/data.

Basic properties of the galaxies relevant to their selection are given in
Tables 1 and 2, and Figure 1 shows MUSE+ALMA composite images for the full
sample.

Over the distance range of these galaxies (5-20 Mpc; Table 1), JWST’s point
spread function FWHM subtends 2-6 pc at 2$\mu$m, and 17-65 pc at 21$\mu$m. For
the first time, JWST allows for mid-infrared imaging of individual star
clusters, HII regions, and molecular clouds across the nearby galaxy
population, which are well-matched to observations of these populations from
HST, MUSE, and ALMA. The combination of datasets from these four facilities is
essential for achieving our specific science goals (§6), and the PHANGS
program currently provides the largest uniform collection of such data for
galaxies in the local volume.

Figure 1: Composite $gri$+H$\alpha$+CO images for the 19 galaxies in the PHANGS-JWST Treasury program. The $gri$+H$\alpha$ images are constructed from VLT MUSE full-field spectral mapping (Emsellem et al., 2022), with H$\alpha$ line emission in red, and combined with ALMA CO(2–1) (Leroy et al., 2021) maps, with CO(2–1) flux in blue. JWST NIRCam and MIRI footprints have been defined to overlap existing MUSE, ALMA, and HST data. Footprints shown with dashed lines represent observations that have not been executed at time of writing (October 2022), and may rotate slightly due to the range of allowed orient angles specified for the target. The JWST sample spans a factor of $\sim 50$ in stellar mass and SFR, and a factor of $\sim 100$ in CO surface density (Table 2). It includes galaxies with prominent dust lanes, a full range of stellar bar, bulge, spiral arm morphologies, as well as nuclear starbursts and AGN activity. Table 1: PHANGS-JWST Target Galaxy Distance, Orientation, and Morphology Galaxy | $\alpha$ | $\delta$ |  $D$aaCompilation of galaxy distances ($D$) and uncertainties ($\sigma_{D}$). Distance methods include TRGB (Tip of the Red Giant Branch), Group, NAM (Numerical action model), PNLF (Planetary Nebula Luminosity Function), SCM (standardizable candle method), Cepheid (Cepheid period-luminosity relation). Superscripts indicate the primary reference given in the compilation by Anand et al. (2021a), and are as follows: 1) Anand et al. (2021b), 2) Kourkchi & Tully (2017), 3) Shaya et al. (2017), 4) Kourkchi et al. (2020), 5) Scheuermann et al. (2022), 6) Nugent et al. (2006), and 7) Freedman et al. (2001). |  $\sigma_{D}$aaCompilation of galaxy distances ($D$) and uncertainties ($\sigma_{D}$). Distance methods include TRGB (Tip of the Red Giant Branch), Group, NAM (Numerical action model), PNLF (Planetary Nebula Luminosity Function), SCM (standardizable candle method), Cepheid (Cepheid period-luminosity relation). Superscripts indicate the primary reference given in the compilation by Anand et al. (2021a), and are as follows: 1) Anand et al. (2021b), 2) Kourkchi & Tully (2017), 3) Shaya et al. (2017), 4) Kourkchi et al. (2020), 5) Scheuermann et al. (2022), 6) Nugent et al. (2006), and 7) Freedman et al. (2001). | MethodaaCompilation of galaxy distances ($D$) and uncertainties ($\sigma_{D}$). Distance methods include TRGB (Tip of the Red Giant Branch), Group, NAM (Numerical action model), PNLF (Planetary Nebula Luminosity Function), SCM (standardizable candle method), Cepheid (Cepheid period-luminosity relation). Superscripts indicate the primary reference given in the compilation by Anand et al. (2021a), and are as follows: 1) Anand et al. (2021b), 2) Kourkchi & Tully (2017), 3) Shaya et al. (2017), 4) Kourkchi et al. (2020), 5) Scheuermann et al. (2022), 6) Nugent et al. (2006), and 7) Freedman et al. (2001). |  $i$bbGalaxy inclination and position angle adopted from Lang et al. (2020) where available and Leroy et al. (2021) otherwise. |  $PA$bbGalaxy inclination and position angle adopted from Lang et al. (2020) where available and Leroy et al. (2021) otherwise. | Tcc Morphological T-type from HyperLEDA (Makarov et al., 2014). Morphological classification from Buta et al. (2015) except for NGC 2835, which comes from de Vaucouleurs et al. (1991). | Morphologycc Morphological T-type from HyperLEDA (Makarov et al., 2014). Morphological classification from Buta et al. (2015) except for NGC 2835, which comes from de Vaucouleurs et al. (1991).
---|---|---|---|---|---|---|---|---|---
| (J2000) | (J2000) | (Mpc) | (Mpc) |  | (deg) | (deg) |  |
NGC 0628**One of first four galaxies observed which form the basis of the PHANGS-JWST First Results presented in this volume. | 01h36m41.75s | +15d47m01.2s | 9.84 | 0.63 | TRGB1 | 9 | 21 | 5.2 | SA(s)c
NGC 1087 | 02h46m25.16s | -00d29m55.1s | 15.85 | 2.24 | Group2 | 43 | 359 | 5.2 | SB(rs)cd pec
NGC 1300 | 03h19m41.08s | -19d24m40.9s | 18.99 | 2.85 | NAM3,4 | 32 | 278 | 4 | (R’)SB(s,bl,nrl)b
NGC 1365**One of first four galaxies observed which form the basis of the PHANGS-JWST First Results presented in this volume. | 03h33m36.37s | -36d08m25.4s | 19.57 | 0.78 | TRGB1 | 55 | 201 | 3.2 | (R’)SB(rs,nr)bc
NGC 1385 | 03h37m28.85s | -24d30m01.1s | 17.22 | 2.58 | NAM3,4 | 44 | 181 | 5.9 | SB(s)dm pec
NGC 1433 | 03h42m01.55s | -47d13m19.5s | 18.63${\dagger}$${\dagger}$The PNLF distances to NGC 1433 and 1512 varied from Anand et al. (2021a), which was used in Emsellem et al. (2022) and Leroy et al. (2021), and Scheuermann et al. (2022). | 1.86 | PNLF5 | 29 | 200 | 1.5 | (R_1_’)SB(r,p,nrl,nb)a
NGC 1512 | 04h03m54.28s | -43d20m55.9s | 18.83${\dagger}$${\dagger}$The PNLF distances to NGC 1433 and 1512 varied from Anand et al. (2021a), which was used in Emsellem et al. (2022) and Leroy et al. (2021), and Scheuermann et al. (2022). | 1.88 | PNLF5 | 43 | 262 | 1.2 | (RL)SB(r,bl,nr)a
NGC 1566 | 04h20m00.42s | -54d56m16.1s | 17.69 | 2.00 | Group2 | 30 | 215 | 4 | (R_1_’)SAB(rs,rs)b
NGC 1672 | 04h45m42.50s | -59d14m49.9s | 19.40 | 2.91 | NAM3,4 | 43 | 134 | 3.3 | (R’)SAB(rs,nr)b
NGC 2835 | 09h17m52.91s | -22d21m16.8s | 12.22 | 0.94 | TRGB1 | 41 | 1 | 5 | SB(rs)c
NGC 3351 | 10h43m57.70s | +11d42m13.7s | 9.96 | 0.33 | TRGB1 | 45 | 193 | 3.1 | (R’)SB(r,bl,nr)a
NGC 3627 | 11h20m14.96s | +12d59m29.5s | 11.32 | 0.48 | TRGB1 | 57 | 173 | 3.1 | SB_x_(s)b pec
NGC 4254 | 12h18m49.60s | +14d24m59.4s | 13.1 | 2.8 | SCM6 | 34 | 68 | 5.2 | SA(s)c pec
NGC 4303 | 12h21m54.90s | +04d28m25.1s | 16.99 | 3.04 | Group2 | 24 | 312 | 4 | SAB(rs,nl)bc
NGC 4321 | 12h22m54.83s | +15d49m18.5s | 15.21 | 0.49 | Cepheid7 | 39 | 156 | 4 | SAB(rs,nr,nb)bc
NGC 4535 | 12h34m20.31s | +08d11m51.9s | 15.77 | 0.37 | Cepheid7 | 45 | 180 | 5 | SAB(s)c
NGC 5068 | 13h18m54.81s | -21d02m20.8s | 5.20 | 0.21 | TRGB1 | 36 | 342 | 6 | SB(s)d
NGC 7496**One of first four galaxies observed which form the basis of the PHANGS-JWST First Results presented in this volume. | 23h09m47.29s | -43d25m40.6s | 18.72 | 2.81 | NAM3,4 | 36 | 194 | 3.2 | (R’)SB_x_(rs)b
IC 5332**One of first four galaxies observed which form the basis of the PHANGS-JWST First Results presented in this volume. | 23h34m27.49s | -36d06m03.9s | 9.01 | 0.41 | TRGB1 | 27 | 74 | 6.8 | SAB(s)cd
Table 2: PHANGS-JWST Target Galaxy Physical Properties

Galaxy | log M⋆aaGalaxy stellar mass. Following Leroy et al. (2021), based on Spitzer IRAC 3.6 $\mu$m when available, or WISE 3.4 $\mu$m, and mass-to-light ratio prescription of Leroy et al. (2019) calculated as a function of radius in the galaxy. | SFRtotbbThe empirical $1\sigma$ surface brightness sensitivity $\sigma_{I}$ estimated directly from the image data (Section 5.2). This estimate agrees well with the pipeline-provided noise products. |  $R_{e}$ccStellar mass effective radius, from Leroy et al. (2021) and closely resembling near-IR effective radius (Muñoz-Mateos et al., 2015). |  $\log_{10}L_{\rm CO}$ddSFRtot is the total galaxy star formation rate. Based on GALEX FUV and WISE W4 imaging with SFR prescription calibrated to match results from population synthesis modeling of Salim et al. (2016, 2018) as in Leroy et al. (2021). |  $M_{\rm HI}$eeAtomic gas mass, not including helium, from HyperLEDA (Makarov et al., 2014). |  $12+\log_{10}{\rm O/H}$ffGas phase metallicity on the $S$-cal system (Pilyugin & Grebel, 2016) estimated at $R_{e}$ by Groves et al. (subm.). |  $f_{\rm JWST}$ggFraction of the SFR estimated from the UV+IR that lies within the PHANGS–HST field of view from Lee et al. (2022). This can be applied with reasonable precision to aperture correct the SFR, $L_{\rm CO}$, or $M_{\star}$ to estimate the quantity inside the JWST field of view.
---|---|---|---|---|---|---|---
| ($M_{\odot}$) | ($M_{\odot}$ yr-1) | (kpc) | (K km s-1 pc2) | ($M_{\odot}$) |  |
NGC 0628**One of first four galaxies observed which form the basis of the PHANGS-JWST First Results presented in this volume. | 10.3 | 1.7 | 3.9 | 8.4 | 9.7 | 8.53 | 0.53
NGC 1087 | 9.9 | 1.3 | 3.2 | 8.3 | 9.1 | 8.48 | 0.88
NGC 1300 | 10.6 | 1.2 | 6.5 | 8.5 | 9.4 | 8.62 | 0.87
NGC 1365**One of first four galaxies observed which form the basis of the PHANGS-JWST First Results presented in this volume. | 11.0 | 17 | 2.8 | 9.5 | 9.9 | 8.67 | 0.76
NGC 1385 | 10.0 | 2.1 | 3.4 | 8.4 | 9.2 | 8.46 | 0.94
NGC 1433 | 10.9 | 1.1 | 4.3 | 8.5 | 9.4 | 8.57 | 0.50
NGC 1512 | 10.7 | 1.3 | 4.8 | 8.3 | 9.9 | 8.58 | 0.46
NGC 1566 | 10.8 | 4.6 | 3.2 | 8.9 | 9.8 | 8.61 | 0.70
NGC 1672 | 10.7 | 7.6 | 3.4 | 9.1 | 10.2 | 8.57 | 0.87
NGC 2835 | 10.0 | 1.3 | 3.3 | 7.7 | 9.5 | 8.56 | 0.46
NGC 3351 | 10.4 | 1.3 | 3.0 | 8.1 | 8.9 | 8.58 | 0.66
NGC 3627 | 10.8 | 3.9 | 3.6 | 9.0 | 9.8 | 8.54 | 0.86
NGC 4254 | 10.4 | 3.1 | 2.4 | 8.9 | 9.5 | 8.59 | 0.90
NGC 4303 | 10.5 | 5.4 | 3.4 | 9.0 | 9.7 | 8.58 | 0.80
NGC 4321 | 10.8 | 3.5 | 5.5 | 9.0 | 9.4 | 8.56 | 0.68
NGC 4535 | 10.5 | 2.2 | 6.3 | 8.6 | 9.6 | 8.54 | 0.57
NGC 5068 | 9.4 | 0.3 | 2.0 | 7.3 | 8.8 | 8.32 | 0.71
NGC 7496**One of first four galaxies observed which form the basis of the PHANGS-JWST First Results presented in this volume. | 10.0 | 2.2 | 3.8 | 8.3 | 9.1 | 8.51 | 0.88
IC 5332**One of first four galaxies observed which form the basis of the PHANGS-JWST First Results presented in this volume. | 9.7 | 0.4 | 3.6 | 7.1 | 9.3 | 8.30 | 0.27

ddfootnotetext: Integrated CO (2-1) luminosity. Scale by $\alpha_{\rm
CO}\approx 6.7$ M⊙ pc-2 (K km s-1)-1 to estimate $M_{\rm mol}$ including
helium and metals using a fixed CO-to-H2 conversion factor.

The PHANGS surveys focus on nearby, star-forming, relatively face-on spiral
galaxies. The parent sample (N=90), described in Leroy et al. (2021), aimed to
select galaxies visible from the southern hemisphere (i.e., observable by ALMA
and the VLT) that have inclination less that $\sim 75^{\circ}$, distances less
than $\sim 17$ Mpc, stellar masses $\gtrsim 5\times 10^{9}$ M⊙, and specific
star formation rates, SFR/$M_{\star}$, above $\sim 10^{-11}$ yr-1. The
inclination criterion minimizes source blending within the galaxies and line-
of-sight dust attenuation. The distance requirement ensures that the galaxies
are close enough that HST and JWST can resolve individual clusters and
associations, MUSE can identify and characterize individual Hii regions, and
ALMA resolves the molecular ISM into individual massive molecular clouds and
complexes. Meanwhile the mass and specific star formation rate criteria select
galaxies on the “main sequence” of star-forming galaxies, ensuring that the
surveys capture environments that reflect where most stars form at $z=0$
(e.g., Brinchmann et al., 2004; Salim et al., 2007; Saintonge et al., 2017).

As typical for surveys of nearby galaxies, our best estimates of the
properties of galaxies in the sample have evolved since they were first
selected, in particular due to the continuing improvement in distance
estimates (Anand et al., 2021a; Lee et al., 2022). As a result, the properties
of parent sample have broadened slightly beyond the original selection
criteria, with the result that the PHANGS-JWST sample includes galaxies with
distances out to $\sim 20$ Mpc and stellar masses as low as $\sim 2.5\times
10^{9}$ M⊙.

In practice, the requirement that PHANGS-JWST galaxies have the full
complement of PHANGS ALMA, MUSE, and HST data means that the PHANGS-JWST
selection will be identical to that of the precursor survey with the smallest
sample, which is PHANGS-MUSE (Emsellem et al., 2022). PHANGS-MUSE targeted the
first 19 galaxies from the PHANGS parent sample for which ALMA CO(2–1) mapping
was obtained. The resulting sample satisfies the general goal of providing a
reasonably representative selection of massive star-forming galaxies –
spanning a factor of $\sim 50$ in stellar mass and SFR (Figure 3 in Emsellem
et al., 2022); morphological types from Sa-Sd with a diversity of bar and ring
structures; and a factor of $\sim 100$ in CO surface densities (Figure 1 in
Lee et al., 2022).

##  3 JWST Observations

Figure 2: top: Model SEDs of dust-enshrouded young star clusters with filter
coverage of the PHANGS-HST and JWST Treasury programs. Models are of a solar
metallicity $10^{4}$ M⊙ young star cluster at $d=10$ Mpc with varying ages and
levels of extinction generated with CIGALE (Boquien et al., 2019a; Turner et
al., 2021). Colored bands show the 8 JWST NIRCAM and MIRI filters, spanning
from 2$-21\mu$m, selected to probe stellar photospheric emission, PAH
features, and the dust continuum. The 5 UV-visible filters used by PHANGS-HST
(Lee et al., 2022) appear in grey. bottom: Normalized throughput for each of
the eight filters used in the PHANGS-JWST survey. For comparison, the four
Spitzer IRAC filters and MIPS 24 $\micron$ filter are also shown. A model dust
SED is overplotted to illustrated the features probed by each filter. The
three PAH features targeted by our survey are marked.

PHANGS-JWST observations for our sample of 19 galaxies are obtained under JWST
program 2107 (PI: J. Lee) with a total allocation of 112.6 hrs.222Accounting
for changes in overhead calculations as of 19 November 2022. Approximately 70%
of this allocation is spent on observational overheads (e.g., slew times) As
mentioned in the Introduction, data for four galaxies (NGC 7496, IC 5332, NGC
628, NGC 1365) were obtained within the first two months of science operations
(Table 3), and provide the basis for the first PHANGS-JWST results presented
in this Issue.333Due to a guide star acquisition failure, the NIRCam
observations of IC 5332 were not completed and have been rescheduled for later
in JWST Cycle 1. Since our observation strategy uses the time when the NIRCam
observations are targeting the galaxy to observe the MIRI off-source position
“in parallel,” our IC 5332 dataset also currently lacks an off-source
observation for MIRI. Six more targets are currently scheduled for
observations during 2022, and program completion is anticipated in mid-2023.

Table 3: PHANGS-JWST First Results Observations Galaxy | MIRI Obs. Date. | NIRCam Obs. Date
---|---|---
| (UT: 2022) | (UT: 2022)
NGC 7496 | 07/06 18:02 - 19:45 | 07/06 19:50 - 20:51
IC 5332 | 07/06 21:05 - 22:48 | $\cdots$
NGC 0628 | 07/17 12:14 - 14:47 | 07/17 15:01 - 17:03
NGC 1365 | 08/13 15:00 - 16:43 | 08/13 18:33 - 20:35

Imaging is conducted with NIRCam and MIRI in eight filters from 2 to 21 $\mu$m
(Figure 2). This set of filters is chosen to support our broad science goals
(see Section 6) as well as to optimize observing efficiency (more below).

PHANGS-JWST coverage is designed to maximize overlap with PHANGS data already
obtained by HST, VLT-MUSE, and ALMA, all of which targeted the main star-
forming area of the galaxy disk. This requires 1-2 pointings with NIRCam
(Module B only) and 2-4 pointings with MIRI for each galaxy. For both NIRCam
and MIRI, we use 10% overlap between rows and columns of mosaic tiles. With
this set-up, 13 galaxies required 2 pointings with NIRCam (Module B only)
while 9 were observed with a single pointing. With MIRI, up to four pointings
were required, with 1, 6, 3, 9 galaxies requiring 1, 2, 3, 4 pointings
respectively. The resultant PHANGS-JWST NIRCam and MIRI footprints are shown
in Figure 1.

Table 4: Total Exposure Times, Resolution, and Sensitivities for PHANGS-JWST NIRCAM and MIRI Imaging.  NIRCam |
---|---
Filter | FWHM | FWHM | $N_{\mathrm{exp}}$ | Exp. Time |  $\Omega_{\mathrm{PSF}}$aaThe solid angle subtended by the PSF, calculated using the WebbPSF package (Perrin et al., 2014). |  $\sigma_{\mathrm{I}}$bbThe empirical $1\sigma$ surface brightness sensitivity $\sigma_{I}$ estimated directly from the image data (Section 5.2). This estimate agrees well with the pipeline-provided noise products. |  $5\sigma_{f}$ccThe $5\sigma$ point source sensitivity ($5\sigma_{f}$) based on empty apertures in low-brightness regions of the image. See Section 5.2.
| (arcsec) | (pixel) |  | (s) | ($10^{-12}$ sr) | (MJy sr-1) | ($\mu$Jy)
F200W | 0.066 | 2.141 | 16 | 1202.5 | 0.167 | 0.068 | 0.079
F300M | 0.100 | 1.585 | 4 | 386.5 | 0.365 | 0.045 | 0.077
F335M | 0.111 | 1.760 | 4 | 386.5 | 0.458 | 0.042 | 0.100
F360M | 0.120 | 1.901 | 8 | 429.5 | 0.530 | 0.058 | 0.170
MIRI |
F770W | 0.25 | 2.27 | 4 | 88.8 | 2.180 | 0.11 | 0.87
F1000W | 0.32 | 2.91 | 4 | 122.1 | 3.770 | 0.12 | 1.90
F1130W | 0.36 | 3.27 | 4 | 310.8 | 4.950 | 0.15 | 2.40
F2100W | 0.67 | 6.09 | 8 | 321.9 | 16.10 | 0.25 | 8.00
Table 5: NIRCam Primary Observation & MIRI Background Parallel Visit Plan
Intramodulebox pattern with 4 primary dithers, BRIGHT1 readout pattern
---
NIRCam | NIRCam | Grp/Int | Int/Exp | Exp. Time | MIRI Bkgd Filter | Grp/Int | Int/Exp | Exp. Time
Short Filter | Long Filter |  |  | (s) | (in parallel) |  |  | (s)
F200W | F360M | 3 | 1 | 214.7 | F770W | 8 | 1 | 88.8
F200W | F360M | 3 | 1 | 214.7 | F1000W | 11 | 1 | 122.1
F200W | F300M | 5 | 1 | 386.5 | F1130W | 28 | 1 | 310.8
F200W | F335M | 5 | 1 | 386.5 | F2100W | 14 | 1 | 321.9
Table 6: MIRI Primary Observation Visit Plan 4-point extended source dither
pattern
---
FASTR1 readout pattern
MIRI Filter | Groups per | Integs. per | Exp. Time
| Integration | Exposure | (s)
F770W | 8 | 1 | 88.8
F1000W | 11 | 1 | 122.1
F1130W | 28 | 1 | 310.8
F2100W | 14 | 2 | 321.9

Total exposure times per pointing in each filter are listed in Table 4 and
range from 6.4-20 min with NIRCam and 1.5-5.4 min with MIRI. Tables 5 and 6
summarize the observing parameters and exposure sequences adopted for each
filter in each visit.

####  3.0.1 NIRCam Primary Observations

Our NIRCam primary observations are divided into four separate sequences to
maximize observing efficiency (Table 5). This allows for MIRI sky background
observations to be taken in parallel with each NIRCam sequence, with one
sequence for each of the four MIRI filters in our program. With NIRCam, we use
Module B (FULL subarray) and an INTRAMODULEBOX-4 primary dither pattern with
no additional subpixel dither.444The primary dither pattern itself has very
minimally-sampled subpixel dithering. We obtain four F200W exposures, one at
each INTRAMODULEBOX-4 dither position (for a total of 16 exposures), using the
BRIGHT1 readout pattern with one integration per exposure. Of these
independent sequences, two have 5 groups/integration, contributing 386.5s each
to the total F200W exposure time, and the other two have 3 groups/integration,
yielding 214.7s each. The total F200W exposure time is thus 1202.5s per
pointing.

Simultaneously with F200W primary imaging in the short wavelength channel (and
coordinated parallel MIRI imaging of sky background), we observe the target
with F300M, F335M, and F360M in the long wavelength channel. The F300M and
F335M observations are paired with the two longer F200W exposure sequences.
The exposure time for F300M and F335M is consequently 386.5s (each). During
both of the two shorter F200W exposure sequences, F360M imaging is obtained,
for a total exposure time of 429.5s.

####  3.0.2 MIRI Primary Observations

For our MIRI primary observations, we use a 4-point dither pattern optimized
for extended sources. We observe with the F770W, F1000W, F1130W and F2100W
filters and the FASTR1 readout pattern yielding total exposure times per
pointing of 88.8s, 122.1s, 310.8s, 321.9s respectively (Table 6). All of the
galaxies are larger than the MIRI field of view, so we obtain a background
measurement using this same MIRI imaging sequence for a blank region of sky
near the galaxy in parallel with NIRCam, as just discussed.

####  3.0.3 Special Requirements

We include the following special requirements in our observation plan: 1) a
timing requirement (Sequence Observations, Non-interruptible) that ensures the
MIRI background imaging is obtained directly following the MIRI observation of
the target; 2) position angle requirements to maximize the overlap of the
primary MIRI observations with existing HST, MUSE, ALMA coverage, while
placing the parallel MIRI background observation on blank sky. In some of the
galaxies with largest angular extents in our sample, the parallel sky
background measurement may not be completely beyond the outskirts of the
galaxy disk. Finally, whenever possible, we attempted to widen the orient
constraints to provide a minimum observation window of 2 weeks to provide
greater opportunities for scheduling. It should be noted that the range in
allowable orient angles can result in imaging that is not optimally aligned
with previous data or astrophysically interesting features in the galaxy. For
example, in NGC 1365, the observed orientation led to the omission of portions
of the eastern spiral arm.

##  4 Initial Image Processing

In this section, we describe how the STScI JWST Science Calibration
Pipeline555https://github.com/spacetelescope/jwst is used with additional
processing to enable a first set of analyses by the PHANGS team. We completed
processing of the images for the papers presented in this Issue by early
September 2022. The latest versions of the pipeline and calibration files
available at the time of processing were used: version 1.7.0 (for MIRI) and
1.7.1 (for NIRCam) of the JWST pipeline and Calibration Reference Data
System666https://jwst-crds.stsci.edu/ (CRDS context number 0968 for both
NIRCam and MIRI, though for MIRI we used slightly updated flats provided by K.
Gordon).

###  4.1 Level One Processing

We start with the uncalibrated raw (“uncal”) files obtained from MAST, and use
Detector1Pipeline, which converts from “ramps” to “slopes” and applies basic
detector-level corrections to the data, including the flagging of saturated
pixels and corrections for chip persistence777https://jwst-
pipeline.readthedocs.io/en/latest/jwst/pipeline/calwebb_detector1.html. We ran
the Detector1Pipeline with the default parameters, with one exception. To
attempt to recover some of the saturated pixels, we remove the restriction
that during ramp fitting at least two groups must not reach saturation. These
saturated pixels are primarily an issue in the centers of NGC 1365 and NGC
7496, which both host bright AGN. Removing the restriction did not recover the
very central pixels coincident with the AGN themselves, but it did allow us to
recover a number of the brighter pixels surrounding the central point source.
However, some of these sources, especially the brightest compact, massive
star-forming regions in the starburst ring of NGC 1365, still show artifacts
that have structure suggesting that the source brightness remains
underestimated by the imaging.

###  4.2 Level Two Processing

Stage two of the pipeline produces calibrated individual
exposures888https://jwst-
pipeline.readthedocs.io/en/latest/jwst/pipeline/calwebb_image2.html. During
this stage, the pixel coordinates are translated into WCS coordinates, which
includes application of distortion corrections. This stage also involves flat-
fielding and background subtraction (the background subtraction is used only
for the MIRI data). Because of our parallel observing visit sequence (§3), the
pipeline does not automatically associate the appropriate MIRI off (sky
background) observations with the on-galaxy images. Instead, we manually
implement this association during this processing step. For IC 5332, only the
on-galaxy MIRI observations were obtained during our initial visit. Therefore,
we used the off-galaxy observation from NGC 7496 for this purpose in IC 5332.
This appears to work reasonably well, but does result in a few visible
imperfections in the background subtraction for IC 5332. Also for MIRI, we
masked the part of the image associated with the Lyot coronograph. In this
early reduction, the coronagraph portions of the image consistently showed
imperfect background subtraction that caused issues with our mosaicking.

###  4.3 Principal Component Analysis Destriping

After stage two, we applied additional processing to suppress $1/f$ noise in
the NIRCam data. This correlated read noise primarily manifests as striping
across the NIRCam images, along detector rows (in the fast-read direction).
This can severely degrade the quality of the images, particularly at shorter
wavelengths. Schlawin et al. (2020) studied this effect using simulated data
and provided a number of suggestions for how to deal with this $1/f$ noise. We
experimented with simple row-by-row median subtraction, but found that it was
often unfeasible to calculate a robust background median in the small range
covered by each amplifier due to the presence of widespread diffuse emission
in most of our images. Moreover, we found that though this median subtraction
produced visually better individual images, it yielded poor results when
multiple images were combined to estimate 3.3$\mu$m PAH maps (Sandstrom et
al., subm.a). In those cases, the $1/f$ noise-driven stripes reappeared and
were severe in the star-subtracted PAH images.

Figure 3: Left: Original calibrated image, presented with a strong intensity
transfer function to highlight stripes due to $1/f$ noise. Middle: Principal
component analysis noise model, with the displayed intensity range now
centered on zero and the intensity scale boosted by a factor 5 compared to the
original image. Right: Final destriped data.

Instead, as shown in Fig. 3, we had success modeling the $1/f$ noise using
robust principal component analysis (PCA) based on the technique developed in
Wild & Hewett (2005) and Budavári et al. (2009) for SDSS spectra. We treat
each column of the data as an individual noise spectrum and fit results for
each amplifier separately, because the noise properties differ between
amplifiers. We mask out bright sources and then apply a Butterworth (1930)
filter to remove large-scale structure but retain the small-scale noise
properties. We also shuffle in the $x$ direction and apply a different offset
to each column, effectively rolling the column along the $y$-axis by a random
amount. These shifts prevent the PCA simply learning where the mask is and
responding to the location of bright emission. After this processing, we use
PCA to fit 50 components. Then we reconstruct a model describing the $1/f$
noise-induced stripes for that column using the 5 components with the largest
eigenvalues. Using only five components limits processing time and we found
that increasing the number of components beyond 5 did not significantly
improve the noise model. Finally we subtracted the model of the $1/f$ noise-
induced stripes from the data.

Figure 3 illustrates this process. The left image shows visible artifacts,
mostly horizontal striping. The PCA model successfully captures these features
without including the extended bright emission from the source. Occasionally
corrupted columns and other features arise, which the PCA also robustly
handles. The right image shows visibly less striping and fewer artifacts than
the original image. We find that the PCA reduces the RMS noise in the images
between around 10% to 30%, with the amount of reduction depending mainly on
the amount of area filled by emission in the image. For images with more
source-free sky background, the PCA produces a more significant decrease in
the noise level.

###  4.4 Level Three Processing

The level three stage of the pipeline999https://jwst-
pipeline.readthedocs.io/en/latest/jwst/pipeline/calwebb_image3.html is
intended to combine individual tiles to produce a final rectified mosaic. This
is achieved through relative alignment between tiles, background matching, and
the final drizzling to the output pixel grid.

In our early science reduction, this step involved the most changes from the
default pipeline parameters. For MIRI, we ran the level three pipeline on
individual tiles, i.e., the individual pointings, which we later mosaicked
together by hand. For NIRCam, we used the JWST pipeline to stitch the tiles.

We adjusted the stage three pipeline as follows. First, in the tweakreg step,
which provides relative alignment between tiles, we found it necessary to
limit the ‘roundness’ of sources used for alignment to $-0.5<{\rm
roundness}<0.5$, to reject diffraction spikes from stars and the numerous
extended sources in our field. We also turned off the 2dhist option, as our
relative corrections were generally $\lesssim 1\arcsec$, and leaving this on
degraded the quality of the alignment. We fit the per-tile offsets using only
($x,y$) shifts, as we found allowing for rotation produced negligible rotation
angles and degraded the quality of the fits. For data acquired in July and
August 2022, we found relative corrections as large as $\sim$1″. This
primarily reflects the quality of the guide star catalog during that period.
The size of the correction will likely decrease as JWST observations continue
and the guide star catalog is updated and improved.

For the NIRCam data, we calculate and subtract the background level for each
detector individually. By default, the pipeline will subtract a single
background for the four short-wavelength NIRCam detectors, however, we found
that this produced clear ‘steps’ in our mosaics.

In the final redrizzling step during stage three, we produced mosaics with the
conventional north-up orientation to allow for more straightforward comparison
with data at other wavelengths.

###  4.5 Absolute Astrometric Alignment

The level three pipeline produced mosaics that have good relative alignment
between NIRCam tiles, but the alignment of the JWST images relative to other
data with well-established absolute astrometry tended to be poorer, as high as
$\sim 1^{\prime\prime}$ in some cases. After stage three processing, we
therefore performed an additional correction to improve the absolute
astrometry of the NIRCam and MIRI images. We adopt the general strategy of
bootstraping the astrometric solution from images that are close in wavelength
instead of directly using astrometric catalogs, as was followed for PHANGS–HST
(Lee et al., 2022).

Our JWST images lack enough Gaia (Gaia Collaboration et al., 2016) sources to
anchor our astrometry directly to GAIA (Lindegren et al., 2018a, b). However
our NIRCam images detect many AGB stars that are also visible in the
PHANGS–HST images, which have astrometric solutions accurate to better than 10
mas. Thus, we extracted a set of AGB stars from the PHANGS–HST DOLPHOT catalog
(Thilker et al., 2022; Lee et al., 2022), and cross-matched them to sources
detected in the NIRCam bands in the level 3 source_catalog step (the final
level 3 step) using the XYXYMatch function in tweakwcs. We solve for a linear
offset and rotation to match the images (though typically the rotation was
very small). Typically, we identified a few hundred AGB stars per target from
the PHANGS–HST catalogs and the pipeline found 50-100 good matches in the
NIRCam imaging. After applying the solutions comparisons between the NIRCam
and HST data suggest that the rms scatter in the astrometric accuracy across
the NIRCam images is about $\pm 0.1$ NIRCam pixel.

The MIRI images have fewer point sources than those from NIRCam and HST, so we
derive the astrometric solution for MIRI using a cross-correlation approach.
To do this, we take advantage of the fact that the F335M NIRCam band, which
has been anchored using AGB stars to the PHANGS–HST and Gaia frame, contains
both stellar and ISM-like emission because of the strong PAH band at $3.3\mu$m
(see Sandstrom et al., subm.a). The stellar emission allows us to pin the
astrometry to the AGB stars and the PHANGS–HST frame, while the morphology of
the ISM at $3.3\mu$m resembles the ISM emission in the longer wavelength MIRI
bands. This allows us to use a cross-correlation analysis to solve for the
relative astrometry of NIRCam and MIRI bands. Specifically, we used the image-
registration package to run a cross-correlation and solve for the linear shift
(by identifying the maximum of the cross-correlation) needed to match the
shortest MIRI band, F770W, to the F335M astrometry. Then, with the F770W
astrometry established, we solved for the shift needed to align F1000W to
F770W, F1130W to F1000W, and F2100W to F1130W. We adopted this stepwise
approach to reflect the increasing dominance of the ISM emission over stellar
emission as the wavelength increases. The cross-correlation technique
generally produces ‘good’ results but can be sensitive to artifacts, e.g.,
diffraction spikes around the AGN. By eye, this seems to yield images that are
aligned at the level of $\pm 1$ MIRI pixel, i.e., about $0.1^{\prime\prime}$.
While this is sufficient for the first results presented in this Issue (except
where noted in specific papers), the alignment is not as good as would be
expected based on alignment based on a more limited set of carefully selected,
bright, isolated point sources (e.g., Lee et al., 2022). We expect to
significantly improve this in future work.

###  4.6 By-Hand MIRI Mosaicking for Early Science

After level three processing and alignment, we combined the individual MIRI
tiles into a single image for each galaxy at each band. To do this, we defined
one of the tiles for each galaxy as the reference tile. Then we reprojected
the other tiles onto the astrometric grid for the reference tile and compared
the intensity of the two tiles in the region where they overlap. We solved for
and applied the additive offset needed to yield a one-to-one match in
intensities between the other tile and the reference file. These offsets were
typically $\lesssim 0.05$ MJy sr-1 for F770W and F1000W, $\lesssim 0.15$ MJy
sr-1 for F1130W, and as high as $\sim 0.3{-}0.8$ MJy sr-1 for F2100W. This
yielded a set of individual tiles with the same background level, but did not
anchor that background level to the correct absolute value.

Next we defined a new astrometric grid with a larger size but the same pixel
scale and orientation as that of the reference field. We then reprojected all
tiles onto this new grid, averaging intensities from different tiles with
equal weighting where tiles overlapped. The result at this stage was a single
combined MIRI image registered via cross-correlation to the NIRCam and other
MIRI images, but with an absolute background level that was still uncertain.

Note that we expect to deprecate most of these manual steps as the pipeline
processing improves in the future. However, for this first processing during
these early months we found that this simple, by-hand approach yielded
smoother backgrounds and a better match to previous infrared imaging of our
targets than the pipeline.

###  4.7 Refinements to the Pipeline Background Subtraction

As a final step, we adjust the overall background level of the MIRI images to
match previous wide-field mid-infrared imaging of our targets. The JWST field-
of-view for most of our targets contain relatively little empty sky and the
match between the off-source and the on-source image was not sufficient to
yield a precise background level. Fortunately, all of our sources have
previous observations at least by WISE at $12\mu$m and $22\mu$m (Wright et
al., 2010) and for NGC 628, NGC 1365, and IC 5332 by Spitzer at $8\mu$m and
$24\mu$m (Kennicutt et al., 2003; Dale et al., 2009; Armus et al., 2009).
Though these data have much worse resolution than the JWST images they cover a
much larger area and extend to empty sky and so have well-established
background levels.

We perform this background homogenization in two stages following a procedure
detailed in the Appendices of Leroy et al. (subm.a). First we establish a
common background system across all JWST bands. Then we anchor the overall
system to an external band with a well-established background. To do this, we
make a version of the MIRI images that all share the PSF of the F2100W image
and a version of all JWST, Spitzer, and WISE data that share a common
$15^{\prime\prime}$ FWHM Gaussian PSF. That is, we match the backgrounds among
the JWST bands at common resolution. These procedures leverage the fact that
mid-infrared emission at different wavelengths shows strong, often nearly
linear correlations even though the exact band ratios do vary with filter
combination (see Appendix in Leroy et al., subm.a). This procedure appears to
yield good results with an uncertainty in the overall background level of
better than $\pm 0.1$ MJy sr-1 across all MIRI bands.

###  4.8 Limitations, Caveats, and Expected Improvements

The reduction procedures described above yield data that are useful for the
initial work presented in this Issue. Over the longer term, improvements will
be made to address known limitations that we summarize here (as well as
possible additional issues found during future analysis). In addition to
refining our own procedures, we expect to take full advantage of the monthly
refinements to the pipeline being released by STScI101010https://jwst-
docs.stsci.edu/jwst-science-calibration-pipeline-overview/jwst-operational-
pipeline-build-information. The pipeline documentation already identifies some
of these issues as areas of future development.

Where these limitations affect our First Results scientific papers they are
noted, but most of our analyses are conducted to mitigate their impact. For
example, aperture photometry with local background subtraction avoids
uncertainties in measurement of the background (e.g., Rodriguez et al., 2022;
Whitmore et al., subm.).

####  4.8.1 Astrometric Uncertainties

The relative alignment of the NIRCam images is excellent, with sufficient
numbers of common sources available in adjacent bands to align frames and to
tie to the astrometry established in the PHANGS-HST
imaging111111https://archive.stsci.edu/hlsp/phangs-hst. Using AGB stars, we
can achieve $<10$ mas astrometric accuracy for the NIRCam data (Section 4.5).
The MIRI images are currently aligned by cross correlation and have larger
uncertainty in their astrometric solutions ($\pm 0\farcs 1$).

We expect that a combination of better constraints from cross-correlation and
the use of AGB stars will increase the astrometric accuracy of the MIRI
images. Astrometric solutions are also expected to continue to improve as
distortion models are updated by STScI.

Our science images are not currently drizzled onto a matched pixel grid, but
adopting and drizzling to the grid adopted for the PHANGS-HST images would
significantly facilitate joint HST-JWST analysis. However, the different pixel
scales spanned from 2-21$\mu$m make this exercise a trade-off between
retaining information and limiting data volume.

####  4.8.2 Flux calibration & backgrounds

The zero points (i.e., absolute flux calibration) for the JWST instruments are
being refined. These changes will primarily primarily affect the NIRCam data
(Boyer et al., 2022), and shortly after the completion of the analyses
presented in this Volume, these zero-points were updated by STScI.

The background levels of the MIRI data have been inferred from band-to-band
correlations and comparison with existing lower resolution infrared imaging
from previous missions. These are currently uncertain at the $\pm 0.1$ MJy
sr-1 level. Comparison of the MIRI data with previous data indicates that,
after the subtraction of a background, the intensity measurements agree to
better than 10%.

We expect that the mosaicking and tiling in the pipeline will improve and
replace our by-hand approach to the MIRI data. In addition to yielding a
better astrometric solution, this will allow rigorous testing of the pipeline
noise estimates and move us towards an accurate noise model for MIRI. As
improved flats and better sky subtraction techniques become available they
should also improve the local behavior of the MIRI backgrounds.

####  4.8.3 Saturation and Poor Ramp Fitting

Some of the brightest sources in the MIRI imaging, particularly in NGC 1365,
are saturated, show artifacts resulting from poor ramp fitting, and the
surrounding areas are affected by diffraction spikes. The photometry of
sources with poor ramp fits is likely underestimated (Section 4.1).

Hassani et al. (subm.) produced bespoke masks to flag diffraction spikes due
to the AGN in NGC 1365 and NGC 7496, but as better PSF estimates become
available, subtracting such features from the image should become an option.
Liu et al. (subm.) present the first efforts at recovering the fluxes of
sources with saturated pixels and poor ramp fits by using PSF matching in the
outskirts of the source.

####  4.8.4 Angular resolution

Finally, while not strictly an image processing issue, we caution that the
large span of wavelengths of our JWST images implies a similar variation in
their angular resolution. Analysis that involves comparisons across multiple
JWST bands, or between the JWST and other data, requires care to ensure that
relative flux measurements are robust. In general, the analyses in this Volume
rely on flux density measurements from aperture photometry or on surface
brightness comparisons, generally at matched resolution. Where appropriate, we
have generated convolution kernels following the method of Aniano et al.
(2011) to degrade the resolution of short wavelength images to the long
wavelength bands, achieving a common resolution match. In the future, PSF-
fitting packages like dolphot (Dolphin, 2016) will be used, and methods to
employ joint SED/image deconvolution based on priors from the higher-
resolution short-wavelength imaging will be implemented. However, the results
presented in this Volume rely on simpler methods, and should be interpreted
accordingly.

##  5 Overview of First PHANGS-JWST Images

###  5.1 Comparison to Spitzer

Figure 4: New PHANGS-JWST imaging for NCG 628 (right panels) compared with
data previously taken with Spitzer (left panels), illustrating the order of
magnitude gains in resolution and sensitivity. Comparisons at three different
wavelengths are shown: Top panels: Spitzer IRAC 3.6 $\mu$m (Channel 1) vs.
JWST NIRCam F335M; Middle panels: Spitzer IRAC 8 $\mu$m (Channel 4) vs. JWST
MIRI F770W; Bottom panels: Spitzer MIPS 24 $\mu$m vs. JWST MIRI F2100W. The
Spitzer IRAC and MIPS data are taken from the SINGS program (Kennicutt et al.,
2003; Dale et al., 2005). The JWST image is formed from a mosaic of two
pointings with NIRCAM and three pointings with MIRI. Figure 5: Comparison of
new PHANGS-JWST imaging with data previously taken by Spitzer, now for NGC
7496. Here comparisons at two wavelengths are shown: (left two panels) Spitzer
IRAC 3.6 $\mu$m (Channel 1) vs. JWST NIRCam F335M; (right two panels) Spitzer
IRAC 8 $\mu$m (Channel 4) vs. JWST MIRI F770W.

Figure 6: Still images from animation
(https://app.box.com/s/zpviwjgtv14ozlrc0z1yi4dt9s9mtrmk) showing a color
composite image of NGC 7496 created from HST UV-optical imaging (red:
F814W/F555W/F438W; green: F336W; blue:F275W) that fades into the same image
combined with a JWST MIRI composite in red hue (with red: F2100W; green:
F1130W; blue: F1000W). Both images produced by J. Schmidt.

While past infrared missions have provided imaging of the nearby galaxies in
the PHANGS-JWST sample, JWST’s vast improvement in sensitivity and resolution
reveals an extraordinary new view of the complex organization of the ISM. In
Figures 4 and 5, we present our JWST imaging of NGC 628 and NGC 7496 in
selected filters, alongside previous Spitzer observations, to illustrate data
quality and the new science enabled by the observations. The two galaxies are
at 9.84 Mpc and 18.7 Mpc respectively, and span most of the distance range
(5-20 Mpc) covered by our sample. In Figure 2, we show the filter curves of
the Spitzer bands which overlap JWST wavelength coverage together with the
eight PHANGS-JWST filters.

Perhaps the most remarkable feature in the PHANGS-JWST imaging is the network
of filaments, shells, bubbles, and the star-forming populations nested in
these structures, as traced by the dust in emission. While the highest surface
brightness features are apparent in the Spitzer imaging, the JWST imaging
shows the pervasiveness of the network deep into the interarm regions, and
morphological details that suggest that the multi-scale impact of star
formation feedback on the ISM is ubiquitous. With the PHANGS-JWST imaging, we
will now be able to develop catalogs of these structures and characterize
their ensemble properties (e.g., Watkins et al., subm.; Rodriguez et al.,
2022; Hassani et al., subm.; Thilker et al., subm.) even with the lowest
resolution image at 21$\mu$m.

For both NGC 628 and NGC 7496, we show the NIRCam F335M and Spitzer/IRAC 3.6
$\mu$m images. The NIRCam F335M filter better isolates the 3.3 $\mu$m PAH
feature, and observations in the flanking F300M and F360M bands (not shown)
enable continuum subtraction. It is clear that the increase in resolution (PSF
FWHM 1$\farcs$6 vs. 0$\farcs$11), which now samples 5 pc at NGC 628 and 10 pc
at NGC 7496, enables the study of individual star clusters and associations,
and allows embedded stellar populations to be identified. A slightly extended
point source southeast of the galactic center in NGC 628 is resolved into two
background galaxies, and illustrates how JWST significantly improves the
characterization of extended stellar structures and the identification of
background “interlopers.”

For NGC 628, the MIRI F770W and Spitzer/IRAC 8 $\mu$m images are shown. The
bandpasses, which are similar, capture the complex of PAH features in the
$\sim$8 $\mu$m spectral region (Figure 2). Overall the same structures are
apparent in both images, with MIRI F770W image being eight times sharper (PSF
FWHM 2$\farcs$0 vs. 0$\farcs$24), sampling 12 pc at NGC 628.

Finally, the MIRI F2100W and Spitzer/MIPS 24 $\mu$m images, which capture flux
from the warm dust continuum, are shown for both galaxies. Here, although the
relative increase in the angular resolution is similar as for the previous two
sets of images, the improvement in the fidelity of the JWST data is the most
dramatic as the MIPS 24 $\mu$m PSF was only 6″, and the majority of emission
appeared diffuse. With the MIRI F2100W PSF of 0$\farcs$67 (30 pc at NGC 628
and 60 pc at NGC 7496), the dust emission is resolved into compact sources,
mostly along the spiral arms, and shows the same intricate ISM structure seen
in the MIRI F770W image.

In terms of surface brightness, the Spitzer images achieve slightly better
sensitivities than the JWST data, as might be expected. The NGC 628 images,
from the Spitzer SINGS program (Kennicutt et al., 2003), are mosaics with
1$\sigma$ surface brightness limits of $\sim$0.02 MJy sr-1 at 3.6$\mu$m and
$\sim$0.2 MJy sr-1 at 24$\mu$m. The corresponding sensitivities are
$0.04~{}\mathrm{MJy~{}sr^{-1}}$ for NIRCam F335M and
$0.25~{}\mathrm{MJy~{}sr^{-1}}$ for MIRI F2100W (see Table 4 and discussion in
the next section). Of course, the principle gain is in the $\sim$10-fold
improvement in resolution and corresponding decrease in the solid angle
subtended by the PSF, so that the JWST point source sensitivity is $\sim 50$
times better than Spitzer.

###  5.2 Empirical Noise Estimates

Table 4 provides sensitivity limits based on our initial reduction of PHANGS-
JWST data. We evaluated the depth of our data with respect to detection of
point sources at the resolution achieved in each filter, and measurement of
diffuse emission on a fixed larger scale.

For the point source limit, we first identified the faintest area mapped that
was also fully contained within the footprint of maximum exposure time. Inside
each area, aperture photometry was performed for a set of 50 independent
circular apertures sized to match the 50% encircled energy radius of each
filter. Background estimation for each aperture was also filter-dependent,
using an annulus spanning a radial range from 2-3 times the aperture radius.
The sigma-clipped standard deviation of the background-subtracted flux for
these ‘empty’ apertures was then scaled by a factor of ten to estimate the
5-sigma point source detection limit in each filter ($5\sigma_{f}$), which
accounts for a factor 2 for the choice of 50% enclosed energy radius and a
factor of 5 for the choice of significance level. No correction was made for
the contribution of the point source flux to the background annulus flux,
which is expected to be negligible for an unresolved source. Averages of these
galaxy-specific limits per filter are tabulated. It is important to note that
this method, does not include loss of point source sensitivity due to source
crowding in more complex, brighter regions of each JWST target. Such loss of
completeness due to crowding and associated photometric bias will be
quantified later when PSF-fitting photometry is undertaken for our entire set
of targets.

To estimate the surface brightness sensitivity ($\sigma_{I}$) for the imaging
data, we consider the distribution of pixel values in an unsharp masked image.
For each image, we compute a median filter with a circular tophat filter with
a diameter 6 times the PSF FWHM in the band. We subtract off the median
smoothed version from the original map to remove large scale structure and
produce an unsharp masked image. We then use a median absolute deviation
estimator to measure the local noise in the unsharp masked image. We
iteratively reject all values larger than the local 3$\sigma$ noise
measurement in each filter. After the data rejection converges, the local
noise estimates are smoothed with a tophat filter that has a size of
15$\times$ the PSF FWHM and interpolating over missing values. We compare this
local estimate of the noise to the noise estimates provided by the JWST
imaging pipeline. The resulting noise maps are within 10% of the pipeline
generated noise maps. We also estimated the local noise by calculating the
median autocorrelation of data as a function of scale and by examining the
distributions of data in signal-free parts of images (though some bands show
emission across the field). All methods arrive at similar noise estimates and
reflect the spatial structure in the mosaicked fields.

Note that because of the extended JWST PSFs, there is less encircled light at
a PSF FWHM than for a Gaussian source. Thus, the simple scaling of surface
brightness noise to obtain a point source sensitivity leads to an overestimate
of the point source sensitivity:
$\sigma_{I}\Omega_{\mathrm{PSF}}\gtrsim\sigma_{f}$, where
$\Omega_{\mathrm{PSF}}$ is the solid angle subtended by the PSF.

###  5.3 Color Composites & Public Outreach

Since the first observations were taken for the PHANGS-JWST Treasury survey in
2022 July, color composites that combine JWST imaging from multiple filters
have been developed and released for all of our targets by astronomers,
professional public outreach teams, the press, as well as the public. The
color images have broadly captured the attention of both the science community
and general public not only because the structures portrayed are aesthetically
stunning, but also because the images vividly illustrate and make
qualitatively accessible the physics of star formation, feedback, and the ISM
– particularly when the UV-optical imaging from HST is included.

Figure 6 shows the first composite PHANGS-JWST image published for NGC 7496,
which was observed shortly after the end of commissioning and among the first
science data to be released from the mission. The image is a sum of two
composites: one based on the HST UV-optical filters (red: F814W/F555W/F438W;
green: F336W; blue:F275W) and a second based on the JWST MIRI F1000W, F1130W,
F2100W filters in red hue. The animation compares the emission from the HST
and JWST data. The regions and structures that are dark in the optical due to
dust obscuration are illuminated in the mid-infrared by the re-radiated
emission from small dust grains. The complex network of filaments, bubbles,
shells, and compact sources described earlier can now be seen in the context
of the visible young stellar populations that line the peripheries of the
network, and have provided the feedback energy that, together with galactic
dynamics, shapes the ISM. Infrared compact sources without optical
counterparts can be identified, revealing the sites of the earliest stages of
star formation.

Figures 7 and 8 show RGB color composites, now based on only the MIRI imaging,
for all four PHANGS-JWST First Results galaxies. The diversity of the dust
emission structure motivates the study of a representative sample of galaxies,
and our specific science goals (next section). The four galaxies bracket the
full range of star formation properties in the sample (with NGC 1365 having
the highest star formation activity and gas surface density and IC 5332 among
the lowest) and feature varied dynamical structures (e.g., bars in NGC 1365
and NGC 7496; strong spiral arms in NGC 628 and weak spiral structure in IC
5332). The PHANGS-JWST team is collaborating with image processor Judy Schmidt
to produce color composites as new data are received, and with the public
outreach offices at ESA, STScI, and to support the communication of new
results.

Figure 7: Color composite images produced by J. Schmidt from MIRI data of NGC
628 (top) and IC 5332 (bottom). Red: MIRI F2100W; orange: MIRI F1130W; cyan:
MIRI F770W and extra overall brightness in grayscale: MIRI F1000W. The images
were used as the models for the first two ESA/JWST Pictures of the Month from
the JWST Cycle 1 General Observers Program as featured at
https://esawebb.org/images/potm2208a/ and
https://esawebb.org/images/comparisons/potm2209a/.

Figure 8: Same as Figure 7 but for NGC 1365 (top) and NGC 7496 (bottom).

##  6 PHANGS-JWST Science Goals

In this section, we summarize the science goals that motivate the PHANGS-JWST
survey, and highlight how results presented in this Issue begin to make
progress toward those goals.

Our studies of star formation and dust emission on the scales of star
clusters, HII regions, and giant molecular clouds are enabled by JWST’s new
high resolution views of the near- and mid-IR in our selected set of 8
filters. As shown in Figs. 2 and 9, the F200W, F300M, and F360M filters
provide low obscuration views of stellar photospheric emission, with some
contribution from nebular emission, and hot dust. The F335M, F770W, F1130W
capture PAH emission, tracing a combination of PAH size and charge, with the
F300M and F360M allowing for robust continuum subtraction for F335M to isolate
the $3.3\micron$ PAH feature. F1000W and F2100W capture the dust continuum,
with possible contribution to the F1000W band by silicate absorption.

Together with data from HST, MUSE, and ALMA, the integrated PHANGS dataset
allows for multi-scale, multi-phases studies of the star formation cycle
across a range of galactic environments. In Figure 2, the five UV-optical
filters used in the PHANGS-HST program (Lee et al., 2022) are also shown;
these mainly sample the stellar continuum and provide a view of dust in
extinction. The HST imaging provides high angular resolution (0$\farcs$8),
comparable to JWST NIRCam imaging at 2$\mu$m. The PHANGS-MUSE program
(Emsellem et al., 2022) provides integral field unit spectroscopy over
$0.475<\lambda/\mu\mathrm{m}<0.935$, covering many of the same bands as HST
but with coarser angular resolution ($\sim 1^{\prime\prime}$). PHANGS-ALMA
(Leroy et al., 2021) shows the distribution and kinematics of the CO that
traces the star forming molecular medium, also at $\sim 1^{\prime\prime}$
resolution.

###  6.1 Constraining the natal cluster mass function and cluster formation
efficiency

Figure 9: Example of a dust embedded young star cluster—a compact infrared
source with no detection in the optical HST images. Postage stamps from all 13
HST+JWST PHANGS images are shown together with photometry, as an illustration
of the SED fitting that will be performed in the future to constrain the
physical properties of these objects.

PHANGS-JWST near- and mid-IR imaging will be used to detect dust embedded
clusters and associations, to mitigate and measure the effects of extinction,
and to constrain other physical properties such as age and mass through SED-
fitting (e.g. Figure 9). We will test different selection strategies to
systematically identify embedded cluster candidates, and produce the first
complete catalogs of these sites of the earliest stages of star formation. The
catalogs will enable us to measure the fraction of stars that are born in
compact clusters; characterize the distribution of cluster masses at birth;
and investigate dependence on environment.

The ensemble properties of star clusters are highly debated in the literature
(e.g., Larsen, 2009; Chandar et al., 2017; Krumholz et al., 2019; Adamo et
al., 2020a, b). Due to dust extinction, it has been difficult to obtain a
truly complete census of clusters across all galactic environments and ages.
The PHANGS-JWST infrared imaging will now allow us to study the clusters in
dusty galaxy centers, spiral arms, and bar-end regions. These are sites where
super star clusters tend to be found, the cluster formation efficiency is
expected to be highest (e.g., Kruijssen, 2012; Krumholz, 2014; Adamo et al.,
2015; Ginsburg & Kruijssen, 2018; Grudić et al., 2021), and molecular cloud
populations show clear enhancements in both density and turbulence (e.g.,
Longmore et al., 2014; Beuther et al., 2017; Sun et al., 2020; Henshaw et al.,
2022). Extrapolating from the visible cluster population, we expect to detect
$\gtrsim$100 new embedded clusters in each galaxy or ${\sim}2{,}000$ across
the sample, with the greatest gain in the dustiest and most active parts of
the sample. Analysis combining JWST with HST data will also allow us to break
the age-reddening degeneracy for optically detected clusters, and derive
robust constraints on age, mass, and reddening for both embedded and visible
cluster populations (e.g. Figure 2).

These investigations use the full set of PHANGS-JWST data for measuring the
properties of embedded clusters. In particular, the near-IR bands provide key
constraints on stellar properties. The F200W observations provide good
visiblity through the dust in the region of the SED still dominated by stellar
photospheric emission. The excellent angular resolution (0$\farcs$07) at
2$\mu$m is required to resolve star clusters, which have effective radii
between $0.5$ pc to about $10$ pc (Portegies Zwart et al., 2010; Ryon et al.,
2017).

Rodriguez et al. (2022) Whitmore et al. (subm.) take the first steps in
identifying embedded clusters, estimating their physical properties, and
examining the incompleteness in optical samples. Hoyer et al. (2022) study the
nuclear star cluster in NGC628 and the nature of the mid-infrared structure
surrounding it. Schinnerer et al. (subm.) show that massive, dusty clusters
can provide a sensitive diagnostic of the very dusty star-forming environments
in nuclear regions when comparing ALMA data and simulations. Some of the
clusters detected in the PHANGS-JWST survey push current stellar and dust
models used by CIGALE to their limits due to their very young ages, high dust
content, and/or low mass. Including dust models computed in the optically
thick case and stellar populations with a stochastically sampled Initial Mass
Function may prove necessary to model such clusters.

###  6.2 Measuring the Duration of Embedded Star Formation

PHANGS-JWST data enable characterization of the poorly studied embedded phase
of star-formation before feedback disrupts the natal cloud. Observations that
resolve galaxies into discrete star-forming regions, clusters, and molecular
clouds have enabled statistical measurements of the timescales associated with
the major phases of the star formation cycle (e.g., Kawamura et al., 2009;
Kruijssen & Longmore, 2014; Meidt et al., 2015; Corbelli et al., 2017; Grasha
et al., 2018, 2019; Kruijssen et al., 2018, 2019a; Hannon et al., 2019;
Chevance et al., 2020a; Ward et al., 2022; Kim et al., 2022a; Hannon et al.,
2022). Over the last decade, the (anti-)correlation between clouds and H II
regions or optically-identified clusters has constrained the total lifetimes
of clouds to 10-30 Myr (where the range represents physical variation rather
than the uncertainty, see e.g., Chevance et al., 2020b, 2022a). However, both
the duration of the earliest, embedded phase and the length of time that a
cloud remains truly devoid of star formation activity remain poorly known.

Measuring these timescales is essential for understanding the dominant physics
of star formation. The time between cloud formation and the onset of star
formation constrains the (im)balance between the driving and decay of
turbulence (e.g., Klessen & Glover, 2016; Padoan et al., 2017). The duration
of the embedded phase reflects the time that it takes feedback to start
dispersing a cloud (e.g., Olivier et al., 2021). Measuring the embedded
timescale can also distinguish between models in which star and cluster
formation “accelerate” over a cloud’s lifetime (Murray, 2011; Lee et al.,
2016) and those that favor a more steady process (Krumholz & McKee, 2005,
2020; Krumholz & Tan, 2007). Timescales for the embedded and optically visible
stages can be used to infer which feedback mechanisms quench star formation
(e.g., Lopez et al., 2014; Kruijssen et al., 2019a; Chevance et al., 2022b;
Kim et al., 2022a; Hannon et al., 2022), and whether clouds are magnetically
sub- or super-critical (Kim et al., 2021b).

Accessing the embedded phase of star formation requires a tracer of obscured
star formation that reaches the scale of individual clouds. The limited
resolution of Spitzer ($\sim 6^{\prime\prime}$) limited the number of galaxies
where such measurements were possible to five (Kim et al., 2021a). With the
increased spatial resolution and sensitivity in the mid-infrared achieved with
MIRI, the time evolution of the early stages of star formation can now be
characterised in galaxies out to $\sim 30\,$Mpc.

By combining some of the first PHANGS-JWST MIRI observations with PHANGS-ALMA
CO observations (Leroy et al., 2021), Kim et al. (2022b) determine the
relative duration of the early phases of star formation, from truly quiescent
clouds, to embedded star formation, revealed star formation, and cold gas-free
H II regions in NGC 628. In combination with the stellar “clocks” offered by
star clusters (see §6.1) and H$\alpha$ emission (e.g., Leroy et al., 2013;
Haydon et al., 2020), these relative measurements are converted into absolute
timescales. PHANGS-JWST data will enable this measurement across the wide
range of environments that are present in the sample. These observations will
constrain how the physical mechanisms that regulate and drive the successive
phases of star formation map to environment.

###  6.3 Characterizing How Dust Properties Respond to Local Environment

Most of the imaging bands used in PHANGS-JWST show emission features from dust
continuum and PAHs. The PAHs reprocess as much as $20$% of all absorbed
stellar UV/optical light (Smith et al., 2007). As they do so, they eject
electrons that heat the ISM and produce bright mid-IR emission features (e.g.,
Tielens, 2008) that are among the most highly detectable star formation
tracers across cosmic time (e.g., Riechers et al., 2014). However, PAH
properties and abundance vary substantially within and between galaxies. Fewer
PAHs are present in H II regions or at low metallicities (e.g., Engelbracht et
al., 2005; Chastenet et al., 2019) but they can make up $>$5% of the dust mass
in the diffuse ISM of solar metallicity galaxies (Draine et al., 2007; Aniano
et al., 2020). This variability hampers the use of PAH emission as a star
formation tracer (Calzetti et al., 2007; Whitcomb et al., 2020) and leads to
critical uncertainties when modeling their role in the ISM. The PHANGS-JWST
Treasury data will yield the first highly resolved atlas of PAH
properties—abundance, size distribution, and charge inferred from mid-IR band
ratios—in the nearby galaxy population. These data can be used to build an
empirical model to predict the abundance and characteristics of PAHs as a
function of local metallicity, ISM phase, density, and radiation field (from
HST, MUSE, and ALMA). The measurements underpinning this model must be highly
resolved, because radiation field, density, and ISM phase can vary by factors
of ${\sim}1000$ on 10-100 pc scales, altering the PAHs (Chastenet et al.,
2019; Draine et al., 2021). PHANGS-JWST First Results in this Issue from
Chastenet et al. (subm.a), Chastenet et al. (subm.b), Dale et al. (subm.), and
Egorov et al. (subm.) all show that PAH emission properties do indeed vary
systematically with the local galactic environment.

###  6.4 A High Resolution View of the Neutral ISM

PAH emission offers a promising tracer for the structure of the neutral ISM.
Traditionally, H I 21-cm emission and the lowest energy rotational lines of CO
($\lambda=2.6$ and 1.3 mm) have been used to map out the atomic and molecular
phases of the ISM. These tracers suffer some limitations: the very long
wavelength of 21-cm emission makes it intrinsically faint, and current
interferomers can typically only achieve resolutions of 5-10” for non-
prohibitive integration times. The (sub-)millimeter CO emission lines are at
shorter wavelengths and thus significantly better resolution of 1” can be
achieved with ALMA (Leroy et al., 2021), but they are an imperfect proxy for
the H2 that constitutes the molecular ISM. With JWST, the mid-IR PAH emission
potentially provides a much higher resolution view ($<0\farcs 37$) of the same
interstellar gas that is traced by H I and CO. While the emissivity of PAHs
likely varies with environment (Draine et al., 2007; Hensley & Draine, 2022),
the spatial distribution of the mid-IR PAH emission yields an exceptionally
detailed, high dynamic range image of the structure of neutral gas in a
galaxy. Based on studies in the Milky Way and the nearest galaxies, the older
“spherical cloud” view of the ISM has been integrated with a newer filamentary
description of ISM structure (Hacar et al., 2022). The enthusiasm for such a
filamentary view has been driven by infrared imaging by the previous
generations of instruments, but it was difficult to extend this view to a
galactic scale in all but the nearest galaxies. JWST imaging of PAH emission
yields such high resolution maps of neutral gas throughout the Local Volume.

Figure 7 shows the filamentary organization of the gas in NGC 628, which
prompted several initial investigations of ISM structure using PAHs as ISM
tracers. Sandstrom et al. (subm.b) demonstrates a new JWST band combination
that produces high quality maps of the PAH feature at 3.3 $\mu$m with
excellent stellar continuum subtraction. These maps offer the highest
resolution ($0\farcs 11$) wide-area maps of the ISM in nearby galaxies.
Sandstrom et al. (subm.a) verifies that PAH features are intimately connected
to the neutral ISM and uses the genus statistic for characterizing the
emission topology. Thilker et al. (subm.) show how the filamentary network of
3.3 $\mu$m PAH emission relates to extinction lanes identified in the optical
HST imaging, and to the young cluster population of a galaxy. Meidt et al.
(subm.) show that the spatial separation scales between filaments is
consistent with large scale gravitational fragmentation models. Williams et
al. (subm.) leverage the high resolution images to make a careful measurement
of evolutionary timescales in a spiral arm spur.

PAH imaging also shows us where the neutral ISM is not found. Rings of PAH
emission with faint centers can be used to define the wall of “bubbles” or
feedback-driven shells. While bubbles from feedback have been long studied
(e.g., Oey & Clarke, 1997; Weisz et al., 2009, and references therein), the
exquisite resolution, the sensitivity to structure on many scales, and the
wide dynamic range of the PHANGS-JWST images make them an excellent tracer of
bubble walls. PHANGS-JWST data can be used to produce inventories of bubbles
and shell features. These structures can be connected to the sources of
feedback identified from PHANGS-MUSE and PHANGS-HST catalogs of star clusters
and associations, and to the kinematics of the cold ISM as traced by PHANGS-
ALMA. As an early demonstration of such analyses, Watkins et al. (subm.)
create a bubble catalog for NGC 628, and examine their properties, including
the size distribution for the ensemble population. Barnes et al. (subm.)
demonstrate the capabilities of the PHANGS multi-wavelength dataset for
measuring the expansion rates and understanding the origins of large kpc-scale
cavities, focusing on the “Phantom Void” in the southeast quadrant of NGC 628.

###  6.5 Robust mid-IR Star Formation Diagnostics From Cloud to Galaxy Scales

Mid-IR emission from hot dust is widely used to trace star formation at both
low and high $z$ (e.g., Kennicutt & Evans, 2012). Unfortunately, the
calibration of mid-IR star formation tracers has a strong dependence on both
spatial scale and environment (e.g., Boquien et al., 2015), which likely stems
from a variable heating contribution by old stars and uncertain dust geometry
(e.g., Groves et al., 2012; Nersesian et al., 2019). PHANGS-JWST imaging,
combined with supporting PHANGS datasets, enables a data-driven description of
the origin and variations in mid-IR emission. Mid-IR emission is due to the
heating of dust and PAHs by both young and old stars, but the bright thermal
dust emission in the mid-IR continuum bands (F2100W and F1000W) is expected to
respond differently to the local dust-heating radiation field than the PAH
emission (Draine et al., 2007; Hensley & Draine, 2022), offering a potentially
powerful diagnostic of local interstellar conditions. With the insights
obtained by comparing mid-IR band ratios and physical dust models, we will
build on previous work that calibrated the mid-IR emission as a star formation
rate indicator on galaxy-wide and sub-kpc scales (e.g. Calzetti et al., 2007).

Combining robust high resolution measurements of the embedded star formation
from PHANGS-JWST with PHANGS-ALMA and PHANGS-MUSE imaging of the molecular gas
and optically visible tracers of star formation and feedback will enable the
first cloud-scale inventory of all stages in the star formation cycle across
the 19 galaxies in our sample. Previous work on $\lesssim\\!100$ pc scales
suggested that up to $\sim 60$% of molecular gas in galaxies may be devoid of
H$\alpha$ emission (e.g., Schruba et al., 2010; Onodera et al., 2010; Kreckel
et al., 2018; Kruijssen et al., 2019a; Schinnerer et al., 2019b; Chevance et
al., 2020a; Kim et al., 2022a; Pan et al., 2022). Yet highly resolved IR
observations show that truly non-star-forming clouds are scarce in our Galaxy
(Mooney & Solomon, 1988; Ginsburg et al., 2012) and Spitzer results suggest
that molecular clouds in Local Group galaxies spend up to half their lifetime
hosting only embedded massive star formation (Kim et al., 2021a). With PHANGS-
JWST, we will confirm whether there is a large reservoir of molecular gas
without star formation in nearby star-forming disk galaxies and, if so,
whether it due to a delayed onset (e.g. Klessen & Glover, 2016; Padoan et al.,
2017) or sustained suppression (e.g., Federrath et al., 2016; Semenov et al.,
2017; Meidt et al., 2018, 2020; Kruijssen et al., 2019b) of star formation.

PHANGS-JWST high resolution measurements will also enable important empirical
tests of cloud-scale models of star formation. These models predict the
integrated and per free-fall time star formation rate and efficiency of
individual clouds based on their density, turbulence, and dynamical state
(e.g., Padoan et al., 2017; Kim et al., 2021b), quantities that have already
been measured using the PHANGS-ALMA data (e.g., Sun et al., 2020, 2022).
Cloud-scale star formation models are widely used to interpret observations
(e.g., Barnes et al., 2017; Utomo et al., 2018) and implemented as sub-grid
models in galaxy simulations (e.g., Gensior et al., 2020; Kretschmer &
Teyssier, 2020). The models have never been rigorously tested beyond the Local
Group, however, since the rapid action of feedback prohibits linking optical
SFR estimates to the progenitor cloud’s gas mass, and hence the integrated
star formation rate per unit gas mass. Lacking cloud-by-cloud estimates of the
embedded star formation activity, studies in external galaxies have resorted
to using population averages, obtaining results that suggest—but cannot
currently prove—important inconsistencies with the models, such as inferred
star formation efficiencies that decrease with the gravitational boundedness
of the gas (e.g., Leroy et al., 2017; Schruba et al., 2019). The new PHANGS-
JWST data are essential for unambiguous cloud-scale tests of these
predictions.

The PHANGS-JWST papers in this Issue have begun to explore these questions.
Hassani et al. (subm.) study compact sources in F2100W, finding that nearly
all sources are compact star-forming regions. These regions show strong
association both with the molecular gas and H$\alpha$ emission. Liu et al.
(subm.) evaluate how strong heating in the IR from dusty star-forming regions
is connected to the density and temperature of the molecular ISM as seen by
ALMA. Leroy et al. (subm.a) use a large set of archival data to show that the
mid-IR has excellent capacity to trace the properties of the star-forming ISM.
This retrospective analysis provides context for the high resolution view
provided by PHANGS-JWST. Using the new data, Leroy et al. (subm.b) conduct a
correlation analysis of the mid-IR data, showing strong correlations with both
the H$\alpha$ and the CO in galaxies and these correlations are stronger than
the correlation between H$\alpha$ and CO at high resolution.

##  7 Data Products

Preliminary JWST image products for NGC 7496, IC 5332, NGC 628, NGC 1365, used
as the basis for the PHANGS-JWST First Results presented in this Volume, are
available at the Canadian Astronomy Data Centre as part of the PHANGS
archive121212https://www.canfar.net/storage/vault/list/phangs/RELEASES – Note
to be removed in proof: JWST data will be posted on acceptance.. This data
release includes stacked, mosaicked, calibrated images based on all PHANGS-
JWST data obtained through 2022 August: imaging in four NIRCam and four MIRI
filters for NGC 7496, NGC 628, NGC 1365, and in four MIRI filter for IC 5332.3
The processing of these data are subject to the caveats discussed in Section
4.8.

After the completion of the PHANGS-JWST observations, we will provide an
expanded set of image products for all 19 galaxies through the Mikulski
Archive for Space Telescopes (MAST) as well as at the CADC PHANGS archive. We
anticipate significant improvements in the processing and calibration to
address currently known issues (Sections 4 and 4.8) as well as those that
arise in future analyses. These later data releases will include the full
PHANGS-JWST imaging stack at native resolution together with products
convolved to match the angular resolution of the lowest resolution PHANGS-JWST
image (MIRI 21$\micron$) and other PHANGS datasets (e.g., maps from ALMA and
MUSE).

The PHANGS-JWST papers in this Issue give examples of the high-level science
products that would be valuable to be developed from JWST data in combination
with the rest of the PHANGS surveys.

PHANGS-JWST First Results on stellar clusters illustrate the potential (and
challenges) in using the data at their highest resolution (which at 2$\mu$m
surpasses the resolution of HST optical imaging) to develop catalogs of
embedded star clusters, to augment the PHANGS-HST star cluster and association
catalogs with photometry from 2-21$\mu$m, and to measure their physical
properties through SED fitting (Rodriguez et al., 2022; Whitmore et al.,
subm.; Hoyer et al., 2022). Catalogs of compact sources in the MIRI bands can
be produced to identify larger star-forming regions on scales comparable to
the HII regions observed with MUSE, and the giant molecular clouds resolved by
ALMA (Hassani et al., subm.; Schinnerer et al., subm.). As PHANGS-JWST
analysis matures, it will be possible to develop complete catalogs of dust-
enshrouded and visible stellar populations across multiple physics scales
(Larson et al., 2022), which include physical properties such as mass, age and
dust reddening, via SED fitting which is able to break degeneracies between
reddening and age (Whitmore et al., 2022).

PHANGS-JWST First Results on feedback show how catalogs of ISM bubbles and
shells can be produced (Watkins et al., subm.; Barnes et al., subm.) and used
in combination with catalogs of the young stellar populations to identify the
contribution of feedback and dynamical processes in the formation and
evolution of these structures. The importance of complete inventories of the
youngest stellar populations and feedback-driven ISM structures and of access
to the earliest embedded phase of star formation via mid-IR JWST imaging for
quantitative estimates of timescales, star formation efficiencies and feedback
parameters is also highlighted by Kim et al. (2022b); Watkins et al. (subm.);
Barnes et al. (subm.).

As shown in Section 5, JWST’s high resolution capabilities have provided the
most detailed maps of the diffuse ISM possible in nearby galaxies to-date
(Sandstrom et al., subm.a) to enable studies of local influences on heating
(Leroy et al., subm.b). Dust emission and absorption maps can be produced from
the combination of the PHANGS JWST, HST, VLT-MUSE, and ALMA datasets (e.g.,
Thilker et al., subm.; Meidt et al., subm.; Williams et al., subm.).

Finally, PHANGS-JWST First Results show how PAH features can be sensitive
diagnostics of ISM conditions through changing flux ratios. Using empirical
methods, Sandstrom et al. (subm.b) demonstrate how to isolate PAH features
from the stellar continuum using JWST bands, which can be developed into a
full set of PAH-only maps of the galaxies. Chastenet et al. (subm.a, subm.b)
use JWST data to find variation in PAH properties in the broader ISM, with
Egorov et al. (subm.) and Dale et al. (subm.) exploring H II regions and star
clusters respectively. These ratio measurements can be refined into
environmental diagnostics of properties like the metallicity and radiation
field.

##  8 Summary

We present the PHANGS-JWST Cycle 1 Treasury Program, one of the key
observational campaigns conducted by the Physics at High Angular resolution in
Nearby GalaxieS (PHANGS) collaboration (Schinnerer et al., 2019a). PHANGS-JWST
is a 112.6 hour program to image 19 nearby galaxies ($D<20~{}\mathrm{Mpc}$) in
eight filters from 2$\mu$m to 21$\mu$m using NIRCam (F200W, F300M, F335M,
F360M) and MIRI (F770W, F1000W, F1130W, and F2100W).

  1. 1.

We summarize the properties of the PHANGS-JWST nearby spiral galaxy sample,
which all have overlapping coverage from ALMA, HST, and VLT-MUSE and other
facilities (Section 2), and present our NIRCam and MIRI observing strategy
(Section 3).

  2. 2.

In Section 4, we present the modifications to the standard pipeline and
additional processing steps used to produce the data products for the First
Results analysis in this Issue. In particular, we describe changes to the
default astrometric alignment, a mitigation strategy for the $1/f$ noise in
NIRCam using Principal Component Analysis, and a background validation
strategy for MIRI data. Current limitations and uncertainties in this first
reduction are summarized.

  3. 3.

In Section 5 we provide an overview of the imaging of the first four targets
(NGC 0628, NGC 1365, NGC 7496, and IC 5332), compare our new JWST data to
existing Spitzer imaging, and compute limiting point source and surface
brightness sensitivities of the data. We highlight the value of the PHANGS-
JWST imaging for engaging the public in JWST discovery.

  4. 4.

In Section 6 we describe the science goals of our program and preview the
Letters in this Issue, while in Section 7 we describe currently available and
planned future image products, as well as higher level science products that
would be valuable to develop for the community.

As a public Cycle 1 Treasury program, the PHANGS-JWST dataset is designed to
broadly enable community science beyond the team science objectives outlined
here. Open access to such standard imaging observations early in the mission
is essential for learning how to best study star formation, stellar
populations, and dust in the local universe at high resolution in the infrared
with JWST. The early publication of our first results, initial lessons-learned
with data processing, and the challenges to be addressed as we expand analysis
to the full PHANGS-JWST sample, is intended to serve as a pathfinder to
facilitate forthcoming work.

## Software

## Acknowledgements

This work is conducted as part of the PHANGS
Collaboration131313http://phangs.org/.

This work is based on observations made with the NASA/ESA/CSA James Webb Space
Telescope. The data were obtained from the Mikulski Archive for Space
Telescopes at the Space Telescope Science Institute, which is operated by the
Association of Universities for Research in Astronomy, Inc., under NASA
contract NAS 5-03127 for JWST. These observations are associated with program
2107.

JCL acknowledges the W.M. Keck Institute for Space Studies (KISS) for
providing collaboration space, and its support of workshops which helped to
seed the work in this paper. In particular, we have benefited from discussions
at the KISS workshops: “Bridging the Gap: Observations and Theory of Star
Formation Meet on Large and Small Scales” in 2014 and “Star Formation in
Nearby Galaxies with JWST” in 2017.

This paper makes use of the following ALMA data: ADS/JAO.ALMA#2012.1.00650.S,
ADS/JAO.ALMA#2015.1.00956.S, ADS/JAO.ALMA#2017.1.00886.L,
ADS/JAO.ALMA#2018.1.01651.S. ALMA is a partnership of ESO (representing its
member states), NSF (USA) and NINS (Japan), together with NRC (Canada), MOST
and ASIAA (Taiwan), and KASI (Republic of Korea), in cooperation with the
Republic of Chile. The Joint ALMA Observatory is operated by ESO, AUI/NRAO and
NAOJ.

Based on observations collected at the European Southern Observatory under ESO
programmes 094.C-0623 (PI: Kreckel), 095.C-0473, 098.C-0484 (PI: Blanc),
1100.B-0651 (PHANGS-MUSE; PI: Schinnerer), as well as 094.B-0321 (MAGNUM; PI:
Marconi), 099.B-0242, 0100.B-0116, 098.B-0551 (MAD; PI: Carollo) and
097.B-0640 (TIMER; PI: Gadotti).

MB acknowledges support from FONDECYT regular grant 1211000 and by the ANID
BASAL project FB210003. G.A.B. acknowledges the support from ANID Basal
project FB210003. E.C. acknowledges support from ANID Basal projects ACE210002
and FB210003. KG is supported by the Australian Research Council through the
Discovery Early Career Researcher Award (DECRA) Fellowship DE220100766 funded
by the Australian Government.

MC gratefully acknowledges funding from the DFG through an Emmy Noether
Research Group (grant number CH2137/1-1). COOL Research DAO is a Decentralized
Autonomous Organization supporting research in astrophysics aimed at
uncovering our cosmic origins. JMDK gratefully acknowledges funding from the
ERC under the European Union’s Horizon 2020 research and innovation programme
via the ERC Starting Grant MUSTANG (grant agreement number 714907).

ADB acknowledges support by NSF-AST2108140 and award JWST-GO-02107.008-A.

ATB, IB, JdB, FB would like to acknowledge funding from the European Research
Council (ERC) under the European Union’s Horizon 2020 research and innovation
programme (grant agreement No.726384/Empire)

CE gratefully acknowledges funding from the Deutsche Forschungsgemeinschaft
(DFG) Sachbeihilfe, grant number BI1546/3-1.

RSK, SCOG, MS and EJW acknowledge the funding provided by DFG in SFB 881 “The
Milky Way System”, subprojects A1, B1, B2, B8, and P1 – funding-ID 138713538).
RSK is thankful for support from the ERC via the ERC Synergy Grant “ECOGAL”
(project ID 855130), from the Heidelberg Cluster of Excellence (EXC 2181 -
390900948) “STRUCTURES”, funded by the German Excellence Strategy, and from
the German Ministry for Economic Affairs and Climate Action in project “MAINN”
(funding ID 50OO2206).

JK gratefully acknowledges funding from the Deutsche Forschungsgemeinschaft
(DFG, German Research Foundation) through the DFG Sachbeihilfe (grant number
KR4801/2-1).

KK, OE, JL and FS gratefully acknowledge funding from the Deutsche
Forschungsgemeinschaft (DFG, German Research Foundation) in the form of an
Emmy Noether Research Group (grant number KR4598/2-1, PI Kreckel).

JS acknowledges support by the Natural Sciences and Engineering Research
Council of Canada (NSERC) through a Canadian Institute for Theoretical
Astrophysics (CITA) National Fellowship.

SD is supported by funding from the European Research Council (ERC) under the
European Union’s Horizon 2020 research and innovation programme (grant
agreement no. 101018897 CosmicExplorer).

HAP acknowledges support by the National Science and Technology Council of
Taiwan under grant 110-2112-M-032-020-MY3.

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