# The JWST Early Release Science Program for the Direct Imaging & Spectroscopy
of Exoplanetary Systems

Sasha Hinkley University of Exeter, Astrophysics Group, Physics Building,
Stocker Road, Exeter, EX4 4QL, UK.  Aarynn L. Carter Department of Astronomy &
Astrophysics, University of California, Santa Cruz, CA 95064, USA  Shrishmoy
Ray University of Exeter, Astrophysics Group, Physics Building, Stocker Road,
Exeter, EX4 4QL, UK.  Andrew Skemer  Department of Astronomy & Astrophysics,
University of California, Santa Cruz, CA 95064, USA  Beth Biller  SUPA,
Institute for Astronomy, The University of Edinburgh, Royal Observatory,
Blackford Hill, Edinburgh, EH9 3HJ, UK  Elodie Choquet  Aix Marseille Univ.,
CNRS, CNES, LAM, Marseille, France  Maxwell A. Millar-Blanchaer Department of
Physics, University of California, Santa Barbara, Santa Barbara, CA, USA
Stephanie Sallum  Department of Physics and Astronomy, University of
California, Irvine, Irvine, CA, USA  Brittany Miles  Department of Astronomy &
Astrophysics, University of California, Santa Cruz, CA 95064, USA  Niall
Whiteford  Department of Astrophysics, American Museum of Natural History,
Central Park West at 79th Street, NY 10024, USA  Polychronis Patapis Institute
for Particle Physics & Astrophysics, ETH Zurich, 8092 Zurich, Switzerland
Marshall Perrin  Space Telescope Science Institute, 3700 San Martin Drive,
Baltimore, MD 21218, USA  Laurent Pueyo  Space Telescope Science Institute,
3700 San Martin Drive, Baltimore, MD 21218, USA  Glenn Schneider  Steward
Observatory, University of Arizona, 933 N. Cherry Ave, Tucson, AZ 85721-0065
USA  Karl Stapelfeldt Jet Propulsion Laboratory, California Institute of
Technology, M/S 321-100, 4800 Oak Grove Drive, Pasadena, CA 91109, USA  Jason
Wang  Department of Astronomy, California Institute of Technology, Pasadena,
CA 91125, USA  Kimberly Ward-Duong Department of Astronomy, Smith College,
Northampton MA 01063, USA  Space Telescope Science Institute, 3700 San Martin
Drive, Baltimore, MD 21218, USA  Brendan P. Bowler  Department of Astronomy,
The University of Texas at Austin, 2515 Speedway Boulevard Stop C1400, Austin,
TX 78712, USA  Anthony Boccaletti  LESIA, Observatoire de Paris, Université
PSL, CNRS, Sorbonne Université, Université Paris Cité, 5 place Jules Janssen,
92195 Meudon, France  Julien Girard  Space Telescope Science Institute, 3700
San Martin Drive, Baltimore, MD 21218, USA  Dean Hines  Space Telescope
Science Institute, 3700 San Martin Drive, Baltimore, MD 21218, USA  Paul Kalas
Department of Astronomy, University of California at Berkeley, CA 94720, USA
Jens Kammerer Space Telescope Science Institute, 3700 San Martin Drive,
Baltimore, MD 21218, USA  Pierre Kervella LESIA, Observatoire de Paris,
Université PSL, CNRS, Sorbonne Université, Université Paris Cité, 5 place
Jules Janssen, 92195 Meudon, France  Jarron Leisenring Steward Observatory,
University of Arizona, 933 N. Cherry Ave, Tucson, AZ 85721-0065 USA  Eric
Pantin  IRFU/DAp Département D’Astrophysique CE Saclay, Gif-sur-Yvette, France
Yifan Zhou  Department of Astronomy, The University of Texas at Austin, 2515
Speedway Boulevard Stop C1400, Austin, TX 78712, USA  Michael Meyer
Department of Astronomy, University of Michigan, Ann Arbor, MI 48109, USA
Michael C. Liu Institute of Astronomy, University of Hawaii, 2860 Woodlawn
Drive, Honolulu, HI 96822, USA  Mickael Bonnefoy  Université Grenoble Alpes /
CNRS, Institut de Planétologie et d’Astrophysique de Grenoble, 38000 Grenoble,
France  Thayne Currie  NASA-Ames Research Center, Moffett Field, California,
USA  Michael McElwain NASA-Goddard Space Flight Center, Greenbelt, MD, USA
Stanimir Metchev  Department of Physics and Astronomy, Centre for Planetary
Science and Exploration, The University of Western Ontario, London, ON N6A
3K7, Canada  Mark Wyatt  Institute of Astronomy, University of Cambridge,
Madingley Road, Cambridge CB3 0HA, UK  Olivier Absil Space Sciences,
Technologies, and Astrophysics Research (STAR) Institute, University of Liège,
B-4000 Liège Belgium  Jea Adams  Center for Astrophysics $|$ Harvard &
Smithsonian, 60 Garden St, Cambridge, MA 02138, USA  Travis Barman  Lunar &
Planetary Laboratory, University of Arizona, Tucson, AZ 85721, USA  Isabelle
Baraffe  University of Exeter, Astrophysics Group, Physics Building, Stocker
Road, Exeter, EX4 4QL, UK.  École Normale Supérieure, Lyon, CRAL (UMR CNRS
5574), Université de Lyon, France  Mariangela Bonavita School of Physical
Sciences, The Open University, Walton Hall, Milton Keynes, MK7 6AA  Mark Booth
Astrophysikalisches Institut und Universitätssternwarte, Friedrich-Schiller-
Universität Jena, Schillergässchen 2-3, D-07745 Jena, Germany  Marta Bryan
Department of Astronomy, University of California at Berkeley, CA 94720, USA
Gael Chauvin  Université Grenoble Alpes / CNRS, Institut de Planétologie et
d’Astrophysique de Grenoble, 38000 Grenoble, France  Christine Chen  Space
Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21218, USA
Camilla Danielski  Instituto de Astrofísica de Andalucía, CSIC, Glorieta de la
Astronomía, 18008, Granada, Spain  AIM, CEA, CNRS, Universitè Paris-Saclay,
Universitè Paris Diderot, Sorbonne Paris Citè, F-91191 Gif-sur-Yvette, France
Matthew De Furio Department of Astronomy, University of Michigan, Ann Arbor,
MI 48109, USA  Samuel M. Factor Department of Astronomy, The University of
Texas at Austin, 2515 Speedway Boulevard Stop C1400, Austin, TX 78712, USA
Jonathan J. Fortney Department of Astronomy & Astrophysics, University of
California, Santa Cruz, CA 95064, USA  Carol Grady  Eureka Scientific, 2452
Delmer St., Suite 100, Oakland CA 96402-3017, USA.  Alexandra Greenbaum IPAC,
Mail Code 100-22, Caltech, 1200 E. California Blvd., Pasadena, CA 91125, USA
Thomas Henning  Max-Planck-Institut für Astronomie, Königstuhl 17, 69117
Heidelberg, Germany  Markus Janson Department of Astronomy, Stockholm
University, 10691, Stockholm, Sweden  Grant Kennedy  Department of Physics,
University of Warwick, Coventry CV4 7AL, UK  Matthew Kenworthy Leiden
Observatory, Leiden University, Postbus 9513, 2300 RA Leiden, The Netherlands
Adam Kraus  Department of Astronomy, The University of Texas at Austin, 2515
Speedway Boulevard Stop C1400, Austin, TX 78712, USA  Masayuki Kuzuhara
Astrobiology Center, 2-21-1, Osawa, Mitaka, Tokyo, 181-8588, Japan  National
Astronomical Observatory of Japan, 2-21-1 Osawa, Mitaka, Tokyo 181-8588, Japan
Pierre-Olivier Lagage  AIM, CEA, CNRS, Universitè Paris-Saclay, Universitè
Paris Diderot, Sorbonne Paris Citè, F-91191 Gif-sur-Yvette, France  Anne-Marie
Lagrange  Université Grenoble Alpes / CNRS, Institut de Planétologie et
d’Astrophysique de Grenoble, 38000 Grenoble, France  Ralf Launhardt  Max-
Planck-Institut für Astronomie, Königstuhl 17, 69117 Heidelberg, Germany
Cecilia Lazzoni  University of Exeter, Astrophysics Group, Physics Building,
Stocker Road, Exeter, EX4 4QL, UK.  INAF–Osservatorio Astronomico di Padova,
Vicolo dell’Osservatorio 5, I-35122, Padova, Italy  James Lloyd  Department of
Astronomy, Cornell University, Ithaca NY, USA  Sebastian Marino  Jesus
College, University of Cambridge, Jesus Lane, Cambridge CB5 8BL, UK  Institute
of Astronomy, University of Cambridge, Madingley Road, Cambridge CB3 0HA, UK
Mark Marley  Lunar & Planetary Laboratory, University of Arizona, Tucson, AZ
85721, USA  Raquel Martinez Department of Physics and Astronomy, University of
California, Irvine, Irvine, CA, USA  Christian Marois Herzberg Astronomy &
Astrophysics Research Centre, National Research Council of Canada, 5071 West
Saanich Road, Victoria, BC V9E 2E7, Canada  Department of Physics & Astronomy,
University of Victoria, 3800 Finnerty Road, Victoria, BC V8P 5C2, Canada
Brenda Matthews Herzberg Astronomy & Astrophysics Research Centre, National
Research Council of Canada, 5071 West Saanich Road, Victoria, BC V9E 2E7,
Canada  Department of Physics & Astronomy, University of Victoria, 3800
Finnerty Road, Victoria, BC V8P 5C2, Canada  Elisabeth C. Matthews
Observatoire Astronomique de l’Université de Genève, 51 Ch. Pegasi, 1290
Versoix, Switzerland  Dimitri Mawet  Department of Astronomy, California
Institute of Technology, Pasadena, CA 91125, USA  Jet Propulsion Laboratory,
California Institute of Technology, M/S 321-100, 4800 Oak Grove Drive,
Pasadena, CA 91109, USA  Mark Phillips  Institute of Astronomy, University of
Hawaii, 2860 Woodlawn Drive, Honolulu, HI 96822, USA  Simon Petrus  Université
Grenoble Alpes / CNRS, Institut de Planétologie et d’Astrophysique de
Grenoble, 38000 Grenoble, France  Núcleo Milenio Formación Planetaria - NPF,
Universidad de Valparaíso, Av. Gran Bretaña 1111, Valparaíso, Chile  Sascha P.
Quanz Institute for Particle Physics & Astrophysics, ETH Zurich, 8092 Zurich,
Switzerland  Andreas Quirrenbach  Landessternwarte,Zentrum für Astronomie der
Universität Heidelberg, Königstuhl 12, 69117 Heidelberg, Germany  Julien
Rameau  Université Grenoble Alpes / CNRS, Institut de Planétologie et
d’Astrophysique de Grenoble, 38000 Grenoble, France  Isabel Rebollido Space
Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21218, USA
Emily Rickman European Space Agency (ESA), ESA Office, Space Telescope Science
Institute, 3700 San Martin Drive, Baltimore, MD 21218, USA  Matthias Samland
Max-Planck-Institut für Astronomie, Königstuhl 17, 69117 Heidelberg, Germany
B. Sargent Space Telescope Science Institute, 3700 San Martin Drive,
Baltimore, MD 21218, USA  Center for Astrophysical Sciences, The William H.
Miller III Department of Physics and Astronomy, Johns Hopkins University,
Baltimore, MD 21218, USA  Joshua E. Schlieder  Exoplanets & Stellar
Astrophysics Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Anand Sivaramakrishnan Space Telescope Science Institute, 3700 San Martin
Drive, Baltimore, MD 21218, USA  Jordan Stone US Naval Research Laboratory,
Remote Sensing Division, 4555 Overlook Ave SW, Washington, DC 20375  Motohide
Tamura Department of Astronomy, The University of Tokyo, 7-3-1, Hongo, Bunkyo-
ku, Tokyo 113-0033, Japan  Astrobiology Center, 2-21-1, Osawa, Mitaka, Tokyo,
181-8588, Japan  National Astronomical Observatory of Japan, 2-21-1 Osawa,
Mitaka, Tokyo 181-8588, Japan  Pascal Tremblin  Maison de la Simulation, CEA,
CNRS, Univ. Paris-Sud, UVSQ, Universitè Paris-Saclay, F-91191 Gif-sur-Yvette,
France  Taichi Uyama  Infrared Processing and Analysis Center, California
Institute of Technology, 1200 E. California Blvd., Pasadena, CA 91125, USA
NASA Exoplanet Science Institute, Pasadena, CA 91125, USA  Malavika Vasist
Space Sciences, Technologies, and Astrophysics Research (STAR) Institute,
University of Liège, B-4000 Liège Belgium  Arthur Vigan Aix Marseille Univ.,
CNRS, CNES, LAM, Marseille, France  Kevin Wagner  Steward Observatory,
University of Arizona, 933 N. Cherry Ave, Tucson, AZ 85721-0065 USA  NASA
Hubble Fellowship Program - Sagan Fellow  Marie Ygouf  Jet Propulsion
Laboratory, California Institute of Technology, M/S 321-100, 4800 Oak Grove
Drive, Pasadena, CA 91109, USA

###### Abstract

The direct characterization of exoplanetary systems with high contrast imaging
is among the highest priorities for the broader exoplanet community. As large
space missions will be necessary for detecting and characterizing exo-Earth
twins, developing the techniques and technology for direct imaging of
exoplanets is a driving focus for the community. For the first time, JWST will
directly observe extrasolar planets at mid-infrared wavelengths beyond 5
$\mu$m, deliver detailed spectroscopy revealing much more precise chemical
abundances and atmospheric conditions, and provide sensitivity to analogs of
our solar system ice-giant planets at wide orbital separations, an entirely
new class of exoplanet. However, in order to maximise the scientific output
over the lifetime of the mission, an exquisite understanding of the
instrumental performance of JWST is needed as early in the mission as
possible. In this paper, we describe our 54-hour Early Release Science Program
that will utilize all four JWST instruments to extend the characterisation of
planetary mass companions to $\sim$15 $\mu$m as well as image a circumstellar
disk in the mid-infrared with unprecedented sensitivity. Our program will also
assess the performance of the observatory in the key modes expected to be
commonly used for exoplanet direct imaging and spectroscopy, optimize data
calibration and processing, and generate representative datasets that will
enable a broad user base to effectively plan for general observing programs in
future Cycles.

##  1 Introduction

In the relatively short span of a quarter century, astronomers have
transitioned from speculating about the prevalence of exoplanetary systems to
discovering thousands, and it is now clear that most stars host planetary
systems (e.g., Cassan et al., 2012; Dressing & Charbonneau, 2013). The vast
majority of these, however, have been identified only indirectly via the
transit and radial velocity detection methods, and lie at close orbital
separations from their host stars.

As the indirect transit and Doppler detection methods are inherently much less
sensitive to wide-separation planets with long orbital periods, the direct
imaging technique (e.g., Bowler, 2016) will be the only approach to fully
define the outermost architectures of planetary systems ($\sim$ 10 to hundreds
of AU), and provide a more complete understanding of the true frequency of
planetary mass companions to nearby stars (e.g., Nielsen et al., 2019; Vigan
et al., 2021). In the last decade, imaging observations mostly at wavelengths
of $\lesssim$2 $\mu$m have produced numerous scattered light images of
circumstellar disks (Esposito et al., 2020), and directly revealed $\sim$
10-20, young ($\lesssim$ 50 Myr), massive ($\gtrsim$1 MJup) planets (e.g.,
Marois et al., 2008; Lagrange et al., 2010; Rameau et al., 2013; Chauvin et
al., 2017; Macintosh et al., 2015; Bohn et al., 2020; Janson et al., 2021).
Direct Imaging is also the only technique that will be capable of
characterising exoplanets at orbital radii $\gtrsim$0.5 AU, as transit
transmission spectroscopy (e.g., Sing et al., 2016) requires multiple transits
to achieve a strong signal for Earth-mass planets on Earth-like orbits around
Sun-like stars (Morley et al., 2017) resulting in prohibitively long time
baselines. It is also the only technique projected to provide the in-depth
characterization of such exo-Earths (e.g., The LUVOIR Team, 2019; Quanz et
al., 2021).

By spatially separating the light of the host star and the extremely faint
planet, the direct imaging technique is also naturally suited to direct
spectroscopy of planets themselves, allowing detailed characterization (e.g.,
Bowler et al., 2010; Macintosh et al., 2015; De Rosa et al., 2016; Chauvin et
al., 2017; Currie et al., 2018). In addition to providing information on
atmospheric properties and compositions (e.g., Hinkley et al., 2015a; Kammerer
et al., 2021), the direct imaging technique can provide powerful estimations
of fundamental parameters, e.g. luminosity, effective temperature, and orbital
properties (Gravity Collaboration et al., 2019). The characterization power of
the direct imaging method becomes even more pronounced when combined with
other exoplanet detection techniques, such as precise radial velocity
monitoring or astrometry (e.g., Nowak et al., 2020; Lagrange et al., 2020;
Wang et al., 2021a; Lacour et al., 2021), to more fully constrain parameters
(e.g. planet mass) that are difficult to ascertain with one technique alone.

Going forward, direct imaging will ultimately provide direct, high-resolution
(R$\sim$30,000-100,000) spectra of exoplanet atmospheres (e.g., Snellen et
al., 2015; Mawet et al., 2018; Vigan et al., 2018; Otten et al., 2021; Wang et
al., 2021b). Obtaining photons directly from the atmosphere of the exoplanet
itself will allow us to apply to exoplanetary atmospheres all of the
spectroscopic techniques that have been applied to stars and brown dwarfs over
the last century. Indeed, all of the detailed information that can be
retrieved using high-resolution spectroscopy of stars and brown dwarfs (e.g.
chemical abundances, compositions, thermodynamic conditions, Doppler
tomography) will also be directly obtained for exoplanetary atmospheres,
allowing much more precise interpretation of the spectra (Konopacky et al.,
2013; Barman et al., 2015).

At the same time, the direct imaging technique has been especially prolific at
imaging both very young primordial (protoplanetary) disks as well as dusty
circumstellar debris disks. Direct images of these disk structures uniquely
allow the study of the dynamical interactions between circumstellar disks, and
the planets that are dynamically sculpting them (Choquet et al., 2016;
Matthews et al., 2017; Avenhaus et al., 2018; Esposito et al., 2020). Upcoming
space-based missions, the advent of the 30-40m telescopes, as well as updates
to existing ground-based high contrast imaging platforms (e.g. MagAOX, GPI
2.0, SPHERE+, SCExAO; Males et al., 2018; Chilcote et al., 2020) will vastly
improve exoplanet direct detection capabilities. Consequently, the development
of the direct imaging technique will be a major priority for the broader
exoplanet community going forward, and is a key mode for current and future
space missions including JWST (Gardner et al., 2006).

###  1.1 JWST

JWST will be transformative for characterizing exoplanet atmospheres at mid-
infrared and longer wavelengths (3-28 $\mu$m). In addition to a handful of
spectra of free-floating brown dwarfs from The Spitzer Space Telescope and
Akari missions (Cushing et al., 2006; Sorahana & Yamamura, 2012), direct
images of exoplanets from 3 to 5 $\mu$m exist (e.g., Galicher et al., 2011;
Skemer et al., 2014, 2016), but the extremely high telluric background has
imposed a strong sensitivity barrier. Transit transmission spectroscopy of
transiting “Hot Jupiter” exoplanets using the Spitzer Space Telescope exist
(e.g., Grillmair et al., 2007, 2008), but this class of planet has very
different properties than young planets studied via direct imaging, or even
solar system giant planets. Even further, despite some tantalizing early
detections (e.g., Wagner et al., 2021), exoplanets have never been directly
observed at wavelengths $\gtrsim$5 $\mu$m.

Table 1: A Table showing the primary science targets to be observed within this ERS program Target | Host Star SpT | Distance (pc) | Age (Myr) | Angular Separation (′′) | Mass (MJup) | References
---|---|---|---|---|---|---
HIP 65426b | A2V | 109.2$\pm$0.8 | 14$\pm$4 | 0.83 | 6-12 | Chauvin et al. (2017); Cheetham et al. (2019)
VHS 1256 b | M7.5$\pm$0.5 | 22.2${}^{+1.1}_{-1.2}$ | 150-300 | 8.06 $\pm$0.03 | 19$\pm$5 | Gauza et al. (2015); Dupuy et al. (2020)
HD 141569A | A2 | 110.6$\pm$0.5 | 5$\pm$3 | - | - | Weinberger et al. (2000); Clampin et al. (2003)

To date, much of the instrumentation dedicated to the purpose of high contrast
imaging (Hinkley et al., 2011b; Macintosh et al., 2018; Beuzit et al., 2019)
has operated at wavelengths $\lesssim$ 3$\mu$m. However, in addition to
observing planets closer to the peak of their thermal emission, obtaining
exoplanet photometry and spectroscopy at 3-5 $\mu$m is a key probe of
atmospheric compositions, and dramatically improves differentiation between
equilibrium and disequilibrium atmospheric chemistry (Skemer et al., 2012;
Phillips et al., 2020; Miles et al., 2020). As well as clear markers of
composition, many of the key atmospheric processes that shape the spectra of
planetary mass companions, e.g. vertical atmospheric mixing (Hubeny & Burrows,
2007; Barman et al., 2011), cloud physics, and the presence of patchy cloud
structures (Currie et al., 2011) have signatures at the wavelengths observable
by JWST. These sets of constraints cannot be entirely accessed through
observations at 1-2 $\mu$m alone. Incorporating data at long wavelengths can
dramatically help to break the persistent degeneracies (see e.g., Chabrier et
al., 2007) in the interpretation of exoplanet spectra that might be in fact
driven by enhanced elemental abundances, or various physical processes such as
vertical atmospheric mixing (Konopacky et al., 2013). While JWST will not have
the angular resolution, nor the inner working angles, of 8-10m ground-based
telescopes operating in the near-infrared (Perrin et al., 2018), the extremely
precise flux measurements afforded by the stability and low thermal background
at the second Lagrange point will allow the differentiation of dominant
exoplanet atmospheric chemistries, as well as derive circumstellar disk
compositions.

The long wavelength coverage offered by JWST will provide abundances of the
expected molecules in exoplanet atmospheres (CH4, CO, CO2, H2O and NH3), using
multiple features and a broad wavelength range to distinguish between clouds
and molecular absorption. Calculating abundances for the major oxygen and
carbon-bearing species will provide measurements of an object’s C/O ratio.
Whether or not the C/O ratio of a companion matches that of its star may
provide clues regarding the companion formation mechanism, and may help to
distinguish zones of formation within the protoplanetary disk (Öberg et al.,
2011; Cridland et al., 2016, 2019; Mollière et al., 2022). However, the
utility and ease of a single metric such as the C/O ratio to capture the
complex physics (e.g., Fortney et al., 2013; Cridland et al., 2016;
Madhusudhan et al., 2017) inherent in the planet formation process has been
debated. For example, some works (e.g., Mordasini et al., 2016) have
demonstrated how limitations in our understanding of disk chemistry, the
planet formation process and migration can limit the utility of this metric.

Differences in the near-infrared Spectral Energy Distributions (SEDs) of
exoplanets and field brown dwarfs (Chauvin et al., 2004; Marois et al., 2008)
suggest that their atmospheres are not the same, perhaps due to differences in
surface gravity (Marley et al., 2012), stellar insolation (Zahnle et al.,
2016), or cloud features. This difference is readily apparent in color-
magnitude diagrams, where planetary-mass objects tend to be much redder than
the field brown dwarf population (Faherty et al., 2016; Liu et al., 2016).
Silicate and iron clouds are typically used to explain the colors of red
(L-type) brown dwarfs, but the colors can also be explained by thermo-chemical
instabilities (Tremblin et al., 2015, 2016) or the viewing angle (Vos et al.,
2017). Although there have been tentative detections of silicates through an
8-12 $\mu$m absorption feature in field brown dwarfs with Spitzer (Cushing et
al., 2006; Looper et al., 2008), extremely red exoplanets might have stronger
features that point towards the cloud hypothesis. Moving forward, it will be
essential to gather empirical constraints on these SEDs in order to
effectively model the sensitivity of JWST to these substellar objects in
future surveys.

Figure 1: Simulated coronagraphic images of the directly imaged exoplanet HIP
65426b (Chauvin et al., 2017) in the 4.1$\mu$m NIRCam filter (F410M, top row),
and the 11.4$\mu$m MIRI filter (F1140C, bottom row) using the simulation
methods described in Carter et al. (2021a). In each set of figures, the left
image shows the coronagraphic point spread function for each bandpass, while
the middle and right panels show subsequent image post-processing employing
Angular Differential Imaging (ADI) or a combination of ADI with Reference
Differential Imaging (labelled “ADI+RDI”) that utilizes a small library of
images obtained using the small grid dither strategy (Lajoie et al., 2016).

JWST utilizes four science instruments operating in the infrared, with three
of these operating at wavelengths shorter than 5 $\mu$m. NIRCam (Rieke et al.,
2005; Beichman et al., 2012) and NIRSpec (Bagnasco et al., 2007; Jakobsen et
al., 2022) are the primary near-infrared imager and spectrograph,
respectively, but only NIRCam and MIRI offer a coronagraphic mode. NIRCam
offers a series of coronagraphic occulting masks (Green et al., 2005; Krist et
al., 2007) and preflight estimates of the coronagraphic performance suggest
that contrasts of $\sim$10${}^{-4}-10^{-5}$ within $\sim$1′′ will be achieved.
NIRSpec will have numerous capabilities for spectroscopically characterizing
the atmospheres of extrasolar planets (e.g., Birkmann et al., 2022), including
obtaining direct spectroscopy for widely separated companions. NIRISS (Doyon
et al., 2012) is equipped with an Aperture Masking Interferometer mode (“AMI,”
Sivaramakrishnan et al., 2012; Artigau et al., 2014; Soulain et al., 2020)
with the goal of obtaining moderate contrast (10${}^{-3}-10^{-4}$) at
separations comparable to (and within) the JWST diffraction limit of 130-150
mas at $\sim$4-5 $\mu$m. MIRI (Rieke et al., 2015; Wright et al., 2015) is the
only instrument capable of observations at wavelengths longer than 5 $\mu$m,
and is equipped with four-quadrant phase mask (FQPM) coronagraphs (Rouan et
al., 2000; Baudoz et al., 2006) operating at 10.65, 11.60, and 15.5 $\mu$m, as
well as a classical Lyot coronagraph operating at 23 $\mu$m (Boccaletti et
al., 2005; Cavarroc et al., 2008; Boccaletti et al., 2015). In addition to
offering very good contrast performance at small inner working angles of
${\sim}\lambda/D$ at wavelengths of 10-16 $\mu$m, the 4QPM coronagraph design
was chosen during the MIRI design phase as it was one of the few coronagraphs
that had been validated both in laboratory testing as well as on sky. Below we
describe some of the potentially transformative science that will be done with
JWST coronagraphy for identifying wide-separation giant planets, as well as
characterizing circumstellar debris disks.

###  1.2 A New Class of Planets: Wide-Separation Saturn & Neptune Analogues

The exquisite thermal infrared sensitivity afforded by JWST means that it will
be possible for the first time to directly image sub-Jupiter mass planets, an
entirely new class of directly imaged planet (Beichman et al., 2010; Carter et
al., 2021b). Previous studies (e.g., Janson et al., 2015) have demonstrated
the gain in sensitivity when using space-based imaging at infrared
wavelengths, similar to those to be used with the JWST NIRCam instrument. In
the background-limited regime within the field of view, JWST will have 50
times the sensitivity of the Gemini Planet Imager (Macintosh et al., 2015)
operating at 2$\mu$m, and 500 times the sensitivity of the Keck/NIRC2
instrument at 4$\mu$m (Perrin et al., 2018). Analogues to our own solar system
ice-giant planets should have very cold temperatures, and thus the peak
wavelength of their emission will be shifted out of the near- and mid-infrared
(1 to $\sim$5 $\mu$m) into to the thermal infrared ($\gtrsim$10 $\mu$m). The
very high telluric background from the ground imposes a stark sensitivity
limit, even with new technologies (e.g. adaptive secondary mirrors). Thus, in
addition to potentially directly detecting a handful of planets detected by
the radial velocity technique (e.g., Brande et al., 2020), the superior
thermal infrared sensitivity of JWST will allow imaging for the first time of
wide, young Saturn analogues in many cases down to masses of 0.2 MJup. In the
most favorable cases, JWST will be capable of directly imaging wide young
Neptune analogues (Beichman et al., 2010; Schlieder et al., 2016; Carter et
al., 2021b).

Early results from direct imaging surveys (e.g., Nielsen et al., 2019; Wagner
et al., 2019; Vigan et al., 2021) suggest that an abundant population of lower
mass planetary companions exists at wide orbital separations (tens to hundreds
of AU). Extrapolating the mass distribution power laws derived by the GPI
campaign (Nielsen et al., 2019) indicates several 0.1-1.0 MJup planets per
star reside at separations of 10-100 AU when considering the broadest range of
host star masses (0.2-5 M⊙). Along the same lines, Fernandes et al. (2019)
predict a factor of $\sim$2$-$4 increase in occurrence rate between 10$-$100
AU for 0.1$-$13 $M_{\mathrm{Jup}}$ planets compared to 1$-$13
$M_{\mathrm{Jup}}$ planets. Recent statistical arguments from microlensing
efforts (e.g., Poleski et al., 2021) are consistent with this, suggesting
$\sim$1.5 ice giant planets ($\lesssim$1 MJup) per star reside between 5-15
AU. The sensitivity of JWST to such a wide range of masses (several MJup down
to $\lesssim$ 1 MJup) has the potential to associate sub-populations of
planets to various planet formation mechanisms. For example, it is expected
that the gravitational instability model (Kratter et al., 2010; Forgan & Rice,
2013) will preferentially form planets with masses $\gtrsim$3 MJup, while
lower mass planets on such wide orbits are challenging to form via this model,
and instead suggest a mechanism more akin to a model based on accretion of
solids in a protoplanetary disk (e.g., Pollack et al., 1996). Therefore,
measuring the mass and separation functions of wide-orbit, low-mass companions
is also a powerful constraint on the specifics of a formation mechanism based
on core accretion.

###  1.3 Studies of Circumstellar Disks with JWST

Exoplanets and circumstellar debris disks go hand-in-hand: the gravitational
influence of giant planets in these systems shapes the belts of debris,
generated by colliding remnant planetesimals (Wyatt, 2008; Hughes et al.,
2018), can exist in stable orbits around a star, and sculpts the dust
distribution through scattering, secular interactions and resonances (Chiang
et al., 2009; Wyatt, 2003; Morrison & Malhotra, 2015). Indeed, the structure
of debris disks reveal dynamical relations between planets and their
circumstellar environment, and any evaluation of JWST’s ability to
characterize planetary systems must also include its ability to characterize
diffuse, extended emission from circumstellar debris disks.

Figure 2: Top panel: The spectral coverage of HIP 65426b we will achieve using
NIRCam and MIRI Coronagraphy (red points), compared to the existing VLT-SPHERE
and NACO photometric points (Cheetham et al., 2019) shown in black/grey, and
overlaid with a model ($T_{\rm eff}$=1340 K, log($g$)=3.95) computed using the
tools described in Tremblin et al. (2015). The simulated JWST data points
reflect the integral of the flux density over the filter bandpass, divided by
the width of the bandpass. Bottom panel: Two example synthetic atmosphere
models (assuming the same physical parameters for each model) taken from
Phillips et al. (2020) illustrating the effects of non-equilibrium chemistry
(orange curve) compared to equilibrium chemistry (blue). Also shown are the
photometric points from NIRCam and MIRI we will obtain, which will easily have
the precision to differentiate between the two models, particularly at 3.56
$\mu$m.

With nearly an order of magnitude greater angular resolution than Spitzer,
JWST will allow resolved imaging of asteroid belts in other systems for the
first time. The very large, contiguous fields-of-view of NIRCam and MIRI,
coupled with their high sensitivity, will be critically important for studies
of debris disks, providing access to regions at high contrast beyond the
control zones of ground-based Adaptive Optics systems (e.g., $\sim$0.5-1.0′′,
Matthews et al., 2018; Hinkley et al., 2021). H2O ice features are strong in
Saturn’s rings and Kuiper belt objects (e.g., Filacchione et al., 2014) and
are expected in debris disks, but sufficiently sensitive observations have
been impossible from the ground. In addition to constraining the dust
scattering properties, NIRCam observations of debris disks may potentially
directly detect the planets responsible for driving the ring structures, or
provide very sensitive upper limits on their presence. Due to very high CO2
opacity in Earth’s atmosphere, debris disks have never been imaged
specifically at 15 $\mu$m—on the steeply rising Wien side of the disk thermal
emission. Spatially resolved 15 $\mu$m debris disk observations will constrain
the disk radial temperature profiles (and thus dust grain sizes), and
disentangle the unresolved excess emission close to the star from the extended
emission in the outer, bright debris structures. JWST is well poised to
address these issues, but the best observing practices need to be established
quickly.

In this paper, we describe our upcoming 54-hour Early Release Science (ERS)
Program dedicated to the direct imaging and spectroscopy of extrasolar
planetary systems. In §2 we provide our rationale for an ERS program dedicated
to this task, and in §3 we provide a detailed overview of the observational
strategy of our program. In §4 we describe our plans for rapid data processing
as well as the Science Enabling Products (“SEPs”) our team will deliver
shortly after the program is executed, and in §5 we provide a summary and
conclude.

Table 2: A Table showing the observing configuration for each of the major tasks in our program: coronagraphy of an exoplanet and a circumstellar disk, spectroscopy of a planetary mass companion, and aperture masking interferometry. In addition to the instruments used, targets to be observed (both primary and calibration targets), wavelengths/resolution employed, and observing modes, the table also shows in parentheses the partitions of time dedicated purely to science observations (denoted “$t_{\rm sci}$”), and the time charged to to the observaotry (“$t_{\rm obs}$”), which includes observatory overheads. Task (Instruments) |  Targets ($t_{\rm sci}$, $t_{\rm obs}$) |  $\lambda$ coverage, $\lambda$/$\Delta\lambda$ | Mode: Filters/Gratings |  $t_{\rm sci}$ (hr) |  $t_{\rm obs}$ (hr)
---|---|---|---|---|---
Exoplanet Coronagraphy | Primary: HIP 65426 b (5.0, 10.4) |  | NIRCam MASK335R: |  |
(NIRCam, MIRI) | Calibrator: HIP 68245 (2.9, 5.9) | 2-15 $\mu$m | F250M, F300M, F410M, F356W, F444W | 7.9 | 16.3
|  |  | MIRI FQPM: F1140C, F1550C |  |
Disk Coronagraphy | Primary: HD 141569A (7.9, 14.6) | 3-15 $\mu$m | NIRCam MASK335R: F300M, F360M | 13.0 | 23.2
(NIRCam, MIRI) | Calibrator: HD 145570 (5.1,8.6) |  | MIRI FQPM: F1065C, F1140C, F1550C |  |
Spectroscopy of a Planetary |  | 1-28 $\mu$m, | NIRSpec IFS: G140H, G235H, G395H |  |
Mass Companion | VHS 1256 b | R$\sim$1500-3000 | MIRI MRS: All channels | 2.7 | 5.6
(NIRSpec, MIRI) |  |  |  |  |
Aperture Masking | Primary: HIP 65426 (3.6,5.2) |  |  |  |
Interferometry | Calibrator: HIP 68245 (0.5, 1.6) | 3.8 $\mu$m | F380M | 4.5 | 8.4
(NIRISS) | Calibrator: HIP 68282 (0.4, 1.6) |  |  |  |
|  |  |  | Total: 28.0 | Total: 53.5

##  2 Rationale for a JWST Early Release Science Program

JWST will be a transformative observatory for directly characterizing both
exoplanets and their circumstellar environments. It will enable very
sensitive, high-fidelity imaging and spectrophotometry of exoplanetary systems
in the near/mid-infrared with NIRCam, NIRSpec, and NIRISS, and with MIRI at
wavelengths $\gtrsim$5 $\mu$m for the first time. However, imaging young
extrasolar giant planets and disks at very small angular separations from a
host star is an extreme technical challenge: at young ages ($\sim$1-100 Myr)
even the most massive planets are typically $\sim$103-106 times fainter than
the host star, and buried in the halo of the instrumental Point Spread
Function (PSF). At such a high level of contrast, an exquisite understanding
is required of the instrument response, PSF stability, and PSF subtraction
techniques during post-processing. To this end, successfully obtaining images
of planets and circumstellar disks requires the correct choice of observation
mode and extremely careful post-processing.

As described in Perrin et al. (2018), the last $\sim$25 years of highly
successful coronagraphy and high contrast imaging with The Hubble Space
Telescope (HST) provides a very useful set of lessons for the best path
forward with JWST. One clear lesson from the HST legacy is that, particularly
for non-optimal coronagraphs (e.g., those on board HST or even JWST), more of
the contrast gains come from state-of-the-art starlight suppression work in
the image post-processing stage than from suppression at the hardware level
(Pueyo, 2016; Choquet et al., 2016; Perrin et al., 2018; Zhou et al., 2021;
Sanghi et al., 2022). Indeed, exquisite calibration of the PSF is key, and
typically this is carried out by using some variation of the Angular
Differential Imaging technique (“ADI,” Marois et al., 2006) to utilize the
rotation of the observatory to disentangle bona fide astrophysical sources
(e.g., planets) from the residual scattered starlight. For each individual
science image, the task is to use other images in the sequence that do not
contain a signal of the planet to construct an optimized, synthetic model of
the PSF, typically using least-squares (“LS,” Lafrenière et al., 2007), or
principal component analysis methods (“PCA,” Soummer et al., 2012; Amara &
Quanz, 2012) to generate synthetic PSF calibrations that are optimally matched
to each science exposure. However, JWST will typically only achieve $\sim$5∘
of rotation when the spacecraft is rolled slightly, meaning that the
sensitivity of JWST will be limited at close separations from the star.
However, it has now been well demonstrated, especially for space-based imaging
such as HST/NICMOS (Soummer et al., 2011) with PSF morphologies that are
highly stable in time, that using a vast suite of reference PSF images from
other epochs/targets can alleviate this problem. Indeed, over its lifetime,
JWST will provide such an extensive library of images, but not necessarily in
the early phases of the mission.

Coronagraphic imaging with HST required several cycles to optimize the
observing strategy and PSF calibration methods, and the methods are still
being refined (e.g., Schneider et al., 2017; Zhou et al., 2021). Further,
applying techniques originally developed for the ground to HST archival
coronagraphic data (Lafrenière et al., 2009; Soummer et al., 2011) has
revealed vastly improved sensitivity to imaging planets and disks including
numerous new discoveries from archival data (Soummer et al., 2014; Choquet et
al., 2016; Sanghi et al., 2022). Similar methods and algorithms may be
applicable to JWST images, provided the correct observing strategy and data
processing methods are identified as early as possible, and used consistently
going forward. Although the JWST instruments have been characterized during
instrument testing, neither the in-flight performance of each instrument, nor
the optimal strategy for obtaining data in flight, or even the optimal ways to
post-process the data are well understood.

The finite lifetime of the JWST mission means that it is essential to
correctly optimize and implement the observing strategies as early as possible
for gaining spectrophotometry and images of exoplanets and circumstellar
disks. In anticipation of this need, in 2009 the Space Telescope Science
Institute (STScI) appointed the JWST Advisory Committee (JSTAC) to provide a
recommendation on how best to maximize the scientific return of JWST. One of
the recommendations was to create a clear pathway “to enable the community to
understand the performance of JWST prior to the submission of the first post-
launch Cycle 2 proposals that will be submitted just months after the end of
commissioning”111The full description of the recommendations of the JSTAC can
be found at: http://www.stsci.edu/jwst/about/history/jwst-advisory-committee-
jstac. In response, the STScI Director created the JWST Early Release Science
(ERS) programs, comprised of $\sim$500 hours of Director’s Discretionary Time
(DDT). This program was to be characterized by open community access to
substantial, representative datasets, in key instrument modes. Along with the
SEPs described in §4, this effort will support the broader community in the
preparation of future JWST proposals, and engage a broad cross-section of the
community to familiarize themselves with JWST data and its scientific
capabilities.222More information on the ERS programs can be found at:
http://www.stsci.edu/jwst/science-planning/calls-for-proposals-and-policy

From 2016-2017 numerous members of the community engaged in research related
to the direct imaging and spectroscopy of exoplanets and circumstellar disks
self-organized to formulate a program designed to address the key questions
about JWST performance that will inform future proposal cycles. A total of 106
proposals were submitted, and 13 proposals were ultimately selected spanning a
range of science disciplines including extrasolar planets, solar system,
stellar populations and stellar physics, Galaxies and intergalactic medium,
and host galaxies to massive black holes.

##  3 Overview of ERS Program 1386

Our Program “High Contrast Imaging of Exoplanets and Exoplanetary Systems with
JWST” (Program 1386) was awarded 54 hours of DDT to utilize all four JWST
instruments, and assess the performance of the observatory in representative
modes that are highly applicable to our community going forward. In addition,
our team has been tasked to: 1) optimize data calibration and analysis
methods; 2) make clear recommendations to our community about the best
practices for JWST observing; and 3) generate a set of SEPs, tools that will
be essential for the broader community to plan for Cycle 2 proposals (§4). The
ultimate outcome of this program will be to rapidly establish the optimal
strategies for imaging and spectroscopy of exoplanetary systems going forward,
and provide the community with a set of tools to help prepare the strongest
proposals for Cycle 2 and beyond.

At the same time, our program will showcase the transformative science
expected from JWST related to the direct characterization of planetary systems
such as highly sensitive coronagraphy, direct spectroscopy into the thermal
infrared, and interferometry. Table 1 provides an overview of the targets we
will observe and their basic properties. The exoplanet HIP 65426b (Chauvin et
al., 2017) and substellar object VHS J125601.92-125723.9b (Gauza et al., 2015;
Miles et al., 2018, hereafter “VHS 1256 b”) are both young companions to their
host stars at wide orbital separations, and HD 141569A, is a young (5$\pm$3
Myr) circumstellar disk (Clampin et al., 2003) still potentially in the phase
of forming a planetary system.

Our program will carry out: 1) coronagraphic imaging of HIP 65426b and 2) HD
141569A at wavelengths extending to 15.5 $\mu$m; 3) spectroscopy of VHS 1256b
at resolution $R$ $\sim$ 1500-3000 out to 28 $\mu$m; and 4) aperture masking
interferometry of a bright star (HIP 65426) at 3.8 $\mu$m. Table 2 lists these
four primary science tasks and the instruments they will employ, the primary
targets for each of these tasks, as well as the objects selected for
calibration. Table 2 also lists the wavelength coverage and spectral
resolution, as well as details of the chosen observing mode, e.g. the NIRCam
“MASK335R” or MIRI FQPM coronagraphy, NIRSpec Integral Field Spectrograph
(IFS) or the MIRI Medium Resolution Spectrograph (MRS). Table 2 also lists the
pure “science time” (denoted “$t_{\rm sci}$”) dedicated to each of these three
tasks, as well as the overall time charged to the observatory (denoted
“$t_{\rm obs}$”) reflecting spacecraft, observatory, and instrument overheads.
In total, our program will utilize 53.5 hours of observing time, with 28 of
these hours dedicated to pure science observations. A majority of this time
will be dedicated to coronagraphic observations of HIP 65426b and HD 141569A
($\sim$40 hours observatory time, 20.9 hours of science). Below we describe
each of these components of the program in greater detail.

Figure 3:  Top row: Simulated contrast curves using the updated version of the
PanCAKE tool (Carter et al., 2021a) showing the predicted contrast performance
in the NIRCam F410M (4.1 $\mu$m) and MIRI F1140C (11.4 $\mu$m) JWST filters.
The two sets of curves illustrate the coronagraphic performance using PSF
subtraction based both on Angular Differential Imaging (ADI) as well as a
combination of ADI and Reference Differential Imaging (RDI), corresponding to
the images shown in Figure 1. The theoretical contrast of the HIP 65426b
exoplanet is shown by the circular point. Bottom row: the corresponding
detection probability maps (Bonavita, 2020) based on these contrast curves
with the 30, 60, 70, and 80% detection probability contours highlighted.

###  3.1 Coronagraphy of an Extrasolar Planet

HIP 65426b is a 6-12 MJup planetary mass companion (Chauvin et al., 2017) with
a wide projected orbital separation of 92 AU, and a young age (14$\pm$4 Myr)
based on its host star’s high probability of membership to the Lower Centaurus
Crux association (de Zeeuw et al., 1999; Rizzuto et al., 2011). This was the
first major discovery by the SPHERE “SHINE” GTO survey (Desidera et al., 2021;
Langlois et al., 2021; Vigan et al., 2021). Initial photometric measurements
from 1 to 5$\mu$m (Cheetham et al., 2019; Stolker et al., 2020) and medium
resolution spectroscopy (Petrus et al., 2021) of this object are consistent
with a dusty, low surface gravity atmosphere with mid/late-L spectral type.
The angular separation of 830 mas and contrast of $\sim$ $10^{-4}$ relative to
the host star make this object an ideal early target to be observed with the
NIRCam and MIRI coronagraphs. Figure 1 shows synthetic coronagraphic images
calculated using an updated version (described in greater detail in Carter et
al., 2021a) of the Pandeia Coronagraphy Advanced Kit for Extractions,
(PanCAKE, Perrin et al., 2018; Girard et al., 2018). PanCAKE is a Python-based
package which extends the capabilities of the primary JWST exposure time
calculator (ETC) Pandeia (Pontoppidan et al., 2016) to be applicable to the
coronagraphic observing modes of JWST.

Figure 1 also highlights the effect of various image post-processing
strategies on the significance of the detection of the underlying planet HIP
65426b. The central panels of Figure 1 show the detection using a simple
framework based on ADI, taking advantage of the fact that HIP 65426 will be
observed in two configurations separated by a physical “roll” of the
observatory of a few degrees. This well-tested methodology is based on the
heritage of “roll deconvolution” methods developed for HST (Krist et al.,
2012; Schneider et al., 2014). Figure 1 also shows the improvement in contrast
that can be gained by tapping into a larger pool of PSF reference images to
perform Reference Differential Imaging (“RDI,” Soummer et al., 2011; Ruane et
al., 2019; Sanghi et al., 2022), and labelled “ADI+RDI” in the figure. This
method takes advantage of the “small-grid dither” strategy in which repeated
observations of a calibration star are obtained at six or nine positions
separated by sub-pixel spacings, with the goal of more broadly sampling PSF
variations due to varying placement behind the corongraphic masks.

Numerous studies have demonstrated the improved coronagraphic suppression at
small inner working angles by tapping into a much larger “library” of
reference images (e.g., Soummer et al., 2011, 2014; Choquet et al., 2016;
Ruane et al., 2019; Wahhaj et al., 2021; Sanghi et al., 2022). This potential
improvement in inner working angle is even more crucial for JWST which
typically has an angular resolution of $\sim$100-140 mas at 3-4 $\mu$m,
compared to the $\sim$30-50 mas resolution of 8-10m ground-based telescopes in
the near-infrared. This improvement is most pronounced with coronagraphic
datasets that a) have a large number of PSF reference images; and b) are
highly stable in time, such as is expected to be the case for JWST. Indeed,
the pool of reference images will inevitably grow over the lifetime of JWST,
and our ability to suppress starlight at small inner working angles will
improve over time as our library of reference images grows. Thus, it is of
paramount importance to determine the correct observing strategy early in the
mission, so that the community may tap into the most uniform pool of reference
images possible. The coronagraphic datasets obtained as part of this ERS
program will mark the first step in this process.

Figure 2 (top panel) shows the planned photometric coverage for the
coronagraphic observations of HIP 65426 b. As listed in Table 2, the total
wavelength coverage for this portion of the program spans from 2.50 $\mu$m to
15.5 $\mu$m using the NIRCam “MASK335R” (335 round coronagraphic mask) F250M,
F300M, F410M, F356W, and F444W filters, as well as the F1140C and F1550C MIRI
filters coupled to the FQPM. The transmission profiles of each filter are
shown in the top panel of Figure 2, and the horizontal error bars on each
point indicate the filter widths (in $\mu$m). The continuous curve in the top
panel of Figure 2 shows a model for a $T_{\rm eff}$=1340 K and log($g$)=3.95
object, and is based on the results presented in Cheetham et al. (2019). The
photometric points in the figure were calculated by integrating the model over
the bandpass of each of the filters.

The bottom panel of Figure 2 shows the power of the longer wavelength data
that will be available in this ERS program to characterize the atmospheres of
substellar objects. The figure shows two synthetic atmospheric models taken
from Phillips et al. (2020) which presents a set of theoretical atmosphere and
evolutionary models for very cool brown dwarfs and self-luminous giant
exoplanets generated using the one-dimensional radiative-convective
equilibrium code ATMO (Tremblin et al., 2016). The figure shows an atmosphere
model based on equilibrium atmospheric chemistry (blue curve), as well as a
model characterized by disequilibrium chemistry (orange curve). Evidence for
exoplanet atmospheres characterized by disequilibrium chemistry already exist
(e.g., Skemer et al., 2012; Konopacky et al., 2013; Miles et al., 2020), and
atmospheres can be driven out of equilibrium due to processes that happen on
timescales faster than the net chemical reaction timescales that would bring
the atmosphere back to equilibrium (e.g., rapid vertical atmospheric mixing
driven by gravity waves or convective overshooting; Freytag et al., 1996;
Kupka et al., 2018). The models plotted rely on the values of 1618K and
log($g$) = 3.78 for the HIP 65426 b system from (Cheetham et al., 2019) which
derived these best fit parameters using a combination of 1-2.2 $\mu$m
spectroscopy and photometry from VLT-SPHERE, as well as $L^{\prime}$ and
$M^{\prime}$ photometry from VLT-NACO. When only considering the band-averaged
brightnesses, the two models are virtually identical at 2.50 $\mu$m, but are
very clearly differentiated at 3.56 $\mu$m. Of course, other atmospheric
properties besides the presence of disequilibrium chemistry can affect the
flux near 3.56 $\mu$m. For example, a higher effective temperature ($T_{\rm
eff}$) or a lower overall atmospheric metallicity leading to decreased CH4
would change the strength of the 3.56 $\mu$m feature. However these
degeneracies can be broken via other parameters accessed by our datasets, e.g.
constraints on the effective temperature afforded by the long wavelength
coverage, and contraints on the atmospheric metallicity from host star
abundance measurements. Thus, the very high expected photometric precision of
NIRCam, combined with long wavelength coverage and host star abundance
constraints means that our data will be sensitive to the effects of
disequilibrium chemistry, which has the largest impact near 3.56 $\mu$m given
the broad CH4 feature.

Figure 4: Top panel: A synthetic spectrum of the VHS 1256b planetary mass
companion we will obtain using NIRSpec and MIRI, reflecting the actual
spectral resolution, as well as a subset of the spectral range that will be
covered (inset). The purple regions signify the 1, 2, 3$\sigma$ confidence
intervals in the spectrum, while the grey regions indicate portions of the
spectrum where no data will be gathered due to detector gaps. The input model
spectrum is based on a synthetic model described in Morley et al. (2012),
which has been used in conjunction with the estimated signal-to-noise ratio
defined in the ETC, to determine the confidence intervals shown in the plot.
Bottom Panel: The predicted SNR for the VHS 1256b spectrum, as determined from
the JWST ETC.

The significant photometric uncertainties indicated by the error bars on the
existing ground-based data shown in Figure 2 (top panel) highlights that it is
challenging for these data to provide precise constraints on the theoretical
atmospheric models of HIP65426b. However, the vastly higher precision NIRCam
and MIRI photometry beyond $\sim$2 $\mu$m, will allow much tighter constraints
on the allowable atmospheric parameters. Along with the vastly increased
photometric precision, the longer wavelength coverage provided by our
observations will provide a much greater source of leverage for performing an
atmospheric retrieval analysis, similar to that described in §3.2.1.
Specifically, we will carry out forward model fitting to extract the best fit
model parameters reflecting the atmospheric compositions as well as a
retrieval analysis to gather constraints on molecular abundances (e.g., CO,
H2O, CH4), disequilibrium chemisty, and the presents of clouds. With high
precision NIRCam and MIRI data beyond $\sim$2 $\mu$m, particularly out to
$\sim$15 $\mu$m on the tail of the SED, our dataset will also provide a much
better constraint on the broadband emission of HIP65426b.

To demonstrate the sensitivity of coronagraphic observations using both NIRCam
and MIRI, Figure 3 shows the expected achievable contrast corresponding to the
various stages of image post-processing at 4.1 $\mu$m and 11.4 $\mu$m shown in
Figure 1. The contrast curves were calculated using a modified version of
PanCAKE described in Carter et al. (2021a) for the F410M and F1140C filters
that we will utilise in this program. Rather than the 5$\sigma$ contrast
curves used historically (e.g., Hinkley et al., 2007), the curves are based on
a $3\times 10^{-7}$ false positive fraction, taking into account the
statistical correction due to small angular separations (Mawet et al., 2014).
The contrast curves presented in Figure 3 are also calibrated for the two-
dimensional throughput of the coronagraphs as well as the inherent throughput
of the Karhunen-Loève Image Projection subtraction routine (“KLIP,” Soummer et
al., 2012) which uses a principal component analysis approach to build a
synthetic reference image for each science image from a library of reference
images. Importantly, these contrast curves also take into account dynamical
changes in the wavefront error due to the varying thermal state of the optical
telescope element (OTE) due to telescope slews, small variations in the
tension of the physical structure of the telescope, or vibrations within the
OTE (e.g. due to the OTE stray light baffle/insulation closeouts). For each
wavelength, Figure 3 displays a curve assuming only a simple roll subtraction
(termed “ADI”). Further, Figure 3 also shows the improvement in contrast by a
factor of a $\sim$few to ten that can be gained by taking advantage of the
small-grid dither strategy (labelled “ADI+RDI” in the figure). As a
calibration strategy for our observations of HIP 65426 b, we will observe the
B2IV star HIP 68245, which is close on the sky to HIP 65426, but has $\sim$8
times the brightness in the mid-infrared (K=4.49 versus K=6.77 for HIP 65426).
The enhanced brightness of HIP68245 allows us to obtain a nine-point dithered
observation of this calibrator without a prohibitively large amount of
observing time. Table 2 shows that this calibrator star will be observed in
roughly half the charged time as our observations of HIP 65426 (5.9 hours
versus 10.4 hours), but with nine times the data volume, enhancing the overall
calibration process.

Figure 5: A demonstration of a simulated atmospheric retrieval analysis of VHS
1256b using simulated HST WFC3 and JWST NIRSpec data. The dataset for NIRSpec
was simulated at an SNR of 50 - 250 using the JWST ETC, and a uniform SNR=50
for WFC3. Retrieval analyses were performed to derive the model parameters,
demonstrating the power JWST can offer for precisely and accurately
constraining crucial model parameters within retrievals.

To calculate a representative estimation of the sensitivity to low mass
companions (in Jupiter masses) as a function of projected orbital separation
(in AU) of these observations, the lower panels of Figure 3 also show the
corresponding two-dimensional detection probability maps based on the
synthetic contrast curves. These detection probability maps, calculated using
the Exoplanet Detection Map Calculator (“Exo-DMC,” Bonavita, 2020),
effectively demonstrate the degree of “completeness” to planetary mass
companions as a function of mass and semi-major axis for a given coronagraphic
observation. For a given planet mass and semi-major axis, Exo-DMC simulates an
ensemble of orbital geometries with varying planetary eccentricity and
inclination to determine which fraction of those realizations, identified by
the various completeness contours, would be detected based on the supplied
single contrast curve, thus providing a measure of the completeness of the
observations. This calculation uses the evolutionary models of Phillips et al.
(2020) assuming an age of 14 Myr to convert from a contrast to a physical
mass, and so these plots are dependent on the underlying evolutionary models
as well the formulation of atmospheric process that they employ.

###  3.2 Spectroscopy of a Planetary Mass Companion

VHS 1256b (Gauza et al., 2015; Miles et al., 2018) is a wide separation
($\sim$103 AU), substellar companion (19$\pm$5 MJup) to a young M7.5 binary
star (Stone et al., 2016; Rich et al., 2016). While the VHS 1256 system is not
a member of any known kinematic young moving groups, (Gauza et al., 2015)
derive an age of 150-300 Myr, consistent with its low surface gravity. With a
spectral type of L7, infrared parallax measurements (Dupuy et al., 2020) show
that VHS 1256b shares a region in a colour-magnitude diagram with other
planetary mass companions that are near the L/T transition, and close to the
deuterium burning limit such as HR 8799b, HD 203030B, and 2MASS
J22362452+4751425 b (Marois et al., 2008; Metchev & Hillenbrand, 2006; Bowler
et al., 2017).

As shown in Table 2, we will observe VHS 1256b using the NIRSpec IFS from 1.66
to 5.27 $\mu$m with the G140H, G235H, G395H gratings
($\lambda/\Delta\lambda\sim$1500-3500). Our selected exposure time provides a
signal-to-noise ratio (SNR) of $>$20 across the majority of the wavelength
range with many features detectable to a SNR of $>$100\. However, these
estimations are based on initial simulations, and the actual data may indeed
have different SNR values. To probe silicate cloud features at 8 to 12 $\mu$m
for the first time ever for low surface gravity objects, we will also perform
mid-infrared spectroscopy on VHS 1256b from $\sim$5 to 28 $\mu$m using the
MIRI MRS in all four channels with all three dispersers. To improve the
sensitivity of the observations and minimize detector effects, we will utilize
four dither positions for both the NIRSpec and MIRI observations.

Historically, the main obstacle to obtaining unbiased spectroscopy of
planetary mass companions from ground-based spectrographs coupled to AO
systems has been contamination from uncontrolled, residual scattered starlight
within $\sim$1-2′′ (Hinkley et al., 2007; Marois et al., 2006; Pueyo, 2016).
The relatively large angular separation ($\sim$8′′) of VHS 1256b from its host
binary pair means that our JWST observations should have minimal contamination
of residual scattered starlight. Thus, JWST Spectroscopy of this object will
allow high-fidelity detections of molecules from $\sim$2 to 28 $\mu$m that
will be vital for the understanding of cloud physics driven by dust or
thermochemical instability under low gravity conditions, as well as searching
for evidence of silicate clouds at 8 to 12 $\mu$m. The VHS 1256b spectroscopy
should also serve as a valuable community dataset for testing spectral
extraction algorithms for NIRSpec and MIRI.

Figure 4 shows an example of the signal-to-noise and wavelength coverage for
the spectroscopy of the VHS 1256b companion that will be obtained with our
program. The figure shows a theoretical model atmosphere of a substellar
object with the currently best-fit derived effective temperature and gravity
parameters for VHS 1256b (Zhou et al., 2020). To calculate the effective
uncertainty in the model as a function of wavelength, this model was then used
as input into the JWST ETC, which in turn provides a measure of the expected
signal-to-noise as a function of wavelength. Dividing the original model
spectrum by this signal-to-noise spectrum effectively provides a measure of
the noise as a function of wavelength, and this noise is represented by the
purple shaded regions in the figure. The lower panel of Figure 4 shows the SNR
as a function of wavelength calculated using the ETC.

####  3.2.1 A Suite of Atmospheric retrievals for VHS 1256b

We will employ an extensive retrieval analysis to the reduced spectroscopic
data for VHS 1256b, testing various approaches. Exploring a range of
atmospheric chemistry parameters will probe whether the atmosphere is in
chemical equilibrium versus disequilibrium (Miles et al., 2020; Mollière et
al., 2020). We expect that the quality of our spectra will easily be sensitive
enough to fully sample the prominent CO features at $\sim$2.3 and $\sim$4.3
$\mu$m, as well as numerous H2O and CH4 features across the majority of the
spectral range. By unlocking the mid-IR we will also be able to robustly
explore potential cloud structures (Manjavacas et al., 2021) and cloud species
present (Burningham et al., 2021). Due to the extensive variability present on
VHS 1256b (Bowler et al., 2020), and as the data will be collected over a
period of hours, we will also probe signatures of variability within our
retrieval analysis.

To simulate the outcome of using an atmospheric retrieval-based approach,
Figure 5 shows a demonstration of a simulated retrieval analysis of VHS 1256b
comparing HST WFC3 against simulated JWST NIRSpec spectroscopy. For this,
employing a forward model created via TauREx3.1 (Waldmann et al., 2015b, a;
Al-Refaie et al., 2021b, a) and FastChem (Kitzmann & Stock, 2018), we
simulated realistic NIRSpec spectroscopy with SNR ranging from $\sim$50 to
$\sim$250 generated with the JWST ETC. We also simulated a WFC3 dataset with a
uniform SNR of 50. We then ran retrievals using these simulated observations
and wide uniform priors on all model parameters. Figure 5 highlights the novel
power JWST can offer for precisely and accurately constraining crucial model
parameters within retrievals. However, the improved precision shown in Figure
5 is probably slightly optimistic as: 1) the retrieval algorithm is retrieving
its own model input; 2) these simulations use a simplistic forward model; and
3) we assume perfect data reduction/calibration ignoring any photometric
variability during the observations. However, we stress that this dramatic
increase in precision is driven primarily by the order of magnitude increase
in wavelength coverage and spectral points as well as improved SNR.
Nonetheless, Figure 5 gives a qualitative sense of the level of improvement
that can be gained with the advent of JWST spectroscopy.

Figure 6: Simulated NIRCam data for our planned observations of HD 141569A
using the F300M filter. The left figure shows our input model of the three
rings that comprise the HD 141569A disk and described in Table 3, while the
middle panel shows a representative NIRCam image free of noise. The right
panel shows a simulated image, now including a realistic estimate of the
NIRCam noise that we can expect with our ERS observations.

####  3.2.2 Variability Monitoring of VHS 1256b

Quasiperiodic brightness variability is commonly found in field L and T type
brown dwarfs, with periods from 1.5-30 hours, and amplitudes ranging from
0.01-27% (Radigan et al., 2014; Metchev et al., 2015). Young directly imaged
exoplanets are likely even more variable, as their free-floating counterparts
appear to be particularly variable (Biller et al., 2015a; Apai et al., 2016).
VHS 1256b is the most variable of this group; with measured variability
amplitudes of $>$25% over an 8-hour long HST observation (Bowler et al., 2020;
Zhou et al., 2020), it may be the most variable substellar object ever
observed. Indeed, VHS 1256b is so highly variable that it may vary by up to
10% over our three-hour ERS observation. However, it should be noted that the
variability of substellar objects is wavelength dependent (Metchev et al.,
2015; Vos et al., 2020), and this has been quantified for VHS 1256b in Zhou et
al. (2020). Over $\sim$1.2 hours on-sky, our program will obtain three
consecutive NIRSpec IFS spectra covering 1-5 $\mu$m in the following order:
1.66-3.17 $\mu$m, 2.37-5.27 $\mu$m, and 0.97-1.89 $\mu$m. Each spectrum will
be taken at four dither positions, providing two temporal samples for each
spectral range, separated by $\sim$12 minutes from the central time point of
each spectrum. It is worth noting that we will also record “up-the-ramp” flux
measurements, and thus our observations may be sensitive to variability on
even shorter timescales. In the extreme case, if VHS 1256b has a similar 1-5
$\mu$m variability amplitude in this epoch as in Bowler et al. (2020), this
would translate to a $\sim$few percent change in overall flux across the
NIRSpec observation, sufficient to cause non-negligible issues with spectral
fitting when stitching together, e.g., the 1.66-3.17 $\mu$m spectrum with the
0.97-1.89 $\mu$m spectrum (the muted variability in the water bands, however,
will facilitate stitching together the spectra). This effect is expected to be
even more pronounced with the MIRI MRS observations, which occur over a longer
timescale. To address this risk, we have initiated a coordinated ground-based
observing campaign that will utilize a suite of longitudinally-distributed
observing sites to observe prior to, and ideally in parallel with, the ERS
observations. These observations will allow us to correctly stitch together
the ERS spectroscopy, and constrain the wavelength-by-wavelength amplitudes
and phase shifts.

###  3.3 Coronagraphy of a Young Circumstellar Disk

HD 141569A is a young circumstellar disk (5$\pm$3 Myr, Weinberger et al.,
2000) that has been a well-studied target of past space-based (e.g., Augereau
et al., 1999a; Weinberger et al., 1999; Konishi et al., 2016) as well as
ground-based (Biller et al., 2015b; Perrot et al., 2016; Currie et al., 2016;
Bruzzone et al., 2020; Singh et al., 2021) high contrast imaging efforts. HD
141569A is also a unique system for our understanding of both protoplanetary
disk dispersal processes as well as the early stages of debris disk systems.
In terms of disk luminosity HD 141569A sits at the transition between
protoplanetary and debris disks (Wyatt et al., 2015), and so is a rare object
caught in transition between the two phases. Thus, from a purely scientific
point of view, our ERS observations will help to illuminate whether e.g. the
dust within the disk is primordial or secondary, and how the observed
morphological structures relate to planet formation. Our observations of HD
141569A will demonstrate the power JWST holds for characterizing circumstellar
debris disks, allowing us to determine the disk’s dust size distribution and
composition as a function of radius, and search for planet-induced structures
in the mid-infrared for the first time.

Given that it is comprised of at least three concentric disk rings, HD 141569A
is also an ideal target to test the sensitivity of JWST to extended structures
as a function of stellocentric angle, as well as study the effects of various
PSF subtraction post-processing algorithms on diffuse structures. We will
obtain coronagraphic imaging of HD 141569A from 3.0 to 15.5 $\mu$m using both
NIRCam and MIRI (Table 2). By utilizing the F300M and F360M NIRCam filters, We
will image the disk on and off the 3.0 $\mu$m H2O ice absorption band that is
difficult to probe with ground-based telescopes. In addition to using the
10.65$\mu$m and 11.4$\mu$m MIRI filters, we will image the disk at 15.5
$\mu$m, one of the primary wavelengths expected to be used for JWST disk
imaging going forward. As with the case of the HIP 65426b observations, for a
calibration strategy a brighter calibrator star (HD 145570, Table 2) will be
used to generate a significant volume of PSF calibration images without a
prohibitively large amount of exposure time. The enhanced brightness of HD
145570 (K=4.63 versus K=6.82 for HD 141569A) means that we will require 5.1
hours of calibration observations using a five-point dither strategy, compared
to 7.9 hours for HD 141569A.

To extract the signal of the disk in the NIRCam data, we will use the KLIP
algorithm to subtract the residual scattered starlight using the dithered
images of the calibrator star. We will also use recently-developed PSF
subtraction algorithms that are known to better preserve the absolute flux of
bright extended structures than PCA-based methods, such as the Non-negative
Matrix Factorization (Ren et al., 2018) and Data Imputation (Ren et al.,
2020), and present a comparison of the results obtained with the different
algorithms. The brightness ratio of the disk between the F300M and F360M
filters will allow us to constrain the composition of the dust, in particular
the ice fraction in the different belts of the system. This will reveal
whether the dust is cometary or asteroid-like in nature, and will provide
direct observations of how water is radially distributed in the outer parts
($\gtrsim$50 AU) of this young system. For this analysis, we will use both a
forward modeling approach as well as an empirical approach to measure the
surface brightnesses and scattering phase functions in both filters. In both
cases, we will compare the data to models created with the MCFOST radiative
transfer code (Pinte et al., 2006) which can simulate dust belts with various
chemical compositions and grain size distributions.

In the mid-infrared, where the existing resolved images of the HD 141569A disk
are from ground-based instruments and only sensitive to the warm inner disk at
$\sim$50 AU (Moerchen et al., 2010; Thi et al., 2014; Mawet et al., 2017),
very little is known about the thermal properties of the outer disk components
at $\sim$200 and $\sim$400 AU. Similarly, the SED of the system is dominated
by the emission of the inner disk between $\sim$8 $\mu$m and $\sim$40 $\mu$m
(see Mawet et al., 2017), which also prevents any constraints on the thermal
properties of the outer belts. Our MIRI imaging will be sensitive for the
first time to the mid-IR emission of the outer belts and spatially separate
them from the inner belt emission. These independent measurements of each disk
component will allow us to disentangle their individual thermal properties.
Simulations of the data we will collect with MIRI based on the model from Thi
et al. (2014), indicate that the two outer belts should be directly visible in
the three MIRI filters, with little contamination from the star, while the
innermost belt will be impacted by both the residual host starlight and
obscuration from the 4QPM coronagraph. Given this, HD 141569A thus has the
added bonus that this target will serve as a good feasibility test for the
MIRI coronagraphs to check whether future observations of protoplanetary disks
might be limited by the (nearly) unresolved warmer inner ring components.

Table 3: Parameters for HD 141569A model System Parameters |  |  |
---|---|---|---
Distance (pc) |  | 110.6 |
Inclination (°) |  | 56.9 |
Position angle (°) |  | 356.1 |
Vertical aspect ratio $h$ |  | 0.04 |
F300M Stellar flux (Jy) |  | 0.71 |
F360M Stellar flux (Jy) |  | 0.50 |
Ring Parameters | Ring 1 | Ring 2 | Ring 3
Semi-major axis (″) | 0.8 | 1.8 | 3.2
Critical radius (au) | 88 | 199 | 354
Inward density index $\alpha_{in}$ | 1.5 | 5.5 | 5.5
Outward density index $\alpha_{out}$ | -7.5 | -7.5 | -3.5
HG parameter $g$ | 0.0 | 0.1 | 0.3
Peak reflectance ($\times 10^{-4}$ ″-2) | 8.0 | 2.8 | 0.4

After removing the residual starlight using the reference star observations,
we will use the MIRI imaging for three main applications. First, the 10.65
$\mu$m image will let us investigate the transition between the scattered-
light and thermal regimes in each belt. While the inner disk 10.65 $\mu$m
image should be largely dominated by thermal emission, scattered-light may be
significant in the images of the outer disks. We will use both the MIRI F1065C
image and the F300M/F360M NIRCam images to disentangle the scattered-light and
thermal contributions of each belt. Second, we will use the 10.65 $\mu$m and
11.4 $\mu$m images to measure the radial distribution of Polycyclic Aromatic
Hydrocarbons (PAH). Unresolved Spitzer IRS observations have previously
detected PAH in this system (Sloan et al., 2005; Berné et al., 2009), but it
is currently unclear if they are distributed uniformly throughout the disk, in
only parts of the disk, or in an outflowing halo of small grains. The F1065C
and F1140C datasets will be sensitive to the broad PAH emission feature at
11.3 $\mu$m, allowing us to constrain the PAH abundances in each ring. Third,
we will use the 10.65 $\mu$m and 15.5 $\mu$m images to measure the continuum
thermal emission of each ring individually and construct a temperature map of
the whole system. This will allow us to derive the emissivity of the grains
and thus the minimum grain size of each ring through radiative transfer
modeling. The distribution of small micron-sized dust inferred from these
observations will be particularly powerful when coupled with information about
the distribution of larger millimeter-sized dust bodies and CO inferred from
ALMA observations (e.g., Di Folco et al., 2020). This will give the grain size
distribution over a wider range of particle sizes, and allow us to test for
models of grain radial transport through interaction with a remnant gas disk
(e.g., Takeuchi & Artymowicz, 2001).

Finally, in all NIRCam and MIRI images of HD 141569A, we will scrutinize the
disk morphology to look for direct and indirect evidence of planets. The
system shows large cavities that suggest dynamical clearing by planets (e.g.,
Wyatt, 2005; Konishi et al., 2016; Perrot et al., 2016), and asymmetries in
the dust density distribution possibly caused by a massive collision between
planetesimals (Singh et al., 2021). The longer wavelengths of JWST offer a
more favorable contrast regime to probe for the presence of giant planets than
existing shorter wavelength observations, as discussed in §1.2. The NIRCam
F360M and the MIRI F1065C images will be the best suited to look for planets
within the gaps of the system, as they offer the best compromise between
contrast limits and spatial resolution.

To simulate the HD 141569A NIRCam data, we use the basic physical parameters
listed in Table 3. We first generate a model composed of three rings, of
respective semi-major axes 0$\farcs$8, 1$\farcs$8, and 3$\farcs$2\. Assuming a
distance of 110.6 pc (Gaia Collaboration et al., 2018), these values
correspond to radii of 88 AU, 199 AU, and 354 AU, based on the findings of
Konishi et al. (2016), and Mouillet et al. (2001). We simulate a common 56.9°
inclination from a face-on configuration and a common position angle 356.1°,
east of north for the three rings (Perrot et al., 2016). We use the ring
parametric model described in Augereau et al. (1999b) to model the dust
density distribution. The radial profile is modeled with an increasing power
law (index $\alpha_{in}$) inward of the critical radius quoted above and a
decreasing power law (index $\alpha_{out}$) outward from this radius. The
power law indices used to model each of the three rings are displayed in Table
3. The vertical profile of the disks follows a Gaussian profile with a
standard deviation increasing radially with an aspect ratio $h=0.04$
(Thébault, 2009).

We simulate the scattering phase function of the rings using a Henyey-
Greenstein (HG) in parametric model with an asymmetry parameter $g$ of 0.0 for
the inner ring (Konishi et al., 2016), 0.1 for the middle ring (Weinberger et
al., 1999), and 0.3 for the outer ring Clampin et al. (2003). We use optically
thin scattered light propagation from the central star, computed with the same
code as in Millar-Blanchaer et al. (2015) to simulate synthetic images of the
three rings. We then scale the peak surface brightness of each disk to the
product between the stellar flux at the considered wavelength and the disk
peak reflectance. The left panel of Figure 6 shows this model, free of any
detector noise. We obtain the peak reflectance of each ring from previous
HST/NICMOS data obtained in the F160W filter (${\sim}1.6\,\mu$m) from the
ALICE archival re-analysis (Hagan et al., 2018), assuming that the dust
reflectance is identical at the longer NIRCam wavelengths (grey scattering).
We compute the stellar flux in the NIRCam F300M and F360M filters from the
JWST online ETC using a Phoenix A1V spectrum scaled to the $K=6.82$ HD 141569A
magnitude (Cutri et al., 2003). The stellar fluxes in the two filters and the
peak reflectance of the three rings are reported in Table 3. Figure 6 also
shows a simulated image of the data we can expect to receive from our NIRCam
imaging of this system, incorporating realistic noise sources.

###  3.4 Aperture Masking Interferometry

Given its 6.5m effective aperture, for the purpose of gaining direct images of
self-luminous exoplanets, JWST will be ultimately limited by its inner working
angle defined by its diffraction limit of $\lambda/D{\sim}$120-140
milliarcseconds at 3-4 $\mu$m. For the nearest star forming regions where the
planet formation process is still likely ongoing (e.g., Taurus, $\sim$2 Myr,
Kraus et al., 2011) or has recently ceased, (e.g., the Scorpius-Centaurus
Association, $\sim$11-16 Myr, de Zeeuw et al., 1999; Preibisch & Mamajek,
2008) with characteristic distances of $\sim$ 140 pc, this angular resolution
corresponds to physical separations of 17-20 AU, well outside the typical
water ice line separations of $\sim$2-3 AU where planet formation is thought
to be most efficient (Frelikh et al., 2019; Fernandes et al., 2019; Fulton et
al., 2021). By dividing the full JWST pupil into a set of smaller sub
apertures, the technique of Aperture Masking Interferometry (“AMI,” Tuthill et
al., 2000; Ireland, 2013; Sallum & Skemer, 2019) can provide modest
sensitivity inside the diffraction limit of JWST. This technique has been used
extensively from the ground (e.g. Hinkley et al., 2011a; Kraus & Ireland,
2012; Hinkley et al., 2015b; Sallum et al., 2015), but never in space, and so
obtaining a representative dataset as early as possible is critical for future
planning. The NIRISS instrument is equipped with a sparse aperture mask
(Sivaramakrishnan et al., 2010) containing seven holes, providing 21 distinct
“non-redundant” baselines that can be used with the medium-band NIRISS filters
from 3.8-4.8$\mu$m (F380M, F430M, and F480M). Figure 7 shows an image of the
seven-hole mask, as well as a simulation of the resulting interferogram on the
detector using the NIRISS F380M filter generated using the methodology
described in Sallum & Skemer (2019), assuming a point source for a total
integration time of 1.28 s. With the goal of identifying the limiting
systematics inherent in AMI observations we will obtain 3.8 $\mu$m sparse
aperture masking observations of the HIP 65426 system. Past studies have
demonstrated that systems with already-identified wide separation planets may
also have additional planets at smaller angular separations (Marois et al.,
2010; Wagner et al., 2019; Lagrange et al., 2020; Nowak et al., 2020), and
this effort will place constraints on any additional companions in this
system. Our proposed observations of the HIP 65426 system are predicted to
reach a contrast ranging from 6 to $\sim$9 magnitudes within the 120 mas JWST
diffraction limit at 3.8 $\mu$m, providing sensitivity to substellar
companions within $\sim$14 AU for this system (Figure 7, lower panels).

Figure 7: (a): An image of the sparse aperture mask installed within the
NIRISS instrument (Sivaramakrishnan et al., 2010) for the purpose of
performing Aperture Masking Interferometry (Artigau et al., 2014; Greenbaum et
al., 2014; Sallum & Skemer, 2019). (b): A simulated NIRISS interferogram in
the F380M filter for an integration time of $\sim$1.28 s. (c): a simulated
contrast curve calculated using the methods described in (Sallum & Skemer,
2019), and (d) the corresponding detection probability map showing the 30, 60,
70 and 80% detection probability contours.

##  4 Analysis Plan & Science Enabling Products for the Broader Community

Our consortium is comprised of a set of four working subgroups, corresponding
to: 1) exoplanet coronagraphic imaging with NIRCam and MIRI; 2) debris disk
imaging with the same two instruments; 3) spectroscopy of substellar
companions with NIRSpec and MIRI; and 4) NIRISS aperture masking
interferometry.

Our team will be responsible for delivering a set of SEPs described in more
detail below that will be widely available, and will assist the community in
preparing future JWST proposals. A final working group will have the task of
ensuring the quality of the SEPs. This working group will validate the utility
of our internal SEPs for preparing future proposals and provide immediate
feedback to the individual working groups regarding the quality of the SEPs.

###  4.1 Science-Enabling Products

In this section we describe in detail each of the SEPs our consortium will
deliver:

Coronagraphic imaging simulator: We will widely distribute an update (Carter
et al., 2021a) to the JWST simulation tool PanCAKE (Perrin et al., 2018;
Girard et al., 2018) that is an adaptation of the JWST ETC Pandeia. This tool
will model the impact on coronagraphic imaging due to details of the
observatory such as target acquisition offsets, time evolution of the incoming
stellar wave front, spacecraft rolls, and small grid dithers. This tool will
also preserve all the capabilities of the Pandeia simulator for generating
coronagraphic scenes with MIRI and NIRCam, but with added accessibility and
functionality.

High-Contrast Imaging Analysis Pipeline: We will release a publicly available
Python-based high-contrast imaging pipeline to process JWST NIRCam and MIRI
coronagraphic imaging data and generate contrast curves based on the existing
PyKLIP pipeline (Wang et al., 2015), a Python adaption of the commonly used
KLIP algorithm that implements several sophisticated state-of-the-art methods
for PSF subtraction and forward modelling of systematics. We will test our
pipeline against other leading high-contrast pipelines to ensure consistent
results. An online manual will describe the algorithm and provide a tutorial
to run the pipeline using the ERS HIP 65426 data. This pipeline will be
optimized for detecting point-sources, but will also produce scientifically
usable images of resolved structures, which we will test and document using
the HD 141569A images. By the Cycle 2 proposal deadline, users will be able to
simulate realistic scenes with the modified version of PanCAKE that will be
provided, which can then be interfaced with PyKLIP to produce realistic
simulated reductions. The pipeline will also enable immediate analysis of data
from future JWST cycles.

Contrast Curves: We will use our coronagraphic NIRCam/MIRI imaging and NIRISS
aperture masking data of HIP 65426 to provide contrast curves showing the
point source sensitivity of JWST as a function of wavelength and stellocentric
angle. To reflect various post-processing strategies, we will provide multiple
curves per bandpass of contrast (in units of delta-magnitude) vs. angular
separation.

Aperture Masking Analysis Pipeline: We will release a publicly available
Python-based pipeline to process simulated NIRISS AMI data generating a
contrast curve and detection tests for single point-source companions. This
will be in place prior to the Cycle 2 proposal deadline along with an online
tutorial to guide users to reproduce our team’s results. The pipeline will
enable rapid sensitivity estimates for proposers in future cycles, as well as
fast analysis of future datasets.

Communication of Best Practices: The wide variety of JWST’s high-contrast
modes will spawn numerous technical questions about best practices for
observations and data post processing. For example, the best strategy for
utilizing JWST in its coronagraphic mode for various point source science
cases is a priori not obvious. This same question has been an active area of
research in the high-contrast imaging community for the past decade, even for
ground-based efforts (e.g., Xuan et al., 2018; Wahhaj et al., 2021). For JWST,
there is an expectation that RDI will be more effective than ADI due to its
limited ability to achieve large roll angles, but it is a clear goal of this
program to establish the point in an observation sequence where one method
becomes beneficial relative to another. While our ERS program is not intended
to be comprehensive, we will make clear recommendations to future JWST
proposers about the following:

  1. 1.

For what observing scenarios does RDI become more effective than ADI? We will
also establish whether this crossover point depends on the variety of PSF
subtraction, e.g. the Locally Optimized Combination of Images versus KLIP
(Lafrenière et al., 2007; Soummer et al., 2012).

  2. 2.

At what angular separations does AMI achieve contrast that is superior to
coronagraphic imaging?

  3. 3.

To what extent do small grid dithers for PSF reference stars improve the final
contrasts?

##  5 Summary & Conclusions

The ERS program described in this work will utilize all four JWST instruments
to carry out some of the first direct characterisation of planetary mass
companions at wavelengths beyond $\sim$5$\mu$m, as well as image a
circumstellar disk in the mid-infrared with unprecedented sensitivity.
Specifically, our program will carry out:

  * •

Coronagraphy of an Extrasolar Planet: Our program will gather photometric
measurements of an extrasolar planet with unprecedented sensitivity from 3-5
$\mu$m, as well as gather the first-ever coronagraphic observations beyond 5
$\mu$m. Our team will produce tools to aid the community in preparing future
JWST proposals such as an update to the JWST coronagraphic imaging simulator
PanCAKE, a standalone high-contrast imaging analysis pipeline, as well as
contrast curves that define the coronagraphic performance of JWST.

  * •

Spectroscopy of a planetary mass companion: We will obtain spectroscopy beyond
20 $\mu$m of the planetary mass companion VHS 1256b. This dataset will
represent an accurate performance of the spectroscopic capabilities of JWST
using NIRSpec and MIRI, and will be a valuable resource for the community to
prepare proposals related to spectroscopy going forward.

  * •

Coronagraphy of a circumstellar disk: We will obtain corongraphy out to 15.5
$\mu$m of HD 141569A, a young circumstellar disk, providing an understanding
of JWST’s sensitivity to extended structures as a function of stellocentric
angle, and sampling the disk brightness on and off the 3.0 $\mu$m H2O ice
feature.

  * •

Aperture Masking Interferometry: To evaluate the sensitivity of JWST near its
fundamental diffraction limit of $\lambda/D{\sim}$120-140 milliarcseconds at
wavelengths of 3-4 $\mu$m, we will carry out observations of the HIP 65426
system. These observations will have sensitivity to substellar companions in
this system within $\sim$14 AU, as well as identify the limiting systematics
inherent in AMI observations.

We have assembled a broad and representative consortium that comprises a large
fraction of the entire high contrast imaging community. Our team members range
from senior experts with decades of experience in exoplanet and disk imaging
with HST, infrared astronomy (e.g., Spitzer) and adaptive optics, to early
career researchers advancing the state-of-the-art in high contrast image
processing and exoplanet atmosphere modelling. Through a series of calls and
open consortium in-person meetings, we have drawn inclusively on a wide range
of perspectives in identifying the highest priority investigations needed
prior to Cycle 2. In addition to the scientific outcomes highlighted in this
work, our program will also (critically) assess the performance of the
observatory in the key modes expected to be commonly used for exoplanet direct
imaging and spectroscopy, optimize data calibration and processing, and
generate representative datasets that will enable a broad user base to
effectively plan for general observing programs in future cycles. While this
ERS program directly addresses the direct imaging of young, Jovian-mass
planets with JWST, this effort marks an important milestone in the long term
effort to image much lower mass, terrestrial planets with future large-scale
space missions.

We thank the anonymous referee for numerous helpful suggestions, which
significantly improved the manuscript. This project was supported by a grant
from STScI (JWST-ERS-01386) under NASA contract NAS5-03127. M.T.is supported
by JSPS KAKENHI grant No.18H05442. MB acknowledges support from the Deutsche
Forschungsgemeinschaft (DFG) through grant Kr 2164/13-2. K.W. acknowledges
support from NASA through the NASA Hubble Fellowship grant HST-HF2-51472.001-A
awarded by the Space Telescope Science Institute, which is operated by the
Association of Universities for Research in Astronomy, Incorporated, under
NASA contract NAS5-26555.

## References

  * Al-Refaie et al. (2021a) Al-Refaie, A. F., Changeat, Q., Venot, O., et al. 2021a, arXiv, arXiv:2110.01271
  * Al-Refaie et al. (2021b) Al-Refaie, A. F., Changeat, Q., Waldmann, I. P., & Tinetti, G. 2021b, ApJ, 917, 37
  * Amara & Quanz (2012) Amara, A., & Quanz, S. P. 2012, MNRAS, 427, 948
  * Apai et al. (2016) Apai, D., Kasper, M., Skemer, A., et al. 2016, ApJ, 820, 40
  * Artigau et al. (2014) Artigau, É., Sivaramakrishnan, A., Greenbaum, A. Z., et al. 2014, Proc. SPIE, 9143, 914340
  * Augereau et al. (1999a) Augereau, J. C., Lagrange, A. M., Mouillet, D., & Ménard, F. 1999a, A&A, 350, L51
  * Augereau et al. (1999b) Augereau, J. C., Lagrange, A. M., Mouillet, D., et al. 1999b, A&A, 348, 557
  * Avenhaus et al. (2018) Avenhaus, H., Quanz, S. P., Garufi, A., et al. 2018, ApJ, 863, 44
  * Bagnasco et al. (2007) Bagnasco, G., Kolm, M., Ferruit, P., et al. 2007, Proc. SPIE, 6692, 66920M
  * Barman et al. (2015) Barman, T. S., Konopacky, Q. M., Macintosh, B., & Marois, C. 2015, ApJ, 804, 61
  * Barman et al. (2011) Barman, T. S., Macintosh, B., Konopacky, Q. M., & Marois, C. 2011, ApJ, 733, 65
  * Baudoz et al. (2006) Baudoz, P., Boccaletti, A., Riaud, P., et al. 2006, PASP, 118, 765
  * Beichman et al. (2012) Beichman, C. A., Rieke, M., Eisenstein, D., et al. 2012, Proc. SPIE, 8442, 84422N
  * Beichman et al. (2010) Beichman, C. A., Krist, J., Trauger, J. T., et al. 2010, PASP, 122, 162
  * Berné et al. (2009) Berné, O., Joblin, C., Fuente, A., & Ménard, F. 2009, A&A, 495, 827
  * Beuzit et al. (2019) Beuzit, J. L., Vigan, A., Mouillet, D., et al. 2019, A&A, 631, A155
  * Biller et al. (2015a) Biller, B. A., Vos, J., Bonavita, M., et al. 2015a, ApJ, 813, L23
  * Biller et al. (2015b) Biller, B. A., Liu, M. C., Rice, K., et al. 2015b, MNRAS, 450, 4446
  * Birkmann et al. (2022) Birkmann, S. M., Ferruit, P., Giardino, G., et al. 2022, arXiv, arXiv:2202.03309
  * Boccaletti et al. (2005) Boccaletti, A., Baudoz, P., Baudrand, J., et al. 2005, Advances in Space Research, 36, 1099
  * Boccaletti et al. (2015) Boccaletti, A., Lagage, P. O., Baudoz, P., et al. 2015, PASP, 127, 633
  * Bohn et al. (2020) Bohn, A. J., Kenworthy, M. A., Ginski, C., et al. 2020, ApJ, 898, L16
  * Bonavita (2020) Bonavita, M. 2020, Exo-DMC: Exoplanet Detection Map Calculator
  * Bowler (2016) Bowler, B. P. 2016, PASP, 128, 102001
  * Bowler et al. (2010) Bowler, B. P., Liu, M. C., Dupuy, T. J., & Cushing, M. C. 2010, ApJ, 723, 850
  * Bowler et al. (2020) Bowler, B. P., Zhou, Y., Morley, C. V., et al. 2020, ApJ, 893, L30
  * Bowler et al. (2017) Bowler, B. P., Liu, M. C., Mawet, D., et al. 2017, AJ, 153, 18
  * Brande et al. (2020) Brande, J., Barclay, T., Schlieder, J. E., et al. 2020, AJ, 159, 18
  * Bruzzone et al. (2020) Bruzzone, J. S., Metchev, S., Duchêne, G., et al. 2020, AJ, 159, 53
  * Burningham et al. (2021) Burningham, B., Faherty, J. K., Gonzales, E. C., et al. 2021, MNRAS, 506, 1944
  * Carter et al. (2021a) Carter, A. L., Skemer, A. J. I., Danielski, C., et al. 2021a, Proc. SPIE, 11823, 118230H
  * Carter et al. (2021b) Carter, A. L., Hinkley, S., Bonavita, M., et al. 2021b, MNRAS, 501, 1999
  * Cassan et al. (2012) Cassan, A., Kubas, D., Beaulieu, J. P., et al. 2012, Nature, 481, 167
  * Cavarroc et al. (2008) Cavarroc, C., Amiaux, J., Baudoz, P., et al. 2008, Proc. SPIE, 7010, 70100W
  * Chabrier et al. (2007) Chabrier, G., Baraffe, I., Selsis, F., et al. 2007, Protostars and Planets V, 623
  * Chauvin et al. (2004) Chauvin, G., Lagrange, A., Dumas, C., et al. 2004, A&A, 425, L29
  * Chauvin et al. (2017) Chauvin, G., Desidera, S., Lagrange, A.-M., et al. 2017, A&A, 605, L9
  * Cheetham et al. (2019) Cheetham, A. C., Samland, M., Brems, S. S., et al. 2019, A&A, 622, A80
  * Chiang et al. (2009) Chiang, E., Kite, E., Kalas, P., et al. 2009, ApJ, 693, 734
  * Chilcote et al. (2020) Chilcote, J., Konopacky, Q., De Rosa, R. J., et al. 2020, Proc. SPIE, 11447, 114471S
  * Choquet et al. (2016) Choquet, É., Perrin, M. D., Chen, C. H., et al. 2016, ApJ, 817, L2
  * Clampin et al. (2003) Clampin, M., Krist, J. E., Ardila, D. R., et al. 2003, AJ, 126, 385
  * Cridland et al. (2019) Cridland, A. J., Eistrup, C., & van Dishoeck, E. F. 2019, A&A, 627, A127
  * Cridland et al. (2016) Cridland, A. J., Pudritz, R. E., & Alessi, M. 2016, MNRAS, 461, 3274
  * Currie et al. (2011) Currie, T., Burrows, A., Itoh, Y., et al. 2011, ApJ, 729, 128
  * Currie et al. (2016) Currie, T., Grady, C. A., Cloutier, R., et al. 2016, ApJ, 819, L26
  * Currie et al. (2018) Currie, T., Brandt, T. D., Uyama, T., et al. 2018, AJ, 156, 291
  * Cushing et al. (2006) Cushing, M. C., Roellig, T. L., Marley, M. S., et al. 2006, ApJ, 648, 614
  * Cutri et al. (2003) Cutri, R. M., Skrutskie, M. F., van Dyk, S., et al. 2003, 2MASS All Sky Catalog of point sources.
  * De Rosa et al. (2016) De Rosa, R. J., Rameau, J., Patience, J., et al. 2016, ApJ, 824, 121
  * de Zeeuw et al. (1999) de Zeeuw, P. T., Hoogerwerf, R., de Bruijne, J. H. J., et al. 1999, AJ, 117, 354
  * Desidera et al. (2021) Desidera, S., Chauvin, G., Bonavita, M., et al. 2021, A&A, 651, A70
  * Di Folco et al. (2020) Di Folco, E., Péricaud, J., Dutrey, A., et al. 2020, A&A, 635, A94
  * Doyon et al. (2012) Doyon, R., Hutchings, J. B., Beaulieu, M., et al. 2012, Proc. SPIE, 8442, 84422R
  * Dressing & Charbonneau (2013) Dressing, C. D., & Charbonneau, D. 2013, ApJ, 767, 95
  * Dupuy et al. (2020) Dupuy, T. J., Liu, M. C., Magnier, E. A., et al. 2020, Research Notes of the American Astronomical Society, 4, 54
  * Esposito et al. (2020) Esposito, T. M., Kalas, P., Fitzgerald, M. P., et al. 2020, AJ, 160, 24
  * Faherty et al. (2016) Faherty, J. K., Riedel, A. R., Cruz, K. L., et al. 2016, ApJS, 225, 10
  * Fernandes et al. (2019) Fernandes, R. B., Mulders, G. D., Pascucci, I., et al. 2019, ApJ, 874, 81
  * Filacchione et al. (2014) Filacchione, G., Ciarniello, M., Capaccioni, F., et al. 2014, icarus, 241, 45
  * Forgan & Rice (2013) Forgan, D., & Rice, K. 2013, MNRAS, 432, 3168
  * Fortney et al. (2013) Fortney, J. J., Mordasini, C., Nettelmann, N., et al. 2013, ApJ, 775, 80
  * Frelikh et al. (2019) Frelikh, R., Jang, H., Murray-Clay, R. A., & Petrovich, C. 2019, ApJ, 884, L47
  * Freytag et al. (1996) Freytag, B., Ludwig, H. G., & Steffen, M. 1996, A&A, 313, 497
  * Fulton et al. (2021) Fulton, B. J., Rosenthal, L. J., Hirsch, L. A., et al. 2021, ApJS, 255, 14
  * Gaia Collaboration et al. (2018) Gaia Collaboration, Brown, A. G. A., Vallenari, A., et al. 2018, Astronomy & Astrophysics, 616, A1
  * Galicher et al. (2011) Galicher, R., Marois, C., Macintosh, B., et al. 2011, ApJ, 739, L41
  * Gardner et al. (2006) Gardner, J. P., Mather, J. C., Clampin, M., et al. 2006, Space Sci. Rev., 123, 485
  * Gauza et al. (2015) Gauza, B., Béjar, V. J. S., Pérez-Garrido, A., et al. 2015, ApJ, 804, 96
  * Girard et al. (2018) Girard, J. H., Blair, W., Brooks, B., et al. 2018, Proc. SPIE, 10698, 106983V
  * Gravity Collaboration et al. (2019) Gravity Collaboration, Lacour, S., Nowak, M., et al. 2019, Astronomy & Astrophysics, 623, L11
  * Green et al. (2005) Green, J. J., Beichman, C., Basinger, S. A., et al. 2005, Proc. SPIE, 5905, 185
  * Greenbaum et al. (2014) Greenbaum, A. Z., Pueyo, L., Sivaramakrishnan, A., & Lacour, S. 2014, ArXiv e-prints
  * Grillmair et al. (2007) Grillmair, C. J., Charbonneau, D., Burrows, A., et al. 2007, ApJ, 658, L115
  * Grillmair et al. (2008) Grillmair, C. J., Burrows, A., Charbonneau, D., et al. 2008, Nature, 456, 767
  * Hagan et al. (2018) Hagan, J. B., Choquet, É., Soummer, R., & Vigan, A. 2018, AJ, 155, 179
  * Hinkley et al. (2011a) Hinkley, S., Carpenter, J. M., Ireland, M. J., & Kraus, A. L. 2011a, ApJ, 730, L21+
  * Hinkley et al. (2007) Hinkley, S., Oppenheimer, B. R., Soummer, R., et al. 2007, ApJ, 654, 633
  * Hinkley et al. (2011b) Hinkley, S., Oppenheimer, B. R., Zimmerman, N., et al. 2011b, PASP, 123, 74
  * Hinkley et al. (2015a) Hinkley, S., Bowler, B. P., Vigan, A., et al. 2015a, ApJ, 805, L10
  * Hinkley et al. (2015b) Hinkley, S., Kraus, A. L., Ireland, M. J., et al. 2015b, ApJ, 806, L9
  * Hinkley et al. (2021) Hinkley, S., Matthews, E. C., Lefevre, C., et al. 2021, ApJ, 912, 115
  * Hubeny & Burrows (2007) Hubeny, I., & Burrows, A. 2007, ApJ, 669, 1248
  * Hughes et al. (2018) Hughes, A. M., Duchêne, G., & Matthews, B. C. 2018, ARA&A, 56, 541
  * Ireland (2013) Ireland, M. J. 2013, MNRAS, 433, 1718
  * Jakobsen et al. (2022) Jakobsen, P., Ferruit, P., Alves de Oliveira, C., et al. 2022, arXiv, arXiv:2202.03305
  * Janson et al. (2015) Janson, M., Quanz, S. P., Carson, J. C., et al. 2015, A&A, 574, A120
  * Janson et al. (2021) Janson, M., Gratton, R., Rodet, L., et al. 2021, Nature, 600, 231
  * Kammerer et al. (2021) Kammerer, J., Lacour, S., Stolker, T., et al. 2021, Astronomy & Astrophysics, 652, A57
  * Kitzmann & Stock (2018) Kitzmann, D., & Stock, J. 2018, FastChem: An ultra-fast equilibrium chemistry
  * Konishi et al. (2016) Konishi, M., Grady, C. A., Schneider, G., et al. 2016, ApJ, 818, L23
  * Konopacky et al. (2013) Konopacky, Q. M., Barman, T. S., Macintosh, B. A., & Marois, C. 2013, Science, 339, 1398
  * Kratter et al. (2010) Kratter, K. M., Murray-Clay, R. A., & Youdin, A. N. 2010, ApJ, 710, 1375
  * Kraus & Ireland (2012) Kraus, A. L., & Ireland, M. J. 2012, ApJ, 745, 5
  * Kraus et al. (2011) Kraus, A. L., Ireland, M. J., Martinache, F., & Hillenbrand, L. A. 2011, ApJ, 731, 8
  * Krist et al. (2012) Krist, J. E., Stapelfeldt, K. R., Bryden, G., & Plavchan, P. 2012, ArXiv e-prints
  * Krist et al. (2007) Krist, J. E., Beichman, C. A., Trauger, J. T., et al. 2007, Proc. SPIE, 6693, 66930H
  * Kupka et al. (2018) Kupka, F., Zaussinger, F., & Montgomery, M. H. 2018, MNRAS, 474, 4660
  * Lacour et al. (2021) Lacour, S., Wang, J. J., Rodet, L., et al. 2021, Astronomy & Astrophysics, 654, L2
  * Lafrenière et al. (2009) Lafrenière, D., Marois, C., Doyon, R., & Barman, T. 2009, ApJ, 694, L148
  * Lafrenière et al. (2007) Lafrenière, D., Marois, C., Doyon, R., et al. 2007, ApJ, 660, 770
  * Lagrange et al. (2010) Lagrange, A., Bonnefoy, M., Chauvin, G., et al. 2010, Science, 329, 57
  * Lagrange et al. (2020) Lagrange, A. M., Rubini, P., Nowak, M., et al. 2020, A&A, 642, A18
  * Lajoie et al. (2016) Lajoie, C.-P., Soummer, R., Pueyo, L., et al. 2016, Proc. SPIE, 9904, 99045K
  * Langlois et al. (2021) Langlois, M., Gratton, R., Lagrange, A. M., et al. 2021, A&A, 651, A71
  * Liu et al. (2016) Liu, M. C., Dupuy, T. J., & Allers, K. N. 2016, ApJ, 833, 96
  * Looper et al. (2008) Looper, D. L., Kirkpatrick, J. D., Cutri, R. M., et al. 2008, ApJ, 686, 528
  * Macintosh et al. (2015) Macintosh, B., Graham, J. R., Barman, T., et al. 2015, Science, 350, 64
  * Macintosh et al. (2018) Macintosh, B., Chilcote, J. K., Bailey, V. P., et al. 2018, Proc. SPIE, 10703, 107030K
  * Madhusudhan et al. (2017) Madhusudhan, N., Bitsch, B., Johansen, A., & Eriksson, L. 2017, MNRAS, 469, 4102
  * Males et al. (2018) Males, J. R., Close, L. M., Miller, K., et al. 2018, Proc. SPIE, 10703, 1070309
  * Manjavacas et al. (2021) Manjavacas, E., Karalidi, T., Vos, J. M., et al. 2021, AJ, 162, 179
  * Marley et al. (2012) Marley, M. S., Saumon, D., Cushing, M., et al. 2012, ApJ, 754, 135
  * Marois et al. (2006) Marois, C., Lafrenière, D., Doyon, R., et al. 2006, ApJ, 641, 556
  * Marois et al. (2008) Marois, C., Macintosh, B., Barman, T., et al. 2008, Science, 322, 1348
  * Marois et al. (2010) Marois, C., Zuckerman, B., Konopacky, Q. M., et al. 2010, Nature, 468, 1080
  * Matthews et al. (2017) Matthews, E., Hinkley, S., Vigan, A., et al. 2017, ApJ, 843, L12
  * Matthews et al. (2018) —. 2018, MNRAS, 480, 2757
  * Mawet et al. (2014) Mawet, D., Milli, J., Wahhaj, Z., et al. 2014, ApJ, 792, 97
  * Mawet et al. (2017) Mawet, D., Choquet, É., Absil, O., et al. 2017, AJ, 153, 44
  * Mawet et al. (2018) Mawet, D., Bond, C. Z., Delorme, J. R., et al. 2018, Proc. SPIE, 10703, 1070306
  * Metchev & Hillenbrand (2006) Metchev, S. A., & Hillenbrand, L. A. 2006, ApJ, 651, 1166
  * Metchev et al. (2015) Metchev, S. A., Heinze, A., Apai, D., et al. 2015, ApJ, 799, 154
  * Miles et al. (2018) Miles, B. E., Skemer, A. J., Barman, T. S., et al. 2018, ApJ, 869, 18
  * Miles et al. (2020) Miles, B. E., Skemer, A. J. I., Morley, C. V., et al. 2020, AJ, 160, 63
  * Millar-Blanchaer et al. (2015) Millar-Blanchaer, M. A., Graham, J. R., Pueyo, L., et al. 2015, ApJ, 811, 18
  * Moerchen et al. (2010) Moerchen, M. M., Telesco, C. M., & Packham, C. 2010, ApJ, 723, 1418
  * Mollière et al. (2020) Mollière, P., Stolker, T., Lacour, S., et al. 2020, A&A, 640, A131
  * Mollière et al. (2022) Mollière, P., Molyarova, T., Bitsch, B., et al. 2022, arXiv, arXiv:2204.13714
  * Mordasini et al. (2016) Mordasini, C., van Boekel, R., Mollière, P., et al. 2016, ApJ, 832, 41
  * Morley et al. (2012) Morley, C. V., Fortney, J. J., Marley, M. S., et al. 2012, ApJ, 756, 172
  * Morley et al. (2017) Morley, C. V., Kreidberg, L., Rustamkulov, Z., et al. 2017, ApJ, 850, 121
  * Morrison & Malhotra (2015) Morrison, S., & Malhotra, R. 2015, ApJ, 799, 41
  * Mouillet et al. (2001) Mouillet, D., Lagrange, A. M., Augereau, J. C., & Ménard, F. 2001, A&A, 372, L61
  * Nielsen et al. (2019) Nielsen, E. L., De Rosa, R. J., Macintosh, B., et al. 2019, AJ, 158, 13
  * Nowak et al. (2020) Nowak, M., Lacour, S., Lagrange, A. M., et al. 2020, A&A, 642, L2
  * Öberg et al. (2011) Öberg, K. I., Murray-Clay, R., & Bergin, E. A. 2011, ApJ, 743, L16
  * Otten et al. (2021) Otten, G. P. P. L., Vigan, A., Muslimov, E., et al. 2021, A&A, 646, A150
  * Perrin et al. (2018) Perrin, M. D., Pueyo, L., Van Gorkom, K., et al. 2018, Proc. SPIE, 10698, 1069809
  * Perrot et al. (2016) Perrot, C., Boccaletti, A., Pantin, E., et al. 2016, A&A, 590, L7
  * Petrus et al. (2021) Petrus, S., Bonnefoy, M., Chauvin, G., et al. 2021, A&A, 648, A59
  * Phillips et al. (2020) Phillips, M. W., Tremblin, P., Baraffe, I., et al. 2020, A&A, 637, A38
  * Pinte et al. (2006) Pinte, C., Ménard, F., Duchêne, G., & Bastien, P. 2006, A&A, 459, 797
  * Poleski et al. (2021) Poleski, R., Skowron, J., Mróz, P., et al. 2021, Acta Astron., 71, 1
  * Pollack et al. (1996) Pollack, J. B., Hubickyj, O., Bodenheimer, P., et al. 1996, Icarus, 124, 62
  * Pontoppidan et al. (2016) Pontoppidan, K. M., Pickering, T. E., Laidler, V. G., et al. 2016, Proc. SPIE, 9910, 991016
  * Preibisch & Mamajek (2008) Preibisch, T., & Mamajek, E. 2008, The Nearest OB Association: Scorpius-Centaurus (Sco OB2), ed. B. Reipurth, 235
  * Pueyo (2016) Pueyo, L. 2016, ApJ, 824, 117
  * Quanz et al. (2021) Quanz, S. P., Absil, O., Benz, W., et al. 2021, Experimental Astronomy
  * Radigan et al. (2014) Radigan, J., Lafrenière, D., Jayawardhana, R., & Artigau, E. 2014, ApJ, 793, 75
  * Rameau et al. (2013) Rameau, J., Chauvin, G., Lagrange, A.-M., et al. 2013, ApJ, 772, L15
  * Ren et al. (2020) Ren, B., Pueyo, L., Chen, C., et al. 2020, ApJ, 892, 74
  * Ren et al. (2018) Ren, B., Pueyo, L., Zhu, G. B., et al. 2018, ApJ, 852, 104
  * Rich et al. (2016) Rich, E. A., Currie, T., Wisniewski, J. P., et al. 2016, ApJ, 830, 114
  * Rieke et al. (2015) Rieke, G. H., Wright, G. S., Böker, T., et al. 2015, PASP, 127, 584
  * Rieke et al. (2005) Rieke, M. J., Kelly, D., & Horner, S. 2005, Proc. SPIE, 5904, 1
  * Rizzuto et al. (2011) Rizzuto, A. C., Ireland, M. J., & Robertson, J. G. 2011, MNRAS, 416, 3108
  * Rouan et al. (2000) Rouan, D., Riaud, P., Boccaletti, A., et al. 2000, PASP, 112, 1479
  * Ruane et al. (2019) Ruane, G., Ngo, H., Mawet, D., et al. 2019, AJ, 157, 118
  * Sallum & Skemer (2019) Sallum, S., & Skemer, A. 2019, JATIS, 5, 018001
  * Sallum et al. (2015) Sallum, S., Follette, K. B., Eisner, J. A., et al. 2015, Nature, 527, 342
  * Sanghi et al. (2022) Sanghi, A., Zhou, Y., & Bowler, B. P. 2022, AJ, 163, 119
  * Schlieder et al. (2016) Schlieder, J. E., Beichman, C. A., Meyer, M. R., & Greene, T. 2016, IAU Symposium, 314, 288
  * Schneider et al. (2017) Schneider, G., Gaspar, A., Debes, J., et al. 2017, Enabling Narrow(est) IWA Coronagraphy with STIS BAR5 and BAR10 Occulters, Tech. rep., STScI, 3
  * Schneider et al. (2014) Schneider, G., Grady, C. A., Hines, D. C., et al. 2014, AJ, 148, 59
  * Sing et al. (2016) Sing, D. K., Fortney, J. J., Nikolov, N., et al. 2016, Nature, 529, 59
  * Singh et al. (2021) Singh, G., Bhowmik, T., Boccaletti, A., et al. 2021, A&A, 653, A79
  * Sivaramakrishnan et al. (2010) Sivaramakrishnan, A., Lafrenière, D., Tuthill, P. G., et al. 2010, Proc. SPIE, 7731, 77313W
  * Sivaramakrishnan et al. (2012) Sivaramakrishnan, A., Lafrenière, D., Ford, K. E. S., et al. 2012, Proc. SPIE, 8442, 84422S
  * Skemer et al. (2012) Skemer, A. J., Hinz, P. M., Esposito, S., et al. 2012, ApJ, 753, 14
  * Skemer et al. (2014) Skemer, A. J., Marley, M. S., Hinz, P. M., et al. 2014, ApJ, 792, 17
  * Skemer et al. (2016) Skemer, A. J., Morley, C. V., Zimmerman, N. T., et al. 2016, ApJ, 817, 166
  * Sloan et al. (2005) Sloan, G. C., Keller, L. D., Forrest, W. J., et al. 2005, ApJ, 632, 956
  * Snellen et al. (2015) Snellen, I., de Kok, R., Birkby, J. L., et al. 2015, A&A, 576, A59
  * Sorahana & Yamamura (2012) Sorahana, S., & Yamamura, I. 2012, ApJ, 760, 151
  * Soulain et al. (2020) Soulain, A., Sivaramakrishnan, A., Tuthill, P., et al. 2020, Proc. SPIE, 11446, 1144611
  * Soummer et al. (2011) Soummer, R., Hagan, J. B., Pueyo, L., et al. 2011, ApJ, 741, 55
  * Soummer et al. (2012) Soummer, R., Pueyo, L., & Larkin, J. 2012, ApJ, 755, L28
  * Soummer et al. (2014) Soummer, R., Perrin, M. D., Pueyo, L., et al. 2014, ApJ, 786, L23
  * Stolker et al. (2020) Stolker, T., Quanz, S. P., Todorov, K. O., et al. 2020, Astronomy & Astrophysics, 635, A182
  * Stone et al. (2016) Stone, J. M., Skemer, A. J., Kratter, K. M., et al. 2016, ApJ, 818, L12
  * Takeuchi & Artymowicz (2001) Takeuchi, T., & Artymowicz, P. 2001, ApJ, 557, 990
  * The LUVOIR Team (2019) The LUVOIR Team. 2019, arXiv, arXiv:1912.06219
  * Thébault (2009) Thébault, P. 2009, A&A, 505, 1269
  * Thi et al. (2014) Thi, W. F., Pinte, C., Pantin, E., et al. 2014, A&A, 561, A50
  * Tremblin et al. (2016) Tremblin, P., Amundsen, D. S., Chabrier, G., et al. 2016, ApJ, 817, L19
  * Tremblin et al. (2015) Tremblin, P., Amundsen, D. S., Mourier, P., et al. 2015, ApJ, 804, L17
  * Tuthill et al. (2000) Tuthill, P. G., Monnier, J. D., Danchi, W. C., et al. 2000, PASP, 112, 555
  * Vigan et al. (2018) Vigan, A., Otten, G. P. P. L., Muslimov, E., et al. 2018, Proc. SPIE, 10702, 1070236
  * Vigan et al. (2021) Vigan, A., Fontanive, C., Meyer, M., et al. 2021, A&A, 651, A72
  * Vos et al. (2017) Vos, J. M., Allers, K. N., & Biller, B. A. 2017, ApJ, 842, 78
  * Vos et al. (2020) Vos, J. M., Biller, B. A., Allers, K. N., et al. 2020, AJ, 160, 38
  * Wagner et al. (2019) Wagner, K., Apai, D., & Kratter, K. M. 2019, ApJ, 877, 46
  * Wagner et al. (2021) Wagner, K., Boehle, A., Pathak, P., et al. 2021, Nature Communications, 12, 922
  * Wahhaj et al. (2021) Wahhaj, Z., Milli, J., Romero, C., et al. 2021, A&A, 648, A26
  * Waldmann et al. (2015a) Waldmann, I. P., Rocchetto, M., Tinetti, G., et al. 2015a, ApJ, 813, 13
  * Waldmann et al. (2015b) Waldmann, I. P., Tinetti, G., Rocchetto, M., et al. 2015b, ApJ, 802, 107
  * Wang et al. (2015) Wang, J. J., Ruffio, J.-B., De Rosa, R. J., et al. 2015, pyKLIP: PSF Subtraction for Exoplanets and Disks
  * Wang et al. (2021a) Wang, J. J., Vigan, A., Lacour, S., et al. 2021a, Astronomical Journal, 161, 148
  * Wang et al. (2021b) Wang, J. J., Ruffio, J.-B., Morris, E., et al. 2021b, AJ, 162, 148
  * Weinberger et al. (1999) Weinberger, A. J., Becklin, E. E., Schneider, G., et al. 1999, ApJ, 525, L53
  * Weinberger et al. (2000) Weinberger, A. J., Rich, R. M., Becklin, E. E., et al. 2000, ApJ, 544, 937
  * Wright et al. (2015) Wright, G. S., Wright, D., Goodson, G. B., et al. 2015, PASP, 127, 595
  * Wyatt (2003) Wyatt, M. C. 2003, ApJ, 598, 1321
  * Wyatt (2005) —. 2005, A&A, 440, 937
  * Wyatt (2008) —. 2008, ARA&A, 46, 339
  * Wyatt et al. (2015) Wyatt, M. C., Panić, O., Kennedy, G. M., & Matrà, L. 2015, Ap&SS, 357, 103
  * Xuan et al. (2018) Xuan, W. J., Mawet, D., Ngo, H., et al. 2018, AJ, 156, 156
  * Zahnle et al. (2016) Zahnle, K., Marley, M. S., Morley, C. V., & Moses, J. I. 2016, ApJ, 824, 137
  * Zhou et al. (2020) Zhou, Y., Bowler, B. P., Morley, C. V., et al. 2020, AJ, 160, 77
  * Zhou et al. (2021) Zhou, Y., Bowler, B. P., Wagner, K. R., et al. 2021, AJ, 161, 244