11institutetext:  LESIA, Observatoire de Paris, Université PSL, CNRS, Sorbonne
Université, Univ. Paris Diderot, Sorbonne Paris Cité, 5 place Jules Janssen,
92195 Meudon, France 22institutetext: Université Paris-Saclay, Université
Paris Cité, CEA, CNRS, AIM, 91191, Gif-sur-Yvette, France 33institutetext:
Max-Planck-Institut für Astronomie (MPIA), Königstuhl 17, 69117 Heidelberg,
Germany 44institutetext: Institute for Astronomy, University of Edinburgh,
Royal Observatory, Blackford Hill, Edinburgh EH9 3HJ 55institutetext:
Department of Astrophysics/IMAPP, Radboud University, PO Box 9010, 6500 GL
Nijmegen, the Netherlands 66institutetext: HFML - FELIX. Radboud University PO
box 9010, 6500 GL Nijmegen, the Netherlands 77institutetext: SRON Netherlands
Institute for Space Research, Niels Bohrweg 4, 2333 CA Leiden, the Netherlands
88institutetext: Department of Astrophysics, University of Vienna,
Türkenschanzstrasse 17, 1180 Vienna, Austria 99institutetext: ETH Zürich,
Institute for Particle Physics and Astrophysics, Wolfgang-Pauli-Strasse 27,
8093 Zürich, Switzerland 1010institutetext: Institute of Astronomy, KU Leuven,
Celestijnenlaan 200D, 3001 Leuven, Belgium 1111institutetext: STAR Institute,
Université de Liège, Allée du Six Août 19c, 4000 Liège, Belgium
1212institutetext: Université Paris-Saclay, CEA, Département d’Electronique
des Détecteurs et d’Informatique pour la Physique, 91191, Gif-sur-Yvette,
France 1313institutetext: LERMA, Observatoire de Paris, Université PSL,
Sorbonne Université, CNRS, Paris, France 1414institutetext: UK Astronomy
Technology Centre, Royal Observatory, Blackford Hill, Edinburgh EH9 3HJ, UK
1515institutetext: Kapteyn Institute of Astronomy, University of Groningen,
Landleven 12, 9747 AD Groningen, the Netherlands 1616institutetext: European
Space Agency, Space Telescope Science Institute, Baltimore, MD, USA
1717institutetext: Department of Astronomy, Stockholm University, AlbaNova
University Center, 10691 Stockholm, Sweden 1818institutetext: School of
Physics & Astronomy, Space Research Centre, Space Park Leicester, University
of Leicester, 92 Corporation Road, Leicester, LE4 5SP, UK 1919institutetext:
Centro de Astrobiología (CAB), CSIC-INTA, ESAC Campus, Camino Bajo del
Castillo s/n, 28692 Villanueva de la Cañada, Madrid, Spain 2020institutetext:
Leiden Observatory, Leiden University, P.O. Box 9513, 2300 RA Leiden, the
Netherlands 2121institutetext: Department of Astronomy, Oskar Klein Centre,
Stockholm University, 106 91 Stockholm, Sweden 2222institutetext: School of
Cosmic Physics, Dublin Institute for Advanced Studies, 31 Fitzwilliam Place,
Dublin, D02 XF86, Ireland 2323institutetext: Jet Propulsion Laboratory,
California Institute of Technology, Pasadena, California, United States

# Imaging detection of the inner dust belt and the four exoplanets in the HR
8799 system with JWST’s MIRI coronagraph

Anthony Boccaletti  0000-0001-9353-2724 Imaging detection of the inner dust
belt and the four exoplanets in the HR 8799 system with JWST’s MIRI
coronagraphImaging detection of the inner dust belt and the four exoplanets in
the HR 8799 system with JWST’s MIRI coronagraph Mathilde Mâlin Imaging
detection of the inner dust belt and the four exoplanets in the HR 8799 system
with JWST’s MIRI coronagraphImaging detection of the inner dust belt and the
four exoplanets in the HR 8799 system with JWST’s MIRI coronagraph Pierre
Baudoz Imaging detection of the inner dust belt and the four exoplanets in the
HR 8799 system with JWST’s MIRI coronagraphImaging detection of the inner dust
belt and the four exoplanets in the HR 8799 system with JWST’s MIRI
coronagraph Pascal Tremblin  0000-0001-6172-3403 Imaging detection of the
inner dust belt and the four exoplanets in the HR 8799 system with JWST’s MIRI
coronagraphImaging detection of the inner dust belt and the four exoplanets in
the HR 8799 system with JWST’s MIRI coronagraph Clément Perrot
0000-0003-3831-0381 Imaging detection of the inner dust belt and the four
exoplanets in the HR 8799 system with JWST’s MIRI coronagraphImaging detection
of the inner dust belt and the four exoplanets in the HR 8799 system with
JWST’s MIRI coronagraph Daniel Rouan Imaging detection of the inner dust belt
and the four exoplanets in the HR 8799 system with JWST’s MIRI
coronagraphImaging detection of the inner dust belt and the four exoplanets in
the HR 8799 system with JWST’s MIRI coronagraph Pierre-Olivier Lagage Imaging
detection of the inner dust belt and the four exoplanets in the HR 8799 system
with JWST’s MIRI coronagraphImaging detection of the inner dust belt and the
four exoplanets in the HR 8799 system with JWST’s MIRI coronagraph Niall
Whiteford Imaging detection of the inner dust belt and the four exoplanets in
the HR 8799 system with JWST’s MIRI coronagraphImaging detection of the inner
dust belt and the four exoplanets in the HR 8799 system with JWST’s MIRI
coronagraph Paul Mollière  0000-0003-4096-7067 Imaging detection of the inner
dust belt and the four exoplanets in the HR 8799 system with JWST’s MIRI
coronagraphImaging detection of the inner dust belt and the four exoplanets in
the HR 8799 system with JWST’s MIRI coronagraph Rens Waters Imaging detection
of the inner dust belt and the four exoplanets in the HR 8799 system with
JWST’s MIRI coronagraphImaging detection of the inner dust belt and the four
exoplanets in the HR 8799 system with JWST’s MIRI coronagraphImaging detection
of the inner dust belt and the four exoplanets in the HR 8799 system with
JWST’s MIRI coronagraphImaging detection of the inner dust belt and the four
exoplanets in the HR 8799 system with JWST’s MIRI coronagraphImaging detection
of the inner dust belt and the four exoplanets in the HR 8799 system with
JWST’s MIRI coronagraphImaging detection of the inner dust belt and the four
exoplanets in the HR 8799 system with JWST’s MIRI coronagraph Thomas Henning
Imaging detection of the inner dust belt and the four exoplanets in the HR
8799 system with JWST’s MIRI coronagraphImaging detection of the inner dust
belt and the four exoplanets in the HR 8799 system with JWST’s MIRI
coronagraph Leen Decin Imaging detection of the inner dust belt and the four
exoplanets in the HR 8799 system with JWST’s MIRI coronagraphImaging detection
of the inner dust belt and the four exoplanets in the HR 8799 system with
JWST’s MIRI coronagraph Manuel Güdel Imaging detection of the inner dust belt
and the four exoplanets in the HR 8799 system with JWST’s MIRI
coronagraphImaging detection of the inner dust belt and the four exoplanets in
the HR 8799 system with JWST’s MIRI coronagraphImaging detection of the inner
dust belt and the four exoplanets in the HR 8799 system with JWST’s MIRI
coronagraphImaging detection of the inner dust belt and the four exoplanets in
the HR 8799 system with JWST’s MIRI coronagraphImaging detection of the inner
dust belt and the four exoplanets in the HR 8799 system with JWST’s MIRI
coronagraphImaging detection of the inner dust belt and the four exoplanets in
the HR 8799 system with JWST’s MIRI coronagraph Bart Vandenbussche
0000-0002-1368-3109 Imaging detection of the inner dust belt and the four
exoplanets in the HR 8799 system with JWST’s MIRI coronagraphImaging detection
of the inner dust belt and the four exoplanets in the HR 8799 system with
JWST’s MIRI coronagraph Olivier Absil Imaging detection of the inner dust belt
and the four exoplanets in the HR 8799 system with JWST’s MIRI
coronagraphImaging detection of the inner dust belt and the four exoplanets in
the HR 8799 system with JWST’s MIRI coronagraph Ioannis Argyriou
0000-0003-2820-1077 Imaging detection of the inner dust belt and the four
exoplanets in the HR 8799 system with JWST’s MIRI coronagraphImaging detection
of the inner dust belt and the four exoplanets in the HR 8799 system with
JWST’s MIRI coronagraph Jeroen Bouwman  0000-0003-4757-2500 Imaging detection
of the inner dust belt and the four exoplanets in the HR 8799 system with
JWST’s MIRI coronagraphImaging detection of the inner dust belt and the four
exoplanets in the HR 8799 system with JWST’s MIRI coronagraph Christophe
Cossou  0000-0001-5350-4796 Imaging detection of the inner dust belt and the
four exoplanets in the HR 8799 system with JWST’s MIRI coronagraphImaging
detection of the inner dust belt and the four exoplanets in the HR 8799 system
with JWST’s MIRI coronagraph Alain Coulais  0000-0001-6492-7719 Imaging
detection of the inner dust belt and the four exoplanets in the HR 8799 system
with JWST’s MIRI coronagraphImaging detection of the inner dust belt and the
four exoplanets in the HR 8799 system with JWST’s MIRI coronagraphImaging
detection of the inner dust belt and the four exoplanets in the HR 8799 system
with JWST’s MIRI coronagraphImaging detection of the inner dust belt and the
four exoplanets in the HR 8799 system with JWST’s MIRI coronagraph René
Gastaud Imaging detection of the inner dust belt and the four exoplanets in
the HR 8799 system with JWST’s MIRI coronagraphImaging detection of the inner
dust belt and the four exoplanets in the HR 8799 system with JWST’s MIRI
coronagraph Alistair Glasse  0000-0002-2041-2462 Imaging detection of the
inner dust belt and the four exoplanets in the HR 8799 system with JWST’s MIRI
coronagraphImaging detection of the inner dust belt and the four exoplanets in
the HR 8799 system with JWST’s MIRI coronagraph Adrian M. Glauser
0000-0001-9250-1547 Imaging detection of the inner dust belt and the four
exoplanets in the HR 8799 system with JWST’s MIRI coronagraphImaging detection
of the inner dust belt and the four exoplanets in the HR 8799 system with
JWST’s MIRI coronagraph Inga Kamp  0000-0001-7455-5349 Imaging detection of
the inner dust belt and the four exoplanets in the HR 8799 system with JWST’s
MIRI coronagraphImaging detection of the inner dust belt and the four
exoplanets in the HR 8799 system with JWST’s MIRI coronagraph Sarah Kendrew
0000-0002-7612-0469 Imaging detection of the inner dust belt and the four
exoplanets in the HR 8799 system with JWST’s MIRI coronagraphImaging detection
of the inner dust belt and the four exoplanets in the HR 8799 system with
JWST’s MIRI coronagraph Oliver Krause Imaging detection of the inner dust belt
and the four exoplanets in the HR 8799 system with JWST’s MIRI
coronagraphImaging detection of the inner dust belt and the four exoplanets in
the HR 8799 system with JWST’s MIRI coronagraph Fred Lahuis Imaging detection
of the inner dust belt and the four exoplanets in the HR 8799 system with
JWST’s MIRI coronagraphImaging detection of the inner dust belt and the four
exoplanets in the HR 8799 system with JWST’s MIRI coronagraph Michael Mueller
0000-0003-3217-5385 Imaging detection of the inner dust belt and the four
exoplanets in the HR 8799 system with JWST’s MIRI coronagraphImaging detection
of the inner dust belt and the four exoplanets in the HR 8799 system with
JWST’s MIRI coronagraph Goran Olofsson  0000-0003-3747-7120 Imaging detection
of the inner dust belt and the four exoplanets in the HR 8799 system with
JWST’s MIRI coronagraphImaging detection of the inner dust belt and the four
exoplanets in the HR 8799 system with JWST’s MIRI coronagraph Polychronis
Patapis  0000-0001-8718-3732 Imaging detection of the inner dust belt and the
four exoplanets in the HR 8799 system with JWST’s MIRI coronagraphImaging
detection of the inner dust belt and the four exoplanets in the HR 8799 system
with JWST’s MIRI coronagraph John Pye Imaging detection of the inner dust belt
and the four exoplanets in the HR 8799 system with JWST’s MIRI
coronagraphImaging detection of the inner dust belt and the four exoplanets in
the HR 8799 system with JWST’s MIRI coronagraph Pierre Royer
0000-0001-9341-2546 Imaging detection of the inner dust belt and the four
exoplanets in the HR 8799 system with JWST’s MIRI coronagraphImaging detection
of the inner dust belt and the four exoplanets in the HR 8799 system with
JWST’s MIRI coronagraph Eugene Serabyn Imaging detection of the inner dust
belt and the four exoplanets in the HR 8799 system with JWST’s MIRI
coronagraphImaging detection of the inner dust belt and the four exoplanets in
the HR 8799 system with JWST’s MIRI coronagraph Silvia Scheithauer
0000-0003-4559-0721 Imaging detection of the inner dust belt and the four
exoplanets in the HR 8799 system with JWST’s MIRI coronagraphImaging detection
of the inner dust belt and the four exoplanets in the HR 8799 system with
JWST’s MIRI coronagraph Luis Colina  0000-0002-9090-4227 Imaging detection of
the inner dust belt and the four exoplanets in the HR 8799 system with JWST’s
MIRI coronagraphImaging detection of the inner dust belt and the four
exoplanets in the HR 8799 system with JWST’s MIRI coronagraph Ewine F. van
Dishoeck  0000-0001-7591-1907 Imaging detection of the inner dust belt and the
four exoplanets in the HR 8799 system with JWST’s MIRI coronagraphImaging
detection of the inner dust belt and the four exoplanets in the HR 8799 system
with JWST’s MIRI coronagraph Göran Ostlin Imaging detection of the inner dust
belt and the four exoplanets in the HR 8799 system with JWST’s MIRI
coronagraphImaging detection of the inner dust belt and the four exoplanets in
the HR 8799 system with JWST’s MIRI coronagraph Tom P. Ray Imaging detection
of the inner dust belt and the four exoplanets in the HR 8799 system with
JWST’s MIRI coronagraphImaging detection of the inner dust belt and the four
exoplanets in the HR 8799 system with JWST’s MIRI coronagraph Gillian Wright
Imaging detection of the inner dust belt and the four exoplanets in the HR
8799 system with JWST’s MIRI coronagraphImaging detection of the inner dust
belt and the four exoplanets in the HR 8799 system with JWST’s MIRI
coronagraph

(submitted to A&A on Sept. 8th, 2023 )

###### Abstract

Context. The MIRI instrument onboard JWST is now offering high-contrast
imaging capacity at mid-IR wavelengths, thereby opening a completely new field
of investigation to characterize young exoplanetary systems.

Aims. The multiplanet system HR 8799 is the first target observed with MIRI’s
coronagraph as part of the MIRI-EC Guaranteed Time Observations exoplanets
programme, in Nov. 2022. We obtained deep observations in three coronagraphic
filters from $\sim 10$ to 15 $\muup$m (F1065C, F1140C, F1550C), and one
standard imaging filter at $\sim 20\,\muup$m (F2100W), with the goal to
extract the photometry of the four planets, as well as to detect and
investigate the distribution of circumstellar dust.

Methods. Using dedicated observations of a reference star, we tested several
algorithms to subtract the stellar diffraction pattern while preserving the
fluxes of planets, which can be significantly affected by over-subtraction.
Measuring correctly the planet’s flux values requires accounting for the
attenuation by the coronagraphs as a function of their position, and to
estimate the normalisation with respect to the central star. We tested several
procedures to derive averaged photometric values and error bars.

Results. These observations have enabled us to obtain two main results. First
of all, the four planets in the system are well recovered, and their mid-IR
fluxes, combined with near-IR flux values from the literature, are compared to
two exoplanet atmosphere models, ATMO and Exo-REM. As a main outcome, the MIRI
photometric data points imply larger radii (0.86 or 1.07 $R_{\mathrm{J}}$ for
planet b) and cooler temperatures (950 or 1100 K for planet b), especially for
planet b, in better agreement with evolutionary models. Second of all, these
JWST/MIRI coronagraphic data also deliver the first spatially resolved
detection of the inner warm debris disk, the radius of which is constrained to
about 15 au, with flux densities comparable, but lower than former unresolved
spectroscopic measurements with Spitzer.

Conclusions. The coronagraphs of MIRI cast a new vision of known exoplanetary
systems which differs significantly from more shorter wavelength high-contrast
images delivered by extreme adaptive optics from the ground. Inner dust belts
and background galaxies become predominant at some mid-IR wavelengths,
potentially causing confusion for the detection of exoplanets. Future
observing strategy and data reduction should take such features into account.

###### Key Words.:

Stars: individual (HR8799) – Exoplanets – Techniques: image processing –
Techniques: high angular resolution

Table 1: Main parameters of the observations during JWST/MIRI program 1194: date/time, order of execution in the sequence, filter, name of the object, type of object (target or reference, or background image), identifier of the sequence, number of groups, number of integrations, number of dither positions (9 is for the SGD, 4 or 1 is a classical dither, 2 is for the background), total exposure time per dither. date / time in UT | seq. | filter | object | type | obs id | Ngroup | Nint | Ndither | Texp
---|---|---|---|---|---|---|---|---|---
(month/day/year) |  |  |  |  |  |  |  |  | per dither (s)
11/07/2022 21:19 | 1 | F1065C | HR 8799 | TARG | obs 7 | 500 | 9 | 1 | 1080.48
11/07/2022 22:43 | 3 | F1065C | $-$ | BGD | obs 13 | 500 | 9 | 2 | 1080.48
11/07/2022 23:49 | 4 | F1065C | HD 218261 | REF | obs 14 | 500 | 2 | 9 | 239.92
11/08/2022 01:42 | 6 | F1065C | $-$ | BGD | obs 20 | 500 | 2 | 2 | 239.92
11/08/2022 08:24 | 7 | F1140C | HR 8799 | TARG | obs 8 | 500 | 9 | 1 | 1080.48
11/08/2022 13:25 | 10 | F1140C | $-$ | BGD | obs 12 | 500 | 9 | 2 | 1080.48
11/08/2022 14:27 | 11 | F1140C | HD 218261 | REF | obs 15 | 500 | 2 | 9 | 239.92
11/08/2022 16:42 | 14 | F1140C | $-$ | BGD | obs 19 | 500 | 2 | 2 | 239.92
11/08/2022 08:57 | 8 | F1550C | HR 8799 | TARG | obs 9 | 500 | 36 | 1 | 4322.63
11/08/2022 10:43 | 9 | F1550C | $-$ | BGD | obs 11 | 500 | 36 | 2 | 4322.63
11/08/2022 15:28 | 12 | F1550C | HD 218261 | REF | obs 16 | 500 | 2 | 9 | 239.92
11/08/2022 16:27 | 13 | F1550C | $-$ | BGD | obs 18 | 500 | 2 | 2 | 239.92
11/07/2022 22:06 | 2 | F2100W | HR 8799 | TARG | obs 10 | 20 | 50 | 4 | 314.20
11/08/2022 00:50 | 5 | F2100W | HD 218261 | REF | obs 17 | 20 | 05 | 4 | 314.20

##  1 Introduction

Massive giant planets in large orbits ($>$5 au) are found to be relatively
rare, as inferred from direct imaging surveys (Vigan et al., 2020; Nielsen et
al., 2019), and even the statistics from radial velocity surveys are not
complete in that case (Lagrange et al., 2023). Therefore, every single
detection is significant to add to our understanding of how such massive
planets can form in the outer parts of planetary systems, and how they can
impact the fate of other planets. Historically, young giant planets have been
discovered and characterized in the near-IR with general facilities, or
dedicated high-contrast instruments on the ground. But, little is known about
their properties at longer wavelengths. Observations from the ground at mid-IR
have proven to be difficult because of sensitivity issues and essentially
yield no clear detection (Wagner et al., 2021; Skaf et al., 2022). The James
Webb Space Telescope (JWST) mission (Gardner et al., 2023) is definitely a
game changer in this field by providing high-angular resolution together with
high-contrast capacities at wavelengths longer than $\sim 5\,\muup$m that were
not even accessible before. In this respect, mid-IR is crucial to put
independent constraints on planets luminosity, temperature, and radius, as
well as to provide access to molecules like ammonia. Carter et al. (2023)
presented JWST observations of a giant planet, previously discovered with
SPHERE at the VLT (Chauvin et al., 2017), and observed for the very first time
at 10 and 15 $\muup$m with the JWST Mid-Infrared Instrument (MIRI, Wright et
al., 2015) and its coronagraphic system (Boccaletti et al., 2015).

Since its discovery in 2008 at Keck (Marois et al., 2008, 2010), the HR 8799
multiplanet system has been the focus of numerous studies to explore
atmospheric properties, and dynamics, as well as the dust content of its
debris disk. Consisting of four giant planets with masses ranging roughly
between 5 and $10\,M_{J}$ according to hot start evolutionary models, the
system is often seen as an upscale version of a young Solar System (Faramaz et
al., 2021). The HR 8799 system is very unique among all known exoplanetary
systems as it provides a testbed for understanding the formation of planets
around early-type stars.

With the objective to characterize the atmospheres of these four planets,
spectral analysis has been performed for a broad range of near-IR wavelengths
from the z to M bands, in photometry (Currie et al., 2011; Skemer et al.,
2014), at low spectral resolution (Bonnefoy et al., 2016; Ingraham et al.,
2014; Lacour et al., 2019) and even with medium to high resolution (Barman et
al., 2011; Konopacky et al., 2013; Ruffio et al., 2021; Wang et al., 2022).
Their colors indicate that these four giant planets differ from field brown
dwarfs with notably redder colors, near the L/T transition, which require
patchy optically thick clouds and non-equilibrium chemistry to match the
observations. Their temperature range from 900 K to 1300 K, with planet b
being significantly cooler and fainter, while surface gravity ranges between
$\mathrm{log}(g)=3.5$ and $4.5$ (Bonnefoy et al., 2016). These near-IR data
suggest a tendency towards super-solar metallicity. While atmospheric models
reproduce rather well the near-IR photometry and spectrum for planets d and e,
they are much less effective in fitting planets b and c (Bonnefoy et al.,
2016). One issue has been the determination of planet radii which are found to
be too small, in particular for planet b with values as small as
$0.5-0.7\,R_{J}$. For an assumed age of about 30 Myrs evolutionary models
predict radius which can be as large $1.3\,R_{J}$.

At least two molecules are clearly identified, namely H2O and CO, even at low
spectral resolution, while there is a debate about the detection of methane,
which is not reproducible from one data reduction to the other (Barman et al.,
2015; Petit Dit De La Roche et al., 2018). This is one main difference with
field brown dwarfs, the spectra of which show the signature of methane at
temperatures cooler than $\sim$ 1300 K. In addition, the measurement of the
C/O ratio is claimed as one of the most promising ways to constrain the
formation history (Öberg et al., 2011), and to disentangle between accretion-
like and stellar-like mechanisms, the former scenario predicting super-solar
values. As of today, there is no clear consensus on the C/O ratio since
various studies yield solar, or marginally super-solar values (Mollière et
al., 2020; Konopacky et al., 2013; Ruffio et al., 2021). Some of the
atmospheric properties are well explained by self-consistent atmospheric
models like Exo-REM (Charnay et al., 2018). In particular, the colors with
respect to the L/T transition can be explained by low gravity planets with
clouds, although there is an alternative interpretation with cloudless models
but involving fingering convection (Tremblin et al., 2016). Charnay et al.
(2018) also explained how low gravity and clouds contribute to reducing the
abundance of methane.

The HR 8799 multiplanet system also contains planetesimals belts which are
replenishing the system with small dust particles in a collisional cascade.
The dust architecture has been essentially inferred from Spitzer spectroscopy
(Chen et al., 2006), and IR excess analysis, from which Su et al. (2009)
derived the presence of three components: an inner warm ($\sim$ 150 K) belt
located at $\sim$ 6 to 15 au, an outer cold ($\sim$ 45 K) belt at $\sim$ 90 to
300 au, and a halo further out. So far, only the outer cold belt has been
spatially resolved at sub-millimeter and millimeter wavelengths (Hughes et
al., 2011; Booth et al., 2016; Faramaz et al., 2021). In this picture, the
outer edge of the inner belt, and the inner edge of the outer belt, would be
sculpted by planet e and b, respectively. However, the size of the disk is not
fully in agreement with the analysis of the IR excess which raises some debate
about the presence of a fifth planet (Faramaz et al., 2021).

As of today, the HR 8799 multiplanet system has been observed extensively in
the near-IR and far-IR regimes, which are complementary and relevant to
investigate different physical processes, but was lacking any deep exploration
in the mid-IR (Petit Dit De La Roche et al., 2020), which is interesting in
the context of planet formation. As to atmosphere characterisation, mid-IR
observations at wavelengths longer than 5 $\muup$m are valuable to provide
more direct measurements of the effective temperature and radius of giant
planets with less degeneracies caused by clouds, as well as to discriminate
between various atmosphere models. Mid-IR observations encompass the signature
of ammonia which has some advantage over methane to provide temperature
estimates at least for $T<1000\,K$ (Danielski et al., 2018). Moreover, the
broad signature of silicate particles can constrain the composition of clouds
if present (Miles et al., 2022). Additionally, this spectral range matches the
peak of the emission of planets cooler than those with a peak flux in the
near-IR, providing means to push detections to lower masses. Finally, the mid-
IR has the ability to probe the intermediate-size grain population (a few to
tens of $\muup$m) which can be distributed differently than sub-micron sized
grains, seen in scattered light, and the larger millimeter sized grains.

In this paper we present the first spatially resolved detection of the four
planets in the HR 8799 system, and of the inner warm disk, in the mid-
infrared, with JWST/MIRI. Sect. 2 describes the observations and the data
reduction. We detail the photometric measurements in Sect. 3 which are used to
perform the atmospheric modeling (Sect. 4). The detection of the inner disk is
addressed in Sect. 5. Finally, the nature of a background object is discussed
in Sect. 6, and we conclude in Sect. 7.

##  2 Observations and data reduction

###  2.1 Observations

HR 8799 was observed in two runs on Nov. 7th, 2022 (filters F1065C and
F2100W), and Nov. 8th, 2022 (filters F1140C and F1550C), under GTO (Guaranteed
Time Observations) program 1194, using both MIRI’s Four Quadrant Phase Mask
(4QPM) coronagraphs, and standard imaging. The log of observations is provided
in Table 1. For each coronagraphic filter, we observed back-to-back the target
and its associated background (in two dithers), and then the reference star
together with its own background. Background images are necessary to remove
the ”glowstick” effect, as explained in Boccaletti et al. (2022), and are
obtained near the target (typically a few tens of arcseconds away). On the
contrary, no background observation is necessary for non-coronagraphic imaging
data since the star is dithered in four positions across the field of view.

The reference star, HD/,218261, was chosen 1) to be angularly close to the
target ($\sim 1.24^{\circ}$) to minimize the thermal drift induced by
wavefront errors, and 2) with comparable magnitudes to HR 8799 in both the K
band, and the MIRI’s coronagraphic filters, so that it can be shared with the
NIRCAM GTO program, with the goal to optimize overheads in telescope pointing.
Taking into account the stellar residuals and the background noise from our
diffraction model (Boccaletti et al., 2015), the exposure times were
determined to achieve signal-to-noise ratios (S/N) larger than $\sim 10$ on
the planets. The total exposure times on target are 1080 s, 1080 s, 4322 s,
and 1257 s for the F1065C, F1140C, F1550C, and F2100W filter respectively.

Figure 1: Raw coronagraphic (top, detector orientation), and reference star
subtracted (bottom, North is up, East is left) images in the four filters
(left to right: F1065C, F1140C, F1550C, F2100W). The four planets are labelled
b, c, d, and e in panel 1), as well as the background object in panels 3) and
7). The signature of the inner disk is circled with a dotted line in panels 3)
and 4). The 50 au scale stands for the projected physical distance. The Field
of View is $12\times 12"$. Intensity scale is adapted in each panel for
visualisation purpose.

###  2.2 Data reduction

We retrieved the processed data from the Mikulski Archive for Space Telescopes
(MAST111https://mast.stsci.edu/portal/Mashup/Clients/Mast/Portal.html), and
also reprocessed the raw data with our own routines for comparison. We outline
here the main steps of the process. Stage 1 of the JWST
pipeline222https://jwst-pipeline.readthedocs.io/en/latest/jwst-
pipeline.readthedocs.io is taking as input the uncalibrated files (uncal) and
applies the detector-level corrections to raw JWST ramps to produce the 2D
slope product. Stage 1 corrects for the dark current, and bad pixels such as
those that would be saturated or impacted by cosmic rays that would produce
jumps in the ramp. Carter et al. (2023) noted that the default jump threshold
value is too low and leads to erroneously flagged bad pixels. Therefore, we
ran the steps with different values, but we find that it does not have any
impact on the calibration. In comparison to ERS data (Hinkley et al., 2022),
the ramps in this program have many more groups per integration. Therefore,
the jumps have less impact on the calibrated slopes. The first and last group
of each integration are removed, and the ramps are fitted to generate the rate
files. Stage 2 of the JWST pipeline is meant to photometrically calibrate the
data. However, because the 4QPM produces a shadow pattern along its transition
due to diffraction effects, we skip the flat$\\_$field steps to avoid
increasing the noise as well as the ”glow-stick” effect close to the mask
center (precisely where we expect to detect the planets). The photometric
calibration in the pipeline is also skipped, as long as we apply our own
photometric calibration, as detailed in the Sect. 3.3. In practice, stage 2 is
only taking care of the background subtraction.

In the end we made use of the rate and cal files which collapse all
integrations (Nint) in a single frame, yielding a number of frames that is the
number of dither positions (Ndither). All data reductions agree visually in
terms of planet detection as well as photometrically.

A mean combination of the background dithers is subtracted from each target
observation, and we further reject the remaining bad pixels with sigma
clipping. Each coronagraphic image is registered at the 4QPM centers which was
determined during commissioning (using cross-correlation with a large database
of simulated data).

Raw coronagraphic images are essentially dominated by the diffraction. This
specific signature shown in Fig. 1 (top) is a result of the particular JWST
hexagonal pupil, that even an optimized Lyot stop cannot completely suppress
(Boccaletti et al., 2005). The classical workaround is to observe a reference
star in similar conditions referred to as Reference Differential Imaging
(RDI). But, it has been shown that the contrast achieved with MIRI’s 4QPM is
mostly limited by the relative pointing accuracy between the target star and
the reference star (Cavarroc et al., 2008), which is estimated to about $5-10$
mas (Rigby et al., 2022). To mitigate this issue, Soummer et al. (2014) and
Lajoie et al. (2016) proposed to implement the Small Grid Dither (SGD)
strategy. While we obtain a single pointing of the target (hence with a
precision of about $5-10$ mas), several (5 or 9) pointings of the reference
star are performed to provide diversity, and to match, ideally, the
diffraction pattern of the reference star with that of the star. Elaborate
algorithms like PCA (Soummer et al., 2012) and LOCI (Lafrenière et al., 2007)
can provide an optimal subtraction of this diffraction pattern.

For the coronagraphic observations of HR 8799, we used the 9-point SGD,
defined as a regular square grid with 10 mas steps. We compared several types
of algorithms to suppress the star’s diffraction pattern, but since the
diversity is low, the best results are obtained with a linear combination of
the nine reference frames, of which the coefficients are calculated with a
downhill simplex method (referred to as amoeba) applied to a restricted field
of view of 0.4′′ to 8′′ in radius (Fig. 1, bottom). The frames combination
which minimizes the residuals in this field is then subtracted out from the
target image.

The so-called over-subtraction (Pueyo, 2016) can be particularly strong in the
case of MIRI observations given that the angular separations of the planets
are not significantly larger with respect to the angular resolution (about
0.3′′ at 10 $\muup$m). To overcome this issue, we further masked the planets
in the RDI process. In practice, we used a simple patch of 1 $\lambda/D$ in
radius to remove the contribution of the planet’s signal in the minimization
of the residuals. We also tested a method which subtracts the planet’s
diffraction pattern (taking into account the coronagraph’s transmission, see
Sect. 3.2), but this requires a first iteration with masks to evaluate the
planets’ fluxes. Both methods were found to be qualitatively similar although
the latter provides larger error bars. Figure 1 displays the results with the
masking technique, while other methods including variations of amoeba,
together with a median combination of the reference frames, and PCA with
various sets of reference stars, are presented in Figure 7 in the appendix.

For the observations in the F2100W filter in standard imaging mode, we used a
standard 4-point dithering to minimize the impact of the background and
cosmetics of the detector. The reference star is observed the same way. Since
the inner part of the image (essentially the PSF peak) is dominated by the
inner disk flux (see sec. 5), we performed a direct weighted subtraction to
minimize the diffraction in the $2-5^{\prime\prime}$ annular region (to
exclude the region where the inner disk dominates).

###  2.3 Overall description of the images

The four known planets, HR 8799 bcde, are well detected at the two shortest
wavelengths (F1065C in Fig. 1-1, and F1140C in 1-2), while only planet b is
detected at F1550C (Fig. 1-3). However, interestingly, planet c is also barely
detected at F1550C with the PCA reduction technique using the commissioning
stars as references (Fig. 7). The longest wavelength filter (F2100W, Fig. 1-4)
yields no planet detection. Another point source to the northwest of the star,
a suspected background galaxy (Faramaz et al., 2021) labeled ”bgd” in Fig. 1-3
and Fig. 1-7, appears to be very bright at F1550C, and is also visible, but
much fainter, in the other three filters (see Sect. 6). Finally, at the
longest wavelengths (F1550C in Fig. 1-3, and F2100W in 1-4), residual emission
at the center of the images corresponds, presumably, to the inner warm debris
disk. The same feature is also identified at shorter wavelengths with much
lower S/N ratios. The doughnut-like shape of the inner disk is due to the
coronagraphic effect, and the dashed circle in Fig. 1-3 is not representing
the actual size of the disk but the extent of its image (see Sect. 5 for
details). Overall, the MIRI images of the HR 8799 system yield a very
different vision than in the near IR, with the clear detection of the four
planets, together with a localized but extended central emission.

##  3 Photometry of the planets

Extracting the photometry in the MIRI coronagraphic images requires
normalization of the planet’s flux to the stellar Point Spread Function (PSF)
to derive the contrast values, as well as to take into account the planet’s
attenuation due to the coronagraph and to estimate photometric error bars.

###  3.1 PSF normalization

To avoid saturation, the star cannot be observed out of the coronagraphic
mask. There is no direct way to measure the star-to-planet contrast in the
very same filter with this program, and this is certainly a limitation for
accurate photometry. Instead, we considered two solutions for normalizing the
stellar flux, both relying on commissioning (COM) data for which we manage to
observe a PSF (out-of-mask image) and a coronagraphic image (on-mask image).
The first solution makes use of the target acquisition data (TA) in addition
to the COM data. TA comes with any coronagraphic sequence to estimate the
centroid of the target in a coronagraph subarray in order to move the star
right at the location of the coronagraph. The second solution involves the COM
data alone which was designed to accurately measure contrasts for all filters,
carefully choosing a star that is not too bright to obtain unsaturated PSFs
(Boccaletti et al., 2022).

For TA normalization (Eq. 1), the method consists in measuring the flux ratio
of the TA images between the target, HR 8799 ($I_{TA\\_targ}$), and the
commissioning star ($I_{TA\\_com}$), and to apply this factor to the
commissioning PSF ($I_{PSF\\_com}$) to generate a pseudo HR 8799 PSF
($I_{PSF\\_targ}^{TA}$), as follows :

$I_{PSF\\_targ}^{TA}=I_{PSF\\_com}\times\
\dfrac{\sum\limits_{k}A_{k}.I_{TA\\_targ}}{\sum\limits_{k}A_{k}.I_{TA\\_com}}$
(1)

$A_{k}$ is a circular aperture containing $k$ pixels which is set here to a
1′′ radius. The two TA images (at two dithered positions) follow the same data
reduction procedure as other data. We keep the one that is further out from
the 4QPM center to avoid attenuation effects after subtracting them to get rid
of the background. In the case of HR 8799 observations, TA data were obtained
with the neutral density filter (FND). The FND is intentionally broad
($8-18\,\muup$m) to mitigate the chromatic effects due to the 4QPM, avoiding
bias to the centroid estimation (Cavarroc et al., 2008). But, normalization
issues can arise if the science target has a different spectral slope in the
FND spectral range than the commissioning star. This is particularly
problematic when IR emission, like an unresolved (or slightly resolved) disk,
adds to the total flux of the central source. At the moment, only the FND has
been validated against the TA procedure, but for future programs MIRI will
allow TA to be carried out with F560W, F1000W, and F1500W filters too.

The second solution is based on the calibration of the coronagraph attenuation
on the central star using commissionning data. Here, the normalization factor
(Eq. 2) corresponds to the flux ratio of the coronagraphic images
($I_{CORO\\_targ}$, and $I_{CORO\\_com}$). Because, these images are much more
extended than in the former case, $A_{k}$ is representing an aperture of 5′′
in radius to encompass the full coronagraphic diffraction pattern. As an
advantage with respect to the former solution, this method uses the same
coronagraphic filter. Still, the target and commissionning star are different,
and again, the presence of an unresolved inner disk can bias the
normalization. This method is also prone to biasing because of background
objects since the aperture, $A_{k}$, is much larger to encompass the full
coronagraph diffraction pattern. The pseudo-PSF reads:

$I_{PSF\\_targ}^{CORO}=I_{PSF\\_com}\times\
\dfrac{\sum\limits_{k}A_{k}.I_{CORO\\_targ}}{\sum\limits_{k}A_{k}.I_{CORO\\_com}}$
(2)

Since we have two dithers for $I_{PSF\\_com}$ (rejecting two other dithers for
image quality reasons), and nine dithers of $I_{CORO\\_com}$ (because
observations used 9-point SDG) we can evaluate two, respectively eighteen,
values of the star’s count, for $I_{PSF\\_targ}^{TA}$, respectively
$I_{PSF\\_targ}^{CORO}$, contributing to the uncertainties in the planets’
photometry. Therefore, the estimate of the dispersion for each method ($TA$
and $CORO$) is not based on the same amount of independent realizations. But
overall, we estimate that the normalization factors obtained with the two
methods are consistent within 5%, 16% and 37% for filters F1065C, F1440C,
F1550C, respectively. The larger discrepancy at F1550C can be explained by the
background contamination, which is stronger at longer wavelengths, and can
bias the $CORO$ method. The background galaxy, easily identifiable in the raw
coronagraphic frame of HR 8799 (Fig. 1-7), is one potential source of bias.

Table 2:  Transmission of a point source for the three MIRI 4QPM coronagraphs at each planet’s location calculated either in an aperture region of 1.5 $\lambda/D$ in radius, or integrated in the whole image. planet | F1065C | F1140C | F1550C
---|---|---|---
b | 0.79 / 0.82 | 0.75 / 0.79 | 0.76 / 0.82
c | 0.77 / 0.80 | 0.74 / 0.78 | 0.61 / 0.65
d | 0.61 / 0.67 | 0.59 / 0.65 | 0.62 / 0.66
e | 0.56 / 0.58 | 0.52 / 0.56 | 0.40 / 0.45

###  3.2 Coronagraphic transmission

Each planet in the image has a transmission which depends on its position with
respect to the 4QPM center and phase transitions, and which also scales with
wavelength. We estimate this transmission using the diffraction model from
Boccaletti et al. (2015) calibrated against commissioning data (Boccaletti et
al., 2022), with updates of the telescope wavefront error measurements. This
diffraction model is based on a similar concept as webbpsf (Perrin et al.,
2014), so that the planet’s transmission can be also evaluated with the
latter.

We used whereistheplanet (Wang et al., 2021) to estimate the planets’
locations at the epochs of observations with respect to the 4QPM mask
orientation (which is inclined by 4.835∘ with respect to the detector),
accounting for the field orientation (the V3PA parameter in the JWST
coordinate system). The estimated values of the coronagraphs’ transmission are
provided in Table 2 and used to correct for the planets’ photometry. To give
an idea of the impact of the coronagraph transmission, we display in Fig. 8
the image of four point sources of equal intensities located at the positions
of the HR8799 planets.

###  3.3 Planets’ contrasts and fluxes

The planets’ photometry is assessed in two different ways. First, we integrate
the planets’ count rates (in DN/s) in RDI images, in an aperture of 1.5
$\lambda/D$ in radius, the size of which is found to be optimal to integrate
enough planets’ signal while keeping the contamination of the other planets at
a low level. Second, we use negative fake planets. As explained in section
3.2, we modeled the planets’ diffraction pattern for each filter and each
planet positions with respect to the 4QPM transitions. The minimization of the
residuals in the 1.5 $\lambda/D$ area accounts for three parameters: the flux
and the positions of the planet’s model (which is allowed to vary by a few
pixels compared to the theoretical positions). The final contrast is the count
rates measured in the PSF (Sec. 3.1) relative to the count rates measured in
the planet, in the same patch, and corrected by the local attenuation due to
the coronagraph. With respect to the compensation of the inherent over-
subtraction of RDI mentioned in Section 2.2 we opt for the method using masks
which provides lower error bars. Irrespective of the PSF normalization, the
two methods to extract planet’s photometry agree within 3% to 46% depending on
filters and planets, in some cases resulting in two families of contrast
values (for instance for planet b in F1550C, see Table 6). Table 3 provides
the average contrast values and standard deviations together with the
dispersion between the two extraction methods, while the individual
measurements for each PSF normalization and flux extraction methods are given
in the Appendix (Tab. 6).

Table 3: Star-to-planet contrasts measured for planets b, c, d, and e, in the three coronagraphic filters. The values in brackets correspond to the dispersion due to the flux extraction method (aperture and negative fake planets). See Sect. 3.3 for details. planet | F1065C | F1140C | F1550C
---|---|---|---
b | $2609\pm 104$ | $2375\pm 154$ | $2131\pm 463$
| [3%] | [7%] | [39%]
c | $1301\pm 172$ | $1316\pm 213$ | $>489$
| [25%] | [30%] |
d | $1295\pm 283$ | $1292\pm 221$ | $>385$
| [38%] | [31%] |
e | $885\pm 208$ | $878\pm 186$ | $>242$
| [46%] | [40%] |

Converting contrasts to fluxes requires a stellar flux model. We retrieved
synthetic photometry from VOSA (Virtual Observatory SED analyzer, Bayo et al.,
2008) considering the BT NextGen stellar model (Allard et al., 2011; Asplund
et al., 2009) with an effective temperature Teff=7600 K, a surface gravity
$\mathrm{log}(g)=4.5$ cm.s-2 , and solar metallicity. We adopt a stellar
radius of 1.34 R⊙, and a distance $d=40.88$ pc (Gaia Collaboration et al.,
2021). Further, we obtained actual photometry of HR 8799 from VizieR in the
2MASS and WISE filters: J, H, Ks, W1, W2 and W3 (Table 4), excluding the
shorter wavelengths which may not be representative to interpolate the mid IR
fluxes, as well as wavelengths longer than 20 $\muup$m to avoid being biased
by the emission from the debris disk. We perform a $\chi^{2}$ minimization to
determine the global intensity offset between the model and the real star’s
photometric data, and find a correction of a factor of 1.22. Figure 2 shows
the flux density of the four planets as measured in the MIRI filters, with the
over-subtraction compensated (and without to see the corresponding impact),
together with the near-IR photometry from the literature, which is compiled in
Bonnefoy et al. (2016). The flux densities are reported in Table 5 for each
planet and each filter.

Table 4: Photometry of the star retrieved from VizieR. wavelength | flux | flux | flux error | filter
---|---|---|---|---
($\muup$m) | (W.m-2) | (Jy) | (Jy) |
1.24 | 2.69e-11 | 11.1 | 0.3 | 2MASS:J
1.65 | 1.47e-11 | 8.11 | 0.13 | 2MASS:H
2.16 | 7.50e-12 | 5.41 | 0.09 | 2MASS:Ks
3.35 | 2.29e-12 | 2.56 | 0.52 | WISE:W1
4.60 | 1.07e-12 | 1.64 | 0.12 | WISE:W2
11.6 | 6.17e-14 | 0.238 | 0.004 | WISE:W3

##  4 Atmospheric modelling

We explored the properties of the planets’ atmospheres with two complementary
models : Exo-REM and ATMO, both developed for long period young giant planets,
neglecting the stellar irradiation. They both provide grids of synthetic
spectra with various atmospheric parameters.

Table 5: Flux density ($\times 10^{-18}$) in W.m-2.$\muup$m-1 of the four planets in the MIRI filters (min/max values). planet | F1065C | F1140C | F1550C
---|---|---|---
b | 2.90 / 3.14 | 2.37 / 2.70 | 0.66 / 1.03
c | 5.34 / 6.97 | 3.92 / 5.43 |  $<$ 2.90
d | 4.98 / 7.78 | 3.96 / 5.60 |  $<$ 3.69
e | 7.20 / 11.63 | 5.63 / 8.65 |  $<$ 5.09

Exo-REM (Baudino et al., 2015; Charnay et al., 2018) is a self-consistent 1D
radiative-convective equilibrium model which assumes the net flux is
conserved. It incorporates the opacities of collision-induced absorptions
(H2–H2, H2–He, H2O-H2O and H2O–N2), and rovibrational bands from various
molecules (H2O, CH4, CO, CO2, NH3, PH3, TiO, VO, H2S, HCN, and FeH), as well
as resonant lines from sodium and potassium. Exo-REM uses micro-physics to
model clouds of silicate, iron, sulfide, alkali salt, and water clouds, and it
handles disequilibrium chemistry as well.

ATMO (Tremblin et al., 2016, 2017) shares a similar approach for modeling the
atmospheres, but it assumes that the thermo-chemical instability of the CO/CH4
and N2/NH3 transitions can reduce the temperature gradient in exoplanet
atmospheres, acting in a similar way as clouds for reddening the spectra, but
solving the issue of the J band brightening at the L/T transition (Burrows et
al., 2006). This process is controlled by an effective adiabatic index,
$\gamma$, which is lower than for the thermodynamic equilibrium.

Both models are computed for a range of effective temperature, Teff, surface
gravity, $\mathrm{log}(g)$, metallicity, and C/O ratio. The details of the
grid used in ours analysis are described in (Petrus et al., 2023). Since MIRI
coronagraphic data only provide photometry, we intentionally compared them to
near-IR photometric data from the literature and we reduced the parameter
space to two: Teff and $\mathrm{log}(g$), together with $\gamma$ in the case
of ATMO, leaving the metallicity and C/O ratio for further investigations
(here assuming solar values).

In Figure 3 we compare the result of the $\chi^{2}$ minimization for the four
planets using the two models and near-IR data alone, or using both the near-IR
and mid-IR data points. As the mid-IR flux is directly proportional to the
product of the effective temperature and the emitting surface of the planet,
using only mid-IR photometry would obviously result in a strong degeneracy
between temperature and radius. The radius determines the intensity scaling
factor between the data and the model and corresponds to the value which nulls
the derivative of the $\chi^{2}$. It reads as follows :

$R^{2}=\frac{\sum_{\lambda}S(\lambda)\times
M(\lambda)/\sigma(\lambda)^{2}}{\sum_{\lambda}M(\lambda)^{2}/\sigma(\lambda)^{2}}$
(3)

with $S(\lambda)$ the data (planet’s emergent spectrum), $M(\lambda)$ the
model, and $\sigma(\lambda)$ the errors, evaluated in the spectral bandpasses
of the photometric filters.

Overall, the two models, ATMO and Exo-REM, qualitatively reproduce the
spectral slope well, but yield large reduced $\chi^{2}$ values indicative of a
poor fit in most cases. In general, they systematically predict lower fluxes
in the $10-15\,\muup$m range than the measured photometric values. This is
particularly clear for planet e. Including the mid-IR photometry in the fit
together with the near-IR also provide larger reduced $\chi^{2}$ than near-IR
alone, but this is not surprising for such a large spectral range covering
more than an order of magnitude.

Planet b is the only case for which adding the mid-IR photometry makes a net
difference in the fit compared to using the near-IR photometry alone. However,
in this particular case, we suspect that the fit of the near-IR data could be
impacted by the photometry in the M band filter (4.67 $\muup$m) possibly
explaining the low flux beyond 5 $\muup$m. The rather low flux in this
spectral band measured from the ground (Keck data, Galicher et al., 2011)
calls for further investigations, for instance with JWST/NIRCAM data. The
combination of MIRI and near-IR ground-based photometry provides a larger
estimate of the planet b radius compared to the literature (based on near-IR
data): 0.86 and 1.07 $R_{\mathrm{J}}$, for respectively ATMO and Exo-REM, as
opposed to 0.54 and 0.79 $R_{\mathrm{J}}$. It appears more compliant with the
expectation of evolutionary models (Phillips et al., 2020), although not yet
at the predicted value for such an age ($\sim 1.3\,R_{\mathrm{J}}$). In
addition, the temperature of planet b would be cooler, 1100 K and 950 K for
ATMO and Exo-REM, respectively, instead of 1300 K and 1050 K.

Figure 2: Flux density in W.m${}^{-2}.\muup$m-1 of the four planets and the
background object in the MIRI filters, with (filled circles) and without
(unfilled circles) correcting for the over-subtraction, and in the near IR
(squares). Triangles denote flux upper limit.

Figure 3: Flux density in W.m${}^{-2}.\muup$m-1 of the best models using ATMO
(plain lines) and Exo-REM (dashed lines) fitting the near-IR (colored squares)
and mid-IR (black circles) photometric data points for the four planets. The
light color lines correspond to the fit of the near-IR data alone. Figure 4:
Flux density (W.m${}^{-2}.\muup$m-1) of HR 8799 b (red circles) compared to
that of the blackbody for several temperatures (triangles), and if normalized
to F1140C (black squares).

The same tendency (lower temperatures, larger radii) is also observed, but
marginally, for the other planets when incorporating mid-IR data in the fit.
We hypothesize that a possible reason for this difference in the mid-IR is
because the estimation of error bars are not consistent between all the
photometric data points in the literature (likely underestimated). There can
be calibration issues among instruments, as well, or the models are not fully
representative. In the case of HR 8799 the more numerous near-IR measurements
are naturally driving the models to converge to lower radii and higher
temperatures.

For planets b and d, we note that ATMO produces very small and possibly un-
physical surface gravity which can be as low as $\mathrm{log}(g)=2.5$, while
Exo-REM values are more in the higher range, also with possible un-physical
values as high as $\mathrm{log}(g)=5.0$ for planet c. However, trying to
derive the surface gravity with a few photometric data points is likely
difficult as it mostly affects the depth of spectral features which we are not
sensitive to with MIRI coronagraphy. Apart from planet b, the temperatures
derived for the other planets are consistent with the values derived by
Bonnefoy et al. (2016), in the range of 1150-1300 K.

The coronagraphic filters were originally designed to investigate the ammonia
feature at $\sim 10.6\,\muup$m (Boccaletti et al., 2015), although the
temperatures of the HR 8799 planets are higher than the temperature at which
we expect ammonia to clearly stand out in an exoplanet spectrum. Nevertheless,
following Danielski et al. (2018) we compare the measured photometry to
blackbody spectra in order to distinguish a potential variation of the
spectral slope which could be flatter at the shortest coronagraphic filter if
ammonia were detectable. In Fig. 4, we plot HR 8799 b photometry against
several blackbody temperatures from 900 K to 1100 K assuming a radius of 1
$R_{\mathrm{J}}$. Given the error bars, the F1065C and F1140C are the two
relevant filters to derive the equivalent blackbody temperature, which would
range from about 950 K to 1000 K. We may suspect a marginal difference at
F1065C between the data and the blackbody, but, once the blackbody is
normalized to the F1140C data point, the expected blackbody flux at F1065C is
fully compatible with the data within error bars. The same applies to the
other three planets, which have even larger photometric error bars. As a
result, the current data cannot conclude on the detectability of the ammonia
feature in the HR 8799 planets.

Figure 5: Coronagraphic images (top) simulated in the F1550C filter for four
different disk sizes (2, 5, 15 and 30 au), together with the residuals after
subtracting the model to the real F1550C data (reference star subtracted).
Intensity scale is adapted in each panel for visualisation purpose.

##  5 The inner disk component

At F1550C, the central part of the image is dominated by a roughly azimuthally
symmetrical pattern in the form of a broad ring surrounded by another thinner
ring, which prevents the detection of the innermost planets c, d, and e. The
same pattern is also visible, but fainter, in the two other coronagraphic
filters, but with a reduced size.

In fact, the warm inner disk component identified with Spitzer is located at
physical separations of 6 to 15 au in radius (Su et al., 2009) that is
$0.15^{\prime\prime}$ to $0.37^{\prime\prime}$, to be compared with the
angular resolution of about $0.5^{\prime\prime}$ at 15.5 $\muup$m. In
principle the inner disk should be unresolved by MIRI. However, such an
angular size combined with the extreme sensitivity of the 4QPM coronagraph to
pointing, due to its small inner working angle (which is identical to the
angular resolution), results in an significant leakage of the inner disk
itself (the leakage from the star being much lower in intensity). As a
consequence, the inner disk image takes the form of a diffraction pattern,
although with a dark spot in the central diffraction peak owing to the strong
attenuation of the innermost on-axis beams. Therefore, the rings in the image,
which are more extended than the actual inner disk size, are reminiscent of a
diffraction effect (scaling with wavelengths as proven by the signature at
shorter wavelength), the broad ring and the dark spot being the equivalent of
the central PSF diffraction peak, and the thinner ring its first diffraction
ring. This is the same effect that hampered the detection of the inner region
of the Fomalhaut disk at F1550C, as presented in Gaspar et al. (2023).

Estimating the flux of the inner disk requires modelling the effect of the
coronagraph, as a function of the disk size. As already mentioned, this disk
component is angularly small, and so it is difficult to constrain its
morphology with the present data. Therefore, for the sake of simplicity, we
assume a uniform face-on disk model (neglecting the system’s orientation),
defined by a single parameter, its radius, ranging from 2 to 30 au. We use the
diffraction model, as for modeling a planet’s PSF, to generate synthetic disk
images. The 2 au case corresponds to a perfectly unresolved case, hence the
image is similar to a raw coronagraphic image of a point source (Fig. 5), but
at 5 au the disk yields a strong leakage, featuring the same kind of pattern
that is visible in the real data. Increasing the disk radius does not change
the size of its image too much as long as it is fully dominated by diffraction
effects. For larger radii ($\gtrsim$ 30 au), we recover a more classical image
of an extended source in which the 4QPM transitions generate a shadow pattern
on top of the disk image. The top panels in Fig. 5 are to be compared with
panel 3) in Fig. 1.

Minimizing the residuals between the real image and the model in a 1.65′′
aperture radius (which encompasses the first diffraction ring of the disk), we
find an optimal size of 15 au, but 10 au and 20 au also provide a good match.
In this case, the 4QPM attenuates the disk by a factor of 7 (5 to 13 for the
extreme values), which corresponds to a total flux density of $4.2\pm 0.3$
mJy, if we assume the star flux density at 15.5 $\muup$m to be 154.2 mJy
(interpolating between WISE:W3 and AKARI:L18W). A more realistic, ring-like
disk extending from 6 to 15 au, as in Su et al. (2009), yields similar results
with a total flux density of 3.3 mJy, and residuals that are almost identical
to the uniform 15 au case.

The inner warm disk is also detected at F2100W. It is seen as a resolved
central emission without any particular structure after subtracting the
reference star (Fig. 1-4). There is no coronagraphic attenuation in that case
to account for, so the disk flux density can be directly integrated in an
aperture of 1.65′′. Assuming the star flux density at 21 $\muup$m to be 101.8
mJy (interpolating between AKARI:L18W and WISE:W4), we measure a disk flux
density of $9.4$ mJy.

To first order, these values are qualitatively in agreement with the Spitzer
spectrum presented in Su et al. (2009), but a factor of about 2 lower. Su et
al. (2009) reported photosphere-subtracted flux densities of $\sim$8 mJy and
$\sim$19 mJy, at respectively 15.5 $\muup$m and 21 $\muup$m. The exact reason
of this discrepancy is still to be investigated but could be related either to
the reliability of the photosphere subtraction when modeling the spectral
energy distribution of the star in the presence of background objects such as
redshifted galaxies (which can be confused with the star in the Spitzer beam
size), to the limit of our disk model which does not capture the exact belt
geometry, or to the entangling of the disk and planets’ images.

Figure 6: Typical spectrum of a Sb galaxy at a redshift z = 1, taken from the
SWIRE template library (Polletta et al. 2007, ApJ, 663, 81) ; the location of
the four MIRI filters are indicated. A clear excess at $15.5\muup$m can be
observed that could explain the observed fluxes.

##  6 A background object

Searching for additional planets in the HR 8799 system either closer in or
further out, is motivated by the structure of the debris disk made of two
belts. In particular, the inner edge of the outer belt which, depending on
studies, is located at a distance of 110 au (Wilner et al., 2018) or 145 au
(Booth et al., 2016), could be caused by a fifth sub-Jupiter to Saturn mass
planet (Read et al., 2018).

In this respect, the F1550C image reveals a bright point source located at
$\Delta\alpha=-1.653\pm 0.012^{\prime\prime}$ and $\Delta\delta=2.503\pm
0.020^{\prime\prime}$ from the star (error bars being conservative since we
only have two measurements), which also has fainter counterparts in the other
filters. Its photometry in the four MIRI bands is shown in Fig. 2. With a flux
density increasing from 10 $\muup$m to 15 $\muup$m, it could have been the
signature of an object with a low temperature, possibly a planet. However, for
the flux to match the expected emission of a planet cooler than the other
planets in the system (400 K for instance) it would require a radius much
larger than that of Jupiter which is odd given the mass and temperature of the
other planets.

An alternative to the planet hypothesis is provided in Faramaz et al. (2021)
who identified a point-like source in ALMA band 7 data from 2018, near the
inner edge of the outer belt ($\Delta\alpha=-1.28\pm 0.05^{\prime\prime}$ and
$\Delta\delta=2.34\pm 0.05^{\prime\prime}$) with flux densities of
$316\,\muup$Jy and $58\,\muup$Jy at, respectively, 0.87 mm (band 7) and 1.3 mm
(band 6). They also showed that the spectral index is too steep to match the
expected behaviour of a dust clump. As for the astrometric analysis with
respect to the ALMA 2015 data (band 6) in which the same source is marginally
detected, the beam size of ALMA does not allow a firm conclusion regarding the
motion of the source. Interestingly, the source in the ALMA deconvolved image
also seems marginally resolved. Faramaz et al. (2021) concluded that the
bright source is likely a background galaxy.

With a new data point, 4.44 years apart from the former detection, we can now
safely conclude that this is a background object, as the motion between the
two epochs is $\Delta\alpha=-0.373\pm 0.051^{\prime\prime}$ and
$\Delta\delta=0.163\pm 0.054^{\prime\prime}$, consistent within $\sim 2\sigma$
with what is expected from the star’s proper motion:
$\Delta\alpha=-0.48^{\prime\prime}$ and $\Delta\delta=0.22^{\prime\prime}$. As
a consequence, given that the radial separation increases from 2.67′′ to 3′′,
it is not consistent with an orbital motion for a nearly face-on system.

Furthermore, the characteristics of the spectral energy distribution displayed
in Fig. 2 which is peaking at $15\,\muup$m is in line with the $7.7\,\muup$m
PAH emission of a Sb spiral galaxy redshifted at $z\approx 1$. As a
qualitative illustration, Fig. 6 displays a typical spectrum of a Sb galaxy
taken from the SWIRE template library (Polletta et al., 2007). In addition, if
one compares the relative fluxes of the object in ALMA band 6 and 7 with the
predictions by the redshifted Sb galaxy spectrum, normalized at
$10.65\,\muup$m, they are fully consistent (ratio 1.0 and 1.12 respectively).
In future observations with MIRI’s coronagraph we should expect contamination
from background galaxies to complicate the analysis.

##  7 Conclusions

We summarize below the results of the first Guaranteed Time Observations with
the MIRI coronagraphic mode.

  * $\bullet$

We obtained mid-IR observations from 10 to 20 $\muup$m of the multiplanet
system HR 8799, in both coronagraphy and standard imaging with JWST/MIRI. Once
the diffraction and background emission are subtracted out we can easily
detect the four planets, including the closest one in filters F1065C and
F1140C. Indeed, planet e is lying at the Inner Working Angle and is attenuated
by a factor of $\sim 2$. This demonstrates the ability of the MIRI coronagraph
to probe the very inner regions of exoplanetary systems.

  * $\bullet$

We developed a procedure to extract and calibrate the photometry of the
planets making use of target acquisition data and commissioning data. We
identified the oversubtraction as a potential source of error and mitigate
this effect by masking the planets in the optimization of the reference star
subtraction. Comparing with models of exoplanet atmospheres we show that MIRI
photometry favors larger radii and cooler temperatures, than if using near-IR
photometry alone. This is particularly true for planet b (0.86 or 1.07
$R_{\mathrm{J}}$, and 950 or 1100 K), but remains marginal for the other
planets. Overall, MIRI photometric values are systematically brighter than the
models which could indicate a natural difficulty to compile photometries from
various instruments, or a missing ingredient in the models. Taking advantage
of NIRCAM to get overlapping data at near-IR, as for HIP 65426 b (Carter et
al., 2023), can definitely help to cross calibrate near and mid-IR data. A
deeper exploration of the atmospheric properties based on advanced modeling,
taking into account these new MIRI’s photometric data, would definitely be
relevant.

  * $\bullet$

In terms of performance, we tested the ability to use a library of reference
stars. Even if we managed to obtain reasonable contrasts, the best quality is
by far achieved with a dedicated observation of a reference star obtained
close in time. This situation will certainly change along the lifetime of
JWST, as more targets are observed with MIRI’s coronagraphs, providing
sufficient observing time is allocated to this mode.

  * $\bullet$

In line with the capacity of the MIRI’s coronagraph at short angular
separations, we also directly detect and spatially resolve for the first time
the inner warm debris disk in all four filters. By taking into account the
diffractive effect of the 4QPM coronagraph we were able to constrain the inner
disk radius to about $15\pm 5$ au using the F1550C image, which provides a
direct and independent estimate in agreement with the modeling of the IR
excess. We measured a flux density at F1550C and F2100W of a few mJy, a factor
of 2 lower than Spitzer which remains to be understood. The outer cold debris
disk is undetected, consistent with the sensitivity.

  * $\bullet$

In the field of view, we identified a point source which we confirmed to be a
background redshifted galaxy at $z\approx 1$ considering the astrometry with
respect to ALMA observations, and its spectral energy distribution.

  * $\bullet$

Finally, the extreme sensitivity of the 4QPM coronagraph at small angular
separations combined with the presence of inner circumstellar components can
make the detection and the interpretation of young system observations very
challenging, not mentioning the confusion related to background galaxies. The
MIRI’s coronagraphic mode is still in its infancy, and there is room for
improving several aspects: modeling and calibrating the diffraction pattern as
a function of telescope characteristics, developing optimal data reduction
techniques, and interpreting the entangled signals of point-like sources and
extended circumstellar components.

##  Appendix A Other data reductions

Figure 7:  Reference star subtracted images in the three coronagraphic filters
(left to right: F1065C, F1140C, F1550C) for a variety of algorithms: amoeba
(same as in Fig. 1 without masking the planets), amoeba sub. (same as in Fig.
1 with subtracting the planets’ diffraction models, median (median combination
of the 9 small grid dithers), PCA (principal component analysis of the 9 small
grid dithers), PCA lib (principal component analysis using two other reference
stars observed at commissioning, hence totalling 18 small grid dithers). The
Field of View is $12\times 12"$. Intensity scale is adapted in each panel for
visualisation purpose.

##  Appendix B Planets’ footprints

Figure 8: Images in each filters of four point sources with equal brightness,
but located at the positions of the HR8799 planets to illustrate the impact of
the coronagraph transmission as well as the overlapping of the PSFs arising
from the angular resolution at such angular separations.

##  Appendix C Measured contrasts

planet | norm. | F1065C | F1140C | F1550C
---|---|---|---|---
| & extrac. method |  |  |
b | CORO / aper | $2608\pm 101$ | $2491\pm 71$ | $1782\pm 50$
CORO / FP neg | $2618\pm 102$ | $2317\pm 66$ | $2650\pm 75$
TA / aper | $2471\pm 20$ | $2113\pm 19$ | $1221\pm 16$
TA / FP neg | $2480\pm 20$ | $1965\pm 17$ | $1817\pm 24$
c | CORO / aper | $1142\pm 44$ | $1133\pm 32$ | $>415$
CORO / FP neg | $1466\pm 57$ | $1532\pm 44$ | $>589$
TA / aper | $1082\pm 9$ | $961\pm 8$ | $>284$
TA / FP neg | $1389\pm 11$ | $1299\pm 11$ | $>404$
d | CORO / aper | $1025\pm 40$ | $1101\pm 31$ | $>328$
CORO / FP neg | $1503\pm 58$ | $1511\pm 43$ | $>467$
TA / aper | $971\pm 8$ | $934\pm 8$ | $>225$
TA / FP neg | $1424\pm 12$ | $1282\pm 11$ | $>320$
e | CORO / aper | $685\pm 27$ | $712\pm 2$ | $>164$
CORO / FP neg | $1094\pm 42$ | $1072\pm 31$ | $>336$
TA / aper | $649\pm 5$ | $604\pm 5$ | $>112$
TA / FP neg | $1037\pm 8$ | $909\pm 8$ | $>230$
Table 6: Same as Table 3 for each normalization/extraction method.

###### Acknowledgements.

This work is based on observations made with the NASA/ESA/CSA James Webb Space
Telescope. The data were obtained from the Mikulski Archive for Space
Telescopes at the Space Telescope Science Institute, which is operated by the
Association of Universities for Research in Astronomy, Inc., under NASA
contract NAS 5-03127 for JWST. These observations are associated with program
#1194. Part of this work was carried out at the Jet Propulsion Laboratory,
California Institute of Technology, under contrast with NASA (80NM0018D0004).
French contributors acknowledge the support and funding of CNES. This
publication makes use of VOSA, developed under the Spanish Virtual Observatory
(https://svo.cab.inta-csic.es) project funded by
MCIN/AEI/10.13039/501100011033/ through grant PID2020-112949GB-I00. VOSA has
been partially updated by using funding from the European Union’s Horizon 2020
Research and Innovation Programme, under Grant Agreement nº 776403
(EXOPLANETS-A) This work has made use of data from the European Space Agency
(ESA) mission Gaia (https://www.cosmos.esa.int/gaia), processed by the Gaia
Data Processing and Analysis Consortium (DPAC,
https://www.cosmos.esa.int/web/gaia/dpac/consortium). Funding for the DPAC has
been provided by national institutions, in particular the institutions
participating in the Gaia Multilateral Agreement. This research has made use
of the VizieR catalogue access tool, CDS, Strasbourg, France (DOI :
10.26093/cds/vizier). The original description of the VizieR service was
published in 2000, A&AS 143, 23

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