BOWIE-ALIGN: A JWST comparative survey of aligned vs misaligned hot Jupiters
to test the dependence of atmospheric composition on migration history–A

# BOWIE-ALIGN: A JWST comparative survey of aligned vs misaligned hot Jupiters
to test the dependence of atmospheric composition on migration history

James Kirk1 E-mail<EMAIL_ADDRESS>(JK) Eva-Maria Ahrer2 Anna B.T.
Penzlin1 James E. Owen1 Richard A. Booth3 Lili Alderson4 Duncan A. Christie2
Alastair B. Claringbold5,6 Emma Esparza-Borges7,8 Chloe E. Fisher9 Mercedes
López-Morales10 N. J. Mayne11 Mason McCormack12 Annabella Meech10,9 Vatsal
Panwar5,6 Diana Powell12 Jake Taylor9 Denis E. Sergeev11 Daniel Valentine4
Hannah R. Wakeford4 Peter J. Wheatley5,6 Maria Zamyatina11
1Department of Physics  Imperial College London  Prince Consort Road  SW7 2AZ
London  UK
2Max Planck Institute for Astronomy (MPIA)  Königstuhl 17  69117 Heidelberg
Germany
3School of Physics and Astronomy  University of Leeds  Leeds LS2 9JT  UK
4School of Physics  University of Bristol  HH Wills Physics Laboratory
Tyndall Avenue  Bristol BS8 1TL  UK
5Centre for Exoplanets and Habitability  University of Warwick  Gibbet Hill
Road  Coventry CV4 7AL  UK
6Department of Physics  University of Warwick  Gibbet Hill Road  Coventry CV4
7AL  UK
7Instituto de Astrofísica de Canarias  San Cristóbal de La Laguna  Tenerife
E-38200  Spain
8Departamento de Astrofísica  Universidad de La Laguna  San Cristóbal de La
Laguna  Tenerife E-38200  Spain
9Department of Physics  University of Oxford  Keble Road  Oxford  OX1 3RH  UK
10Center for Astrophysics ${\rm\mid}$ Harvard & Smithsonian  60 Garden St
Cambridge  MA 02138  USA
11Department of Physics and Astronomy  Faculty of Environment  Science and
Economy  University of Exeter  Exeter EX4 4QL  UK
12Department of Astronomy and Astrophysics  University of Chicago  IL  60657
USA

(Accepted XXX. Received YYY; in original form ZZZ; 2024)

###### Abstract

A primary objective of exoplanet atmosphere characterisation is to learn about
planet formation and evolution, however, this is challenged by degeneracies.
To determine whether differences in atmospheric composition can be reliably
traced to differences in evolution, we are undertaking a new survey with JWST
to compare the compositions of a sample of hot Jupiters that orbit F stars
above the Kraft break with different orbital alignments. Under the assumption
that aligned planets migrate through the inner disc, while misaligned planets
migrate after disc dispersal, the act of migrating through the inner disc
should lead to a measurable difference in the C/O between aligned and
misaligned planets. We expect the amplitude and sign of this difference to
depend on the amount of planetesimal accretion and whether silicates accreted
from the inner disc release their oxygen. Here, we identify all known
exoplanets that are suitable for testing this hypothesis, describe our JWST
survey, and use noise simulations and atmospheric retrievals to estimate our
survey’s sensitivity. With the selected sample of four aligned and four
misaligned hot Jupiters, we will be sensitive to the predicted differences in
C/O between aligned and misaligned hot Jupiters for a wide range of model
scenarios.

###### keywords:

keyword1 – keyword2 – keyword3

##  1 Introduction

It has long been proposed that measuring an exoplanet’s atmospheric
composition (specifically its carbon-to-oxygen ratio, C/O) can reveal
information regarding where a planet formed with respect to different ice
lines (e.g., Öberg et al., 2011; Madhusudhan et al., 2014; Booth et al., 2017;
Schneider & Bitsch, 2021). The basic principle that the C/O of a planet’s
atmosphere is dependent on where it accreted its atmosphere relative to
different C- and O-bearing molecular ice lines is robust, and there is little
doubt atmospheric composition will lead to insights into planet formation and
evolution. However, there are many uncertainties when relating an individual
planet’s composition to its formation location, which, combined with our lack
of sensitivity to carbon-bearing molecules in the pre-JWST era, have prevented
a detailed investigation of how atmospheric composition depends on planet
formation and evolution.

The challenges to our understanding of the link between formation and
composition include the uncertain and evolving locations of ice lines within
discs (e.g., Morbidelli et al., 2016; Panić & Min, 2017; Owen, 2020), the
observed diversity of protoplanetary discs (e.g., Law et al., 2021), how much
solid versus gaseous material is accreted during planet formation (e.g.,
Espinoza et al., 2017), and the drift of solids relative to the gas in the
disc (e.g., Booth et al., 2017). Furthermore, transit spectroscopy
observations of exoplanets’ atmospheres probe the atmospheric composition at
the planetary limb which might hold inhomogeneities caused by, e.g., local
atmospheric mixing (e.g., Zamyatina et al., 2024) or cloud formation (e.g.,
Helling et al., 2016), therefore not necessarily reflecting the bulk planet’s
atmospheric composition (Müller & Helled, 2024).

Penzlin & Booth et al. 2024 used simulations to demonstrate that the
unconstrained values of key disc and planet formation parameters such as dust-
to-gas mass, disc temperature, and the relative drift of dust to gas (Stokes-
to-alpha number), create a degeneracy between C/O and metallicity (defined
here as O/H) over a wide dynamic range. These uncertainties are hard to
constrain robustly through independent observations. Therefore, we propose the
best way to determine whether differences in formation history lead to a
measurable difference in atmospheric C/O and metallicity is by comparing
populations of planets for which we have independent evidence that they
underwent different evolutionary pathways. Specifically, as Penzlin & Booth et
al. 2024 show, planets that have undergone disc-free (high eccentricity)
migration should have different C/O and metallicity to planets that have
undergone disc migration. The idea behind this is that disc-migrated planets
will accrete solids from the inner disc during their migration while disc-
free, high-eccentricity migrated planets will not since they complete their
migration after disc dispersal (Figure 1).

Figure 1: Schematic demonstrating the hypothesis test. The left hand diagram
shows a planet that is born far out in the disc before undergoing partial
migration through ice lines (CO and CO2 in this example). Subsequently, there
are two possible pathways this planet might follow to become a hot Jupiter: i)
migration through the inner disc whereby the planet’s orbit remains aligned
(top right). In this case, the planet migrates through the H2O ice line (light
blue dashed circle) and accretes O-rich solids from the inner disc. ii) disc-
free (high-eccentricity) migration, whereby the planet is perturbed onto an
eccentric and misaligned orbit before undergoing tidal circularisation. This
migration occurs after disc dispersal and so the planet does not accrete the
O-rich solids from the inner disc.

With the advent of JWST’s revolutionary precision and wavelength coverage of
carbon-bearing molecules (e.g., JWST Transiting Exoplanet Community Early
Release Science Team et al., 2023; Alderson et al., 2023; Rustamkulov et al.,
2023), we are able to test these predictions for the first time against a
well-designed target sample. To this end, we are undertaking a survey with
JWST to compare the C/O and metallicity of four disc migrated hot Jupiters
with four high eccentricity migrated hot Jupiters. We focus specifically on
hot Jupiters, and not smaller planets, since their giant masses likely
necessitate formation beyond ice lines and hence subsequent migration (e.g.,
Lin et al., 1996; Rafikov, 2006; Dawson & Johnson, 2018). Furthermore, their
massive H/He envelopes retain the primordial composition, without being
changed by atmospheric loss (e.g., Owen & Lai, 2018). We will combine the
transmission spectra of five planets from our new observational programme (GO
3838, 49.2 hours, PIs: Kirk & Ahrer) with spectra of three planets from other
programmes (GTO 1274, PI: Lunine; GTO 1353, PI: Lewis; GO 3154, PI: Ahrer).
Our programme is called BOWIE-ALIGN, with BOWIE corresponding to the core
institutions of our collaboration (Bristol, Oxford, Warwick, Imperial, Exeter)
and ALIGN standing for A spectral Light Investigation into hot gas Giant
origiNs.

We distinguish disc migrated from high eccentricity migrated hot Jupiters via
their sky-projected orbital alignments (‘obliquities’) around F stars where
tidal realignment is thought to be inefficient (Albrecht et al., 2012). Disc
migration is expected to lead to a slowly shrinking planetary orbit and the
accretion of gas, dust and planetesimals in the migrating planet’s path (e.g.,
Dawson & Johnson, 2018). This results in little change in the eccentricity and
inclination of a planet’s orbital plane, which remains aligned with the
stellar spin axis (Figure 1). High eccentricity migration likely occurs after
disc dispersal. Under this mechanism, it is thought that an initially cold
Jupiter is perturbed into an eccentric orbit via interactions with a third
body (e.g., Rasio & Ford, 1996; Wu & Murray, 2003), which drive up the
eccentricity and inclination of the planet (e.g., Kozai, 1962; Lidov, 1962;
Muñoz et al., 2016). This method of migration is believed to result in
misalignments between a planet’s orbital plane and the stellar spin axis.
Therefore, by comparing aligned and misaligned hot Jupiters we can test the
predicted impacts of migration method on atmospheric C/O and metallicity.

The key with our survey is that since formation models are unable to a priori
predict the specific values of C/O and metallicity for individual scenarios,
they robustly predict a difference. As described by Penzlin & Booth et al.
2024, the sign of this difference is even uncertain owing to uncertainties in
formation models. Thus, by comparing one sample to another we can test this
difference, along with narrowing down the range of uncertain disc parameters
currently rendering the models unpredictive. Hence, rather than is the common
expectation of measuring an atmospheric composition and comparing it to
formation models to determine how the planet formed, we are proposing an
opposite approach. Namely, testing the idea that different formation scenarios
predict different atmospheric compositions, then using our measured
compositions to constrain the formation and evolution models.

Our paper is set out as follows: in Section 2 we describe our target
selection, in Section 3 we describe the strategy behind our JWST observations,
in Section 4 we explain our simulated transmission spectra and retrievals that
are informed by coupled disc chemistry and planet formation models, in Section
5 we discuss our data reduction and analysis plans, in Section 6 we discuss
existing literature results for two planets in the sample, and in Section 7 we
describe additional science that will be enabled by our homogeneously observed
sample. We summarise in Section 8.

##  2 Target selection

To test whether atmospheric composition depends on migration history, we
needed to construct a sample of planets with large predicted atmospheric
signals and precisely measured obliquities. To do this, we drew from the
TEPCAT catalogue111https://www.astro.keele.ac.uk/jkt/tepcat/obliquity.html,
accessed 23rd May 2024 (Southworth, 2011) of 180 exoplanets with measured sky-
projected obliquities, $\lambda$, primarily measured via the Rossiter-
McLaughlin effect (Rossiter, 1924; McLaughlin, 1924). We define misaligned
exoplanets as those with sky-projected obliquities $|\lambda|$ $>45$ ∘ and
aligned planets as those with $|\lambda|$ $<30$ ∘ and only considered those
planets with precisely measured obliquities ($\sigma$($|\lambda|$) $<15$ ∘).
While we based our sample selection on sky-projected obliquities, it is
possible that planets with low sky-projected obliquities could, in fact, be
misaligned in 3D space once the stellar inclination is resolved. However, of
the 20 planets with measured sky-projected _and_ 3D obliquities from the
TEPCAT catalogue (Southworth, 2011), only one (5 % of the total) is aligned in
2D, sky-projected space and is misaligned in 3D space. Therefore, our target
selection based on sky-projected obliquities is unlikely to be biased by
potential 3D misalignments.

Importantly, we only considered planets that orbit stars above the Kraft
break, defined as a sharp decrease in rotational velocity of stars due to
tidal dissipation in their convective envelopes (Kraft, 1967). Stars below the
Kraft break have deeper convective envelopes, so tidal-realignment is more
efficient (e.g., Albrecht et al., 2012; Spalding & Winn, 2022). In other
words, planets with low obliquities orbiting stars below the Kraft break may
have tidally realigned after high-eccentricity migration, and therefore, it is
believed that their obliquities are independent of their migration method. By
choosing hot stars with radiative envelopes, we can be confident that hot
Jupiters have retained their primordial obliquities. In practice, this
restricts us to only F and A stars. We chose to consider only F stars (stellar
effective temperature, $\mathrm{T_{eff}}\leq 7400$ K) to limit potential
atmospheric differences arising from differing stellar XUV histories and
photodissociation rates.

As explained in the introduction, we chose to focus only on hot Jupiters
(planet mass, MP > 0.3 MJ, planet radius, RP > 0.5 RJ, 1000 < Teq < 2050 K,
where Teq is planet equilibrium temperature) since they are likely to have
formed beyond ice lines before migrating to their current locations (e.g., Lin
et al., 1996; Rafikov, 2006). The upper bound on the equilibrium temperature
was chosen to make sure that H2O would not be thermally dissociated, which
could complicate our inferences of the O abundance and hence the determination
of C/O (e.g., Kreidberg et al., 2018; Lothringer et al., 2018). The value of
2050 K was chosen because HST/WFC3 observations of WASP-19b (Teq $=2077\pm 34$
K, Bernabò et al. 2024) revealed significant water absorption without
dissociation (Huitson et al., 2013). The lower temperature bound was to avoid
including warm Jupiters that may undergo a different migration pathway to hot
Jupiters (e.g., Petrovich & Tremaine, 2016; Jackson et al., 2021).

While hot Jupiters have large amplitude atmospheric signals, the signal to
noise will also depend on the brightness of the host star. For this reason, we
made a final cut based on the Transmission Spectroscopy Metric (TSM, Kempton
et al., 2018), which accounts for the stellar magnitude. We chose to only
consider planets with a K band TSM $>100$.

After these cuts, we were left with a potential 16 hot Jupiters, comprising
seven aligned and nine misaligned planets. As we show in Section 4, adding
more low noise planets does not significantly improve programme sensitivity.
The list of potential targets is given in Table 1. From this list, we sub-
selected the targets with the highest TSM signals. We did not select WASP-79b
due to previous observations of stellar contamination in its transmission
spectrum (Rathcke et al., 2021) and did not include HAT-P-41b among our
aligned sample owing to its relatively higher (more misaligned) obliquity
($\lambda=-22.1^{+0.8}_{-6.0}$∘, Johnson et al. 2017).

Our final list of targets is: TrES-4b, KELT-7b, HD 149026b, NGTS-2b (all
aligned), WASP-94Ab, WASP-17b, HAT-P-30b and WASP-15b (all misaligned). The
first panel of Figure 2 shows the planetary equilibrium temperatures and sky-
projected obliquities of our sample, while the second panel shows the stellar
effective temperatures and metallicities ([Fe/H]) along with the metallicity-
dependent Kraft break from Spalding & Winn (2022). These planets span
equilibrium temperatures of 1604–2028 K, masses of 0.368–1.280 MJ and radii of
0.813–1.932 RJ. While we are primarily interested in how their atmospheric
composition depends on obliquity and hence migration, this uniform sample of
hot Jupiters orbiting F stars will allow for new insights into hot Jupiter
atmospheres in general and enable several ancillary science cases, some of
which we address in Section 7.

Of these planets, we are acquiring new JWST observations of TrES-4b, KELT-7b,
NGTS-2b, HAT-P-30b and WASP-15b through Programme GO 3838 (PIs: Kirk & Ahrer)
with the other targets already observed or due to be observed by other
approved programs (WASP-17b, GTO 1353, PI: Lewis; HD 149026b, GTO 1274, PI:
Lunine, Bean et al. 2023; WASP-94Ab, GO 3154, PI: Ahrer). All of these planets
have eccentricities consistent with zero. With this combination of archival
and new data, we will obtain (i) a four planet sample of aligned planets that
likely migrated through the disc and (ii) a four planet sample of misaligned
planets that ended their formation at orbits near and beyond the water ice
line, and reach their final orbits after disc dispersal.

Table 1: The list of 16 hot Jupiters that passed our selection criteria and are therefore suitable to address our science question. The eight planets in bold are those in our JWST survey. Each list is ordered by the K-band TSM (Kempton et al., 2018). See the text for a description of these parameters. The parameter values are taken from TEPCAT (Southworth, 2011). The following references for each planet are in the order discovery paper, most recent detailed study (as determined by TEPCAT), and obliquity: TrES-4b (Mandushev et al., 2007; Sozzetti et al., 2015; Narita et al., 2010); KELT-7b (Bieryla et al., 2015; Tabernero et al., 2022); HAT-P-41b (Hartman et al., 2012; Johnson et al., 2017); HD 149026b (Sato et al., 2005; Carter et al., 2009; Albrecht et al., 2012); NGTS-2b (Raynard et al., 2018; Anderson et al., 2018); XO-6b (Crouzet et al., 2017; Ridden-Harper et al., 2020); WASP-3b (Pollacco et al., 2008; Maciejewski et al., 2013; Oshagh et al., 2013); WASP-94Ab (Neveu-VanMalle et al., 2014; Ahrer et al., 2024); WASP-17b (Anderson et al., 2010; Triaud et al., 2010; Southworth et al., 2012); HAT-P-30b (Johnson et al., 2011; Blažek et al., 2022; Cegla et al., 2023), WASP-79b (Smalley et al., 2012; Brown et al., 2017); WASP-15b (West et al., 2009; Southworth et al., 2013; Triaud et al., 2010); WASP-7b (Hellier et al., 2009; Southworth, 2012; Albrecht et al., 2012); WASP-109b (Anderson et al., 2014; Addison et al., 2018); HAT-P-6b (Noyes et al., 2008; Southworth, 2012; Albrecht et al., 2012); WASP-180b (Temple et al., 2019). Planet |  $\mathrm{T_{eff}}$ (K) | [Fe/H] |  $\lambda$ (∘) |  $\mathrm{M_{P}}$ ($\mathrm{M_{Jup}}$) |  $\mathrm{R_{P}}$ ($\mathrm{R_{Jup}}$) |  $\mathrm{T_{eq}}$ (K) | K magnitude | TSMK
---|---|---|---|---|---|---|---|---
Aligned sample
TrES-4ba | $6295\pm 65$ | $0.280\pm 0.090$ | $6.30\pm 4.70$ | $0.494\pm 0.035$ | $1.838\pm 0.086$ | $1795\pm 37$ | 10.3 | 302
KELT-7ba | $6699\pm 24$ | $0.240\pm 0.020$ | $-10.55\pm 0.27$ | $1.280\pm 0.170$ | $1.496\pm 0.035$ | $2028\pm 17$ | 7.5 | 287
HAT-P-41b | $6390\pm 100$ | $0.210\pm 0.100$ | $-22.10^{+0.80}_{-6.00}$ | $0.800\pm 0.1020$ | $1.685\pm 0.064$ | $1941\pm 38$ | 9.7 | 237
HD 149026bb | $6147\pm 50$ | $0.360\pm 0.050$ | $12.00\pm 7.00$ | $0.368\pm 0.014$ | $0.813\pm 0.026$ | $1634\pm 57$ | 6.8 | 221
NGTS-2ba | $6450\pm 50$ | $-0.090\pm 0.090$ | $-11.30\pm 4.80$ | $0.670\pm 0.089$ | $1.536\pm 0.062$ | $1638\pm 29$ | 9.8 | 189
XO-6b | $6720\pm 100$ | $-0.070\pm 0.100$ | $-20.70\pm 2.30$ | $2.010\pm 0.710$ | $2.080\pm 0.180$ | $1641\pm 24$ | 9.3 | 142
WASP-3b | $6340\pm 90$ | $0.161\pm 0.063$ | $20.0\pm 3.3$ | $1.770\pm 0.100$ | $1.346\pm 0.063$ | $2020\pm 35$ | 9.4 | 113
Misaligned sample
WASP-94Abc | $6170\pm 80$ | $0.26\pm 0.15$ | $123\pm 3$ | $0.452\pm 0.034$ | $1.720\pm 0.055$ | $1604\pm 24$ | 8.9 | 590
WASP-17bd | $6550\pm 100$ | $-0.25\pm 0.09$ | $-148.5^{+4.2}_{-5.4}$ | $0.477\pm 0.033$ | $1.932\pm 0.053$ | $1755\pm 28$ | 10.2 | 488
HAT-P-30ba | $6338\pm 42$ | $0.12\pm 0.03$ | $70.5^{+2.9}_{-2.8}$ | $0.711\pm 0.028$ | $1.417\pm 0.033$ | $1630\pm 42$ | 9.2 | 333
WASP-79b | $6600\pm 100$ | $0.03\pm 0.10$ | $-95.2^{+0.9}_{-1.0}$ | $0.860\pm 0.080$ | $1.530\pm 0.040$ | $1716\pm 25$ | 9.1 | 246
WASP-15ba | $6573\pm 70$ | $0.09\pm 0.04$ | $-139.6^{+4.3}_{-5.2}$ | $0.592\pm 0.019$ | $1.408\pm 0.046$ | $1676\pm 29$ | 9.7 | 201
WASP-7b | $6520\pm 70$ | $0.00\pm 0.10$ | $86.0\pm 6.0$ | $0.980\pm 0.130$ | $1.374\pm 0.094$ | $1530\pm 45$ | 8.4 | 198
WASP-109b | $6520\pm 140$ | $-0.22\pm 0.08$ | $99.0^{+10.0}_{-9.0}$ | $0.910\pm 0.130$ | $1.443\pm 0.053$ | $1685\pm 40$ | 10.2 | 143
HAT-P-6b | $6570\pm 80$ | $-0.13\pm 0.08$ | $165.0\pm 6.0$ | $1.063\pm 0.057$ | $1.395\pm 0.081$ | $1704\pm 40$ | 9.3 | 132
WASP-180b | $6500\pm 150$ | $0.10\pm 0.20$ | $-162.0\pm 5.0$ | $0.900\pm 0.100$ | $1.240\pm 0.040$ | $1540\pm 40$ | 9.8 | 128
aObserved by our new JWST programme, GO 3838 (PIs: Kirk & Ahrer)
bObserved in emission as part of JWST GTO 1274 (Lunine) and published in Bean
et al. (2023)
cObserved as part of JWST GO 3154 (PI: Ahrer)
dObserved as part of JWST GTO 1353 (PI: Lewis)

Figure 2: Top-panel: the obliquities and equilibrium temperatures of our
sample. The aligned targets are shown in orange and misaligned targets are in
blue. Open circles correspond to targets in JWST programme GO 3838, crosses to
targets from other JWST programmes. Bottom-panel: the effective temperatures
and metallicities of the host stars, plotted with respect to the metallicity-
dependent Kraft break which is taken from Spalding & Winn (2022).

##  3 Observing strategy

Figure 3: Two example forward models used in this analysis, both created for
TrES-4b (an aligned target). Top panel: a $3\times$ solar metallicity,
$\mathrm{C/O}=0.28$ forward model (black dashed line), corresponding to the
fiducial disc model (disc scenario 1, Section 4 and Figure 4, left panel). The
grey uncertainties correspond to the estimated JWST precision at a spectral
resolution of $R=100$. The colour lines show the contribution of different
species to the overall opacity, using line lists for H2O from Polyansky et al.
(2018), CO2 from Tashkun & Perevalov (2011), and CO from Faure et al. (2013);
Gordon et al. (2017). Bottom panel: a $30\times$ solar metallicity,
$\mathrm{C/O}=0.80$ forward model (black line), corresponding to the silicate
rainout disc model (disc scenario 2, Section 4 and Figure 4, right panel).

We will observe our targets in transmission using the JWST NIRSpec G395H
instrument mode, which covers several absorption bands from the primary O and
C bearing species in hot Jupiters (H2O, CO2 and CO, Figure 3 and e.g.,
Alderson et al. 2023). This approach will allow us to measure C/O and O/H and
thus address whether these quantities differ between aligned and misaligned
hot Jupiters. While NIRSpec/PRISM would cover the same molecular features in
addition to covering bluer wavelengths (albeit at lower spectral resolution),
it saturates more quickly than NIRSpec/G395H. The JWST Early Release Science
observations of WASP-39b (K mag = 10.2) with NIRSpec/PRISM saturated at near-
IR wavelengths (Rustamkulov et al., 2023), with the data synthesis analysis
recommending the avoidance of partial saturation with PRISM (Carter & May et
al., in press). Since four of our five GO 3838 targets are brighter than
WASP-39 (Table 1), we selected G395H to avoid saturation and to ensure a
homogeneous data set across the sample. Similarly, the observations of
WASP-17b and WASP-94Ab also make use of the G395H instrument. However, HD
149026b was observed with NIRCam in emission (Bean et al., 2023), and
therefore, we will need to take this into account when we combine the full
sample. We discuss the results of Bean et al. (2023) in Section 6.

We will observe single transits of our planets in Bright Object Time Series
mode with the 2048 subarray and F290LP filter, with each observation
consisting of $10^{2}$–$10^{3}$ integrations covering a continuous baseline of
several hours. This setup will provide high-precision spectrophotometry
covering the wavelength range 3–5 at $R=2700$, allowing us to measure the
planet’s wavelength-dependent transit depth (its ‘transmission spectrum’). For
all of our targets, we set the number of groups per integration to fill 80 %
of the full well. Our observation duration per target was set to be equal to
each planet’s transit duration plus an additional four hours. These four hours
comprise a minimum pre- and post-transit baseline of 1.5 hours to enable
accurate and precise relative transit depth measurements, along with a one
hour window for scheduling flexibility. For the non-GO 3838 targets, the out-
of-transit/eclipse baselines were 3.5 hours for WASP-94Ab (GO 3154), 5.5 hours
for WASP-17b (GTO 1353) and 5 hours for HD 149026b (GTO 1274).

As all our GO 3838 science targets are too bright to use them for target
acquisition (TA), we will utilise nearby (within splitting distance), fainter
stars to conduct our target acquisition via the Wide Aperture Target
Acquisition (WATA) mode. We have selected them using both the 2MASS (Skrutskie
et al., 2006) and GAIA DR3 (Gaia Collaboration et al., 2016, 2023) catalogues.
The first one ensures that we have the necessary brightness for each star to
achieve successful TA (SNR > 20 as verified with the JWST Exposure Time
Calculator, ETC), while the latter ensures that the positions and proper
motions of each star are accurate.

##  4 Predictions from disc models and 1D atmosphere models

In order to determine to what significance we could infer the predicted
difference between C/O and metallicity for aligned and misaligned planets, we
generated a grid of 1D isothermal chemical equilibrium atmosphere models using
the exoplanet atmosphere radiative transfer forward and retrieval modelling
code PLATON (Zhang et al., 2019, 2020). We used the $R=10000$ line lists and
PLATON’s default species which, at the wavelengths and temperatures we are
considering (Figure 3), are dominated by H2O (Polyansky et al., 2018), CO2
(Tashkun & Perevalov, 2011), and CO (Faure et al., 2013; Gordon et al.,
2017)222See Table 4 of Zhang et al. (2020) for the full list of species
included in PLATON..

Along with planetary parameters such as radius and temperature, the PLATON
forward models are parameterised by metallicity (Z) and C/O. In its default
configuration, PLATON assumes solar elemental abundances from Asplund et al.
(2009) and a C/O ratio of 0.53. For our purposes, we set Z and C/O according
to the findings of Penzlin & Booth et al. 2024. Typically, planet formation
and disc models have focused on Sun-like stars; however, the Penzlin & Booth
et al. 2024 calculations were specifically designed for F stars like those in
our survey, which have hotter discs. We considered the results of two end
scenarios that are distinctly opposing, but representative:

  1. 1)

the fiducial model whereby aligned planets trend towards lower C/O than
misaligned planets, owing to their accretion of O-rich ices from the inner
disc and the fact that accreted silicates from the inner disc evaporate their
O into the atmosphere (‘disc scenario 1’, Figure 4, left panel) and

  2. 2)

the silicate rainout model whereby accreted silicates do not evaporate their
oxygen into the atmospheres of planets. In this case, the atmospheric
composition is dominated by gas, volatiles and carbon-rich refractories. This
increases the C/O of disc migrated planets and thus causes aligned planets to
have higher C/O than misaligned planets (‘disc scenario 2’, Figure 4, right
panel).

Figure 4: The C/O and [O/H] (= Z) results of two models from the suite of
models run in Penzlin & Booth et al. 2024. Left panel: The C/O and [O/H] from
the fiducial model (disc scenario 1), showing that aligned, disc-migrated
planets (orange points) tend towards lower C/O than misaligned, high-
eccentricity migrated planets (blue points) due to the accretion of O-rich
ices in the inner disc. The dashed and dotted lines indicate the two values of
metallicity and four values of C/O that were adopted in our simulation setup
here. The white circles with uncertainties show the precision with which we’ll
be able to measure these values from an eight-planet sample (four aligned and
four misaligned planets). Right panel: The C/O and [O/H] from the silicate
rainout model (disc scenario 2), whereby silicates do not release their O into
the atmospheres of exoplanets upon accretion. In this case, aligned, disc-
migrated planets (orange points) tend towards higher C/O than misaligned,
high-eccentricity migrated planets (blue points). Again, the dashed and dotted
lines indicate the two values of metallicity and four values of C/O that were
adopted in our simulation setup here.

With these two scenarios, we then selected two different metallicities
($3\times$ and $30\times$ solar, vertical lines in Figure 4) and the
corresponding C/O ratios for both the aligned and misaligned planets
(horizontal lines, Figure 4). We selected super-solar metallicities given the
existing evidence from JWST for the prevalence of hot Jupiters with super-
solar metallicity atmospheres (e.g., Alderson et al., 2023; Ahrer et al.,
2023; Bean et al., 2023; Feinstein et al., 2023; Rustamkulov et al., 2023; Xue
et al., 2024). In total, we considered eight combinations of Z and C/O: 2 disc
scenarios (fiducial, silicate rainout) $\times$ 2 metallicities ($3\times$,
$30\times$ solar) $\times$ 2 C/O (aligned, misaligned). We also made a second
set of eight atmosphere models where we included a grey cloud deck at a
pressure of 1 mbar, which acts to obscure molecular absorption from pressures
higher than 1 mbar. We did this to consider the impact of muted molecular
absorption on our ability to constrain Z and C/O.

With the grid of 16 atmospheric forward models defined, we proceeded to
generate simulated transmission spectra for all 16 planets in Table 1 (for a
total of 256 simulated transmission spectra). Figure 3 shows two example
transmission spectra for TrES-4b, an aligned target. We added simulated error
bars to our model spectra using the JWST noise software package PandExo
(Batalha et al., 2017). For the seven planets in our sample that are scheduled
for NIRSpec/G395H observations, we set the number of groups and integrations
equal to the actual numbers used in the observations. For the five targets
observed through GO 3838, and those targets not observed to date, the number
of groups is set to get as close to, but not exceed, 80 % of the full well
calculated from the JWST ETC, with the number of integrations set to cover the
transit duration plus 4 hours, for the reasons given in Section 3. The example
spectra in Figure 3 include measurement uncertainties generated by this
method.

With the model spectra from PLATON and uncertainties from PandExo, we
proceeded to use PLATON’s retrieval capabilities to determine the precision to
which we expect to infer Z and C/O from our synthetic spectra. The free
parameters in our retrievals were the planet’s atmospheric metallicity ($\log
Z$), atmospheric C/O, planet radius ($\mathrm{R_{P}}$) and the temperature of
the isothermal atmosphere ($\mathrm{T_{iso}}$), plus a cloud-top pressure for
the cloudy spectra. We placed flat, wide priors on all the retrieved
parameters. Specifically, $\log Z$ was bounded between -1 and 3, C/O between
0.05 and 2.0, $\mathrm{R_{P}}$ between $0.9\times$ and $1.1\times$ the input
value, and $\mathrm{T_{iso}}$ between 300 and 2500 K. For models with clouds,
the cloud-top pressure was bounded between $10^{-6}-1$ bar.

We sampled the parameter space using nested sampling, implemented via dynesty
(Speagle, 2020), with 100 live points. To determine the benefit of increasing
the sample size, we sequentially combined the posteriors of the best 4 (2
aligned, 2 misaligned), 8 (4 aligned, 4 misaligned) and 16 (7 aligned, 9
misaligned) planets, where the best is determined by the TSM ranking in Table
1. The posteriors for the fiducial model setup (disc scenario 1) are shown in
Figure 5 with the silicate rainout model (disc scenario 2) shown in Figure 6.
The posteriors resulting from the fits to the models with the grey cloud deck
at 1 mbar are shown in Appendix A.

Figure 5: The retrieved $\log Z$ and C/O posteriors from the model atmospheres
with $\log Z$ and C/O set by the fiducial disc model (scenario 1). The left
column corresponds to the $3\times$ solar metallicity cases, and the right
column to $30\times$ solar. Top row: the combined $\log Z$ posteriors from the
aligned planets for a _total_ sample size of 4 (2 aligned, 2 misaligned), 8 (4
aligned, 4 misaligned) and 16 (7 aligned, 9 misaligned) planets, with the
legend giving the standard deviations of these distributions. The darkest line
corresponds to our actual sample size of 8 planets. The vertical dashed and
dotted orange lines indicate the mean and standard deviation of the 8 planet
posterior. The vertical dotted black line indicates the input value. Second
row: $\log Z$ posteriors for the misaligned planets. Bottom row: the C/O
posteriors for both the aligned (orange) and misaligned planets (blue). Figure
6: The retrieved $\log Z$ and C/O posteriors from the model atmospheres with
$\log Z$ and C/O set by the silicate rainout disc model (scenario 2). See
Figure 5 for a description of the axes.

As Figures 5 and 6 show, there are diminishing returns as the sample size is
increased. This is because to increase the total sample size, we are adding
planets with lower signal-to-noise (as shown by their TSMs in Table 1). Also,
we see that increasing the sample size leads to a more significant improvement
in the $\log Z$ and C/O precisions for the aligned planets (orange histograms)
as compared to the misaligned planets (blue histograms). This is due to the
fact that the TSMs of the two best misaligned planets (WASP-94Ab and WASP-17b)
are significantly higher than the remaining misaligned planets, while the two
best aligned planets (TrES-4b and KELT-7b) are more similar to the rest of the
aligned planets in terms of TSM (Table 1).

For our actual sample size of eight planets, we estimate a precision on C/O of
$\pm 0.055$ (aligned planets) and $\pm 0.030$ (misaligned planets) for the
$3\times$ solar metallicity, fiducial case (disc scenario 1). This would make
the predicted difference between the aligned and misaligned planets of
$\Delta(\mathrm{C/O})=0.07$ particularly challenging to measure. However, if
the planets are more metal-rich, similar to other planets in the literature,
then we would be sensitive to the difference between the C/O of aligned and
misaligned planets in the $30\times$ solar metallicity fiducial setup.
Specifically, we estimate a precision on C/O of $\pm 0.028$ (aligned planets)
and $\pm 0.016$ (misaligned planets) in this case, which would allow us to
measure the predicted difference between the C/O of the two populations of
$\Delta(\mathrm{C/O})=0.09$ at $2.0\sigma$.

For disc scenario 2, where silicates do not release their oxygen upon
accretion, this leads to a larger predicted difference in C/O between the two
populations that we’d have greater sensitivity to, with the aligned planets
having higher C/O. In this scenario, we estimate a precision on C/O of $\pm
0.021$ (aligned planets) and $\pm 0.017$ (misaligned planets) for the
$3\times$ solar metallicity atmospheres. These precisions would allow us to
measure the predicted difference between the aligned and misaligned planets
that do not undergo silicate evaporation of $\Delta(\mathrm{C/O})=0.13$ at
$3.4\sigma$. For the $30\times$ solar metallicity atmospheres, we estimate a
precision on C/O of $\pm 0.048$ (aligned planets) and $\pm 0.029$ (misaligned
planets), allowing us to measure the predicted difference between the C/O of
the two populations, $\Delta(\mathrm{C/O})=0.18$, at $2.3\sigma$.

As Figures 7 and 8 show, the inclusion of a grey cloud deck widens the C/O
posteriors by up to 0.037. Despite this, we would still be sensitive to the
predicted difference in the C/O for $30\times$ metallicity atmospheres for
both disc scenarios and the difference in C/O for the $3\times$ solar
metallicity atmosphere for disc scenario 2.

Five of our targets will be observed with additional instruments in other
approved JWST programmes. Specifically, WASP-17b will be observed with
JWST/NIRISS, NIRSpec/G395H and MIRI/LRS covering wavelengths from $\sim
0.6-12$ in both transmission and emission (GTO 1353, PI: Lewis). HAT-P-30b,
NGTS-2b, KELT-7b and WASP-94Ab will be observed with JWST/NIRISS in Cycle 3
(GO 5924, PI: Sing), providing transmission spectra from 0.6–2.8 . Combining
these future data with our G395H observations will allow for tighter
constraints than we predict here, although this will require careful analysis
to avoid instrument-specific biases arising in atmospheric inferences (e.g.,
Lueber et al., 2024).

##  5 Data reduction and analysis strategy

To reduce and analyse our data, we will follow and learn from the approaches
used in the JWST Early Release Science (ERS) Transiting Exoplanet Program
(JWST Transiting Exoplanet Community Early Release Science Team et al., 2023;
Alderson et al., 2023; Ahrer et al., 2023; Feinstein et al., 2023; Rustamkulov
et al., 2023). Specifically, we will reduce each of our data sets with more
than one independent open-source pipeline, namely
Tiberius333https://github.com/JamesKirk11/Tiberius (Kirk et al., 2017, 2021),
Eureka!444https://github.com/kevin218/Eureka (Bell et al., 2022) and ExoTiC-
JEDI555https://github.com/Exo-TiC/ExoTiC-JEDI (Alderson et al., 2022a). Each
of these reduction pipelines has been used in the ERS programme and multiple
subsequent JWST analysis papers. While they largely produce results consistent
within uncertainties (e.g., Alderson et al., 2023; Ahrer et al., 2023;
Rustamkulov et al., 2023), small differences between the resulting spectra can
lead to differences in inferences regarding planetary atmospheres (e.g.,
Constantinou et al., 2023; Kirk et al., 2024). By comparing multiple
reductions for every observation in the programme, we will determine how our
conclusions depend on the choice of reduction pipeline and explore the origins
of any differences.

Data reduction (raw images to planet spectra) is only one half of the analysis
needed to infer atmospheric C/O and Z. The second half involves going from
planet spectra to atmospheric constraints. To perform this step, we will use
at least two independent open-source forward modelling and retrieval codes to
interpret each planet’s spectrum, including
PLATON666https://github.com/ideasrule/platon (Zhang et al., 2019, 2020),
petitRADTRANS777https://gitlab.com/mauricemolli/petitRADTRANS (Mollière et
al., 2019; Nasedkin et al., 2024), CHIMERA888https://github.com/mrline/CHIMERA
(Line et al., 2012, 2013) and VULCAN999https://github.com/exoclime/VULCAN
(Tsai et al., 2017, 2021). Each of these codes makes different modelling
assumptions, including equilibrium vs. disequilibrium chemistry, isothermal
vs. non-isothermal temperature profiles, and grey vs. non-grey clouds. As has
been shown by the ERS programme (Welbanks et al., in prep.), different
modelling assumptions can also influence the measured C/O and Z from a
planet’s spectrum which motivates our decision to use at least two codes per
spectrum.

Given the differences that can arise from both the data reduction and data
interpretation steps, we will have at least one constant approach running
through all of our planetary analyses. Specifically, a Tiberius reduction with
the same extraction parameters and wavelength bins and a petitRADTRANS
retrieval with the same gases and temperature–pressure profile
parameterisation. This will avoid reduction-dependent biases, allowing us to
fairly assess how the C/O differs between the aligned and misaligned planets
in an unbiased way.

Upon the conclusion of each planet’s analysis, we will make multiple data
products openly available on Zenodo. These data sets will include extraction
input files, calibrated image files, extracted 2D stellar spectra, white and
spectroscopic light curves, light curve models, transmission spectra, forward
atmosphere models and retrieved atmosphere models.

##  6 Existing results for WASP-17b and HD 149026b

Of the planets in our sample, two have published JWST observations to date.
Grant et al. (2023) presented a MIRI/LRS spectrum of WASP-17b from 5–12 ,
combined with previously published HST and Spitzer observations of the planet
(Alderson et al., 2022b). While the different atmosphere modelling procedures
they used resulted in a range of metallicities and C/O ratios, their result
pointed towards a depleted H2O abundance and a super-solar C/O. Grant et al.
(2023) interpreted this as due to the formation of high temperature aerosols
depleting the O from the observable atmosphere. Since WASP-17b is a misaligned
planet, its super-solar C/O and sub-solar O/H could also be a natural outcome
of it not accreting O-rich material from the inner disc (Penzlin & Booth et
al. 2024) as it likely underwent high-eccentricity migration due to its high
obliquity (Table 1).

Bean et al. (2023) observed an emission spectrum with JWST/NIRCam of the
aligned hot Jupiter HD 149026b, which revealed a metal-rich atmosphere
($59-276\times$ solar) and a super-solar C/O ($0.84\pm 0.03$). However, in a
re-analysis, Gagnebin et al. (2024) showed that the planet’s emission spectrum
could also be fit with a lower, yet still super-solar, metallicity of
$20^{+11}_{-8}\times$ solar and super-solar C/O of $0.67^{+0.06}_{-0.27}$ by
using self-consistent 1D radiative-convective-thermochemical equilibrium
models. Both conclusions are qualitatively consistent with the silicate
rainout disc model (Figure 4 and Penzlin & Booth et al. 2024). HD 149026b is
the only planet in our sample that will not be observed with NIRSpec/G395H in
transmission, with the published data coming from NIRCam/F322W2+F444W emission
observations (Bean et al., 2023), which cover a similar wavelength range to
NIRSpec/G395H (2.3–5.0 ). We will perform a reanalysis of these data using our
own reduction tools (Section 5) to ensure uniformity and investigate the
implications on the measured C/O distribution for the aligned planets when
including and excluding this data set.

While it is tempting to begin to draw conclusions from the published
observations of WASP-17b and HD 149026b, we stress the importance of waiting
for the combined, homogeneously observed and analysed sample when trying to
understand planet formation.

##  7 Additional science enabled by our homogeneous sample

Given that our observing programme will produce eight homogeneously generated
and analysed, high signal-to-noise JWST spectra of hot Jupiters, there are
multiple additional science questions that can be explored, beyond our primary
goal of determining whether C/O and Z depend on migration in a measurable way.
For example, we will also investigate whether Z depends on planetary mass. In
the solar system there is an inverse relationship between mass and metallicity
among the gas giants (e.g., Kreidberg et al., 2014). Determining whether such
a trend exists among exoplanets has long been a goal of observations (e.g.,
Kreidberg et al., 2014; Wakeford et al., 2018; Welbanks et al., 2019). Our
sample of eight planets span Saturn to super Jupiter masses ($\sim 0.4-1.3$
MJ), allowing for informative comparisons with the solar system trend.

In addition to the 1D forward and retrieval modelling we will perform for our
sample, we will investigate the atmospheres of our exoplanets in 3D. Since all
of our targets are on short orbital periods they are expected to be tidally
locked. This leads to a large day-to-night temperature gradient between their
permanent daysides and permanent nightsides which, in turn, drives a super-
rotating equatorial jet that carries hot dayside gas to the nightside and cool
nightside gas to the dayside (e.g., Showman & Polvani, 2010). The result is
hotter evening limbs and cooler morning limbs, and hence different gas phase
chemistry and cloud coverage at each limb. Using a combination of
ingress/egress light curve fits and light curve fits with two planetary radii
for the morning and evening limb, Espinoza et al. (in press) have measured
limb differences in a planet’s transmission spectrum with JWST. We will apply
the same techniques, using Tiberius and
catwoman101010https://github.com/KathrynJones1/catwoman (Jones & Espinoza,
2020) to search for limb asymmetries in our data.

Furthermore, we will investigate how the F star hosts of our sample drive
photochemistry in their planets’ atmospheres. Given the detection of the
photochemical product SO2 in the transmission spectrum of WASP-39b (Alderson
et al., 2023; Rustamkulov et al., 2023; Tsai et al., 2023), a Saturn-mass
giant around a mid-G star, it is plausible that we will also see SO2 in our
planets. If we see evidence for SO2 in a planet’s spectrum, this will
necessitate photochemical modelling for that planet within the primary science
programme. Our inferences of sulphur abundances will allow for additional
precise constraints on atmospheric metallicity (e.g., Tsai et al., 2023) and
will motivate parallel investigations into if and how a planet’s history can
be inferred from its sulphur abundance.

Our investigations of 3D effects and photochemistry will be backed up by a
comparison of each planet’s observed spectrum to the spectrum predicted by a
3D climate model (also known as a general circulation model, or GCM) of a hot
Jupiter atmosphere. This comparison will reveal how the interactions between
atmospheric circulation, radiative transfer, disequilibrium thermochemistry
(e.g., Zamyatina et al., 2024) and photochemistry, in 3D, impact the
exoplanets’ morning and evening limb spectra. Furthermore, this will allow us
to determine to what extent the limb-averaged C/O and Z we measure from our
transit spectra are representative of the overall atmosphere composition.
Among the GCM outputs will be longitude-latitude maps of molecular abundances
as a function of pressure and temperature, which we will use to inform our 1D
models. The ALIGN programme sample could also serve as a high-quality
observational reference for model intercomparison projects, such as those
within the CUISINES framework (Fauchez et al., 2021; Sohl et al., 2024). The
homogeneity of data would allow to benchmark single-column models and 3D GCMs
in terms of e.g. their simulated transmission spectra, for aligned and
misaligned hot Jupiters. This will help to validate our theoretical
predictions and make the conclusions of the programme more robust.

Separately, we will perform cross-correlation analyses for all of the planets
in our sample to search for isotopologues in the planets’ atmospheres. As
Esparza-Borges et al. (2023) showed, one-pixel resolution spectra from
NIRSpec/G395H can be used to detect molecules via cross-correlation
spectroscopy, in a similar way to that done using high-resolution ground-based
instrumentation (e.g., Snellen et al., 2010; Rodler et al., 2012; Brogi et
al., 2016). In the case of WASP-39b, Esparza-Borges et al. (2023) showed that
the signal-to-noise in the cross-correlation function was maximised by the
inclusion of CO isotopologues, demonstrating the sensitivity of JWST to
isotopologues. This will provide additional information regarding our samples’
formation since elemental isotopes and molecular isotopologues may be related
to formation history (e.g., Clayton & Nittler, 2004).

Finally, our JWST observations will allow us to measure extremely precise
flux-calibrated 2.8–5.2 spectra of F stars from around 6300 to 6700 K. These
spectra will enable robust tests of 3D stellar magnetohydrodynamics (MHD)
atmospheric models, specifically those from the MuRAM code (Vögler, 2003;
Vögler et al., 2005).

We will make our data products publicly available after each planets’
analysis, which will enable the community to pursue additional ancillary
science programmes.

##  8 Summary

We are undertaking a survey with JWST to test whether an exoplanet’s
atmospheric C/O and metallicity can be observationally linked to its migration
history. Specifically, we will observe the near-infrared spectra of a sample
of eight hot Jupiters, four of which are aligned with the stellar spin axis
and four of which are misaligned. Crucially for our test, all of our targeted
exoplanets orbit F stars above the Kraft break, where tidal realignment is
expected to be inefficient, meaning that their obliquities retain information
about their migration histories.

Penzlin & Booth et al. 2024 explored the range of C/O and metallicity that can
result from a consideration of multiple different protoplanetary disc and
accretion parameters specifically for planets formed around F stars. The
principal observationally-testable results from their modelling effort are:

  1. 1.

$\mathrm{C/O}_{\mathrm{aligned}}<\mathrm{C/O}_{\mathrm{misaligned}}$: this can
be explained by the late accretion of solid material (planetesimals) by
aligned planets during their migration. This would be direct evidence that
accretion from the inner disc is important in setting a planet’s C/O and that
the aligned planets and misaligned planets did indeed undergo different
migration pathways.

  2. 2.

$\mathrm{C/O}_{\mathrm{aligned}}>\mathrm{C/O}_{\mathrm{misaligned}}$: this
scenario is possible if, during the late accretion of O-bearing silicates in
the inner disc, the silicates do not evaporate their oxygen upon accretion. In
this case, the atmospheric composition will have been dominated by accreted
gas, volatiles and carbon-rich refractories. Like result (i), this would be
direct evidence for the influence of migration on atmospheric composition.

  3. 3.

$\mathrm{C/O}_{\mathrm{aligned}}\approx\mathrm{C/O}_{\mathrm{misaligned}}$:
this result would indicate that the majority of hot Jupiter assembly is
completed before migration.

In this paper, we have described our observational test of these predictions,
including the target selection and sample size, which we investigated using a
combination of 256 1D atmospheric forward models, JWST noise simulations and
atmospheric retrievals. Our simulated transmission spectra and retrievals show
that we will be sensitive to result (i) if the atmospheric metallicities
approach $\sim 30\times$ solar, while the greater differences in C/O between
the two populations for result (ii) means we will be sensitive to these over a
wide range of metallicities. If we measure either result (i) or (ii), this
would represent the first observational confirmation that atmospheric
composition traces formation environment. If we observe super-solar
metallicities, this would indicate the accretion of solid material, like the
solar system, while sub-solar metallicities would suggest that hot Jupiter
assembly differs from the giant planets in the solar system.

Our comparative survey of a population of carefully-selected hot Jupiters will
robustly test the dependence of atmospheric composition on formation in a non-
degenerate way.

## Acknowledgements

J.K. acknowledges financial support from Imperial College London through an
Imperial College Research Fellowship grant. J.E.O. and R.A.B are supported by
Royal Society University Research Fellowships and A.B.T.P. received support
from their Enhancement Awards. This project has received funding from the
European Research Council (ERC) under the European Union’s Horizon 2020
Framework Programme (grant agreement no. 853022, PEVAP). N.J.M., D.E.S. and
M.Z. acknowledge support from a UKRI Future Leaders Fellowship [Grant
MR/T040866/1], a Science and Technology Facilities Funding Council Nucleus
Award [Grant 954 ST/T000082/1], and the Leverhulme Trust through a research
project grant [RPG-2020-82]. P.J.W. acknowledges support from the UK Science
and Technology Facilities Council (STFC) through consolidated grants
ST/T000406/1 and ST/X001121/1. V.P. acknowledges support from the UKRI Future
Leaders Fellowship grant MR/S035214/1 and UKRI Science and Technology
Facilities Council (STFC) through the consolidated grant ST/X001121/1. C.E.F.
acknowledges support from the Swiss National Science Foundation (SNSF)
Mobility Fellowship [grant no. P500PT_203110]. E.A. would like to thank C.
McDonald and D. Veras for insightful discussions.

## Data Availability

The PLATON atmosphere models and PandExo spectral uncertainties will be made
available on Zenodo.

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##  Appendix A Posteriors for cloudy models

This appendix includes the retrieved $\log Z$ and C/O posteriors when
including a grey cloud deck at 1 mbar.

Figure 7: The retrieved $\log Z$ and C/O posteriors from the model atmospheres
with $\log Z$ and C/O set by the fiducial disc model (scenario 1), with the
addition of a grey cloud deck at 1 mbar. See Figure 5 for a description of the
axes. Figure 8: The retrieved $\log Z$ and C/O posteriors from the model
atmospheres with $\log Z$ and C/O set by the silicate rainout disc model
(scenario 2), with the addition of a grey cloud deck at 1 mbar. See Figure 5
for a description of the axes.