Exoplanet atmospheres

We present the first panchromatic JWST transmission spectrum of an ultra-hot Jupiter, combining NIRISS and NIRSpec observations to constrain KELT-7b's atmospheric properties. We show evidence for charge migration in our NIRISS SOSS observation between 1--1.5~μm, a wavelength range crucial to test for enhanced H− previously inferred from HST WFC3/IR G141 observations. We mitigate charge migration by fitting the ramp after extracting 1D stellar spectra at the group level. This ``late-ramp-fit'' method accurately calculates KELT-7b's transmission spectrum between 1-1.5~μm at higher signal-to-noise. Using the transit-derived stellar mean density during stellar property inference reveals that KELT-7 is a 640±100 Myr-old, [Fe/H]=0.46±0.02 star. Combined with NIRSpec and re-reduced WFC3/UVIS G280 data, our free retrieval analysis shows strong evidence for H2O, CO2, and TiO among high-temperature species, but not H− or clouds. Unaccounted-for systematics may therefore bias longer-wavelength WFC3/IR G141 transit depths shallower. Our free retrieval, two equilibrium retrievals, and self-consistent grid fit all prefer a high metallicity but find discrepant C/O ratios. Agglomerated together, we constrain a super-stellar M/H=92+24−23×~Solar and C/O~≤0.9, suggesting enhanced metal accretion in the later stages of KELT-7b's formation. Our GCMs explain the observed lack of limb asymmetry with superrotating jet-driven efficient horizontal mixing. The stark contrast between our panchromatic analysis and prior analyses on subsets of these data demonstrates the value of broad wavelength coverage for the comprehensive study of exoplanet atmospheres.

The Habitable Worlds Observatory (HWO), planned for launch in the 2040s, represents the next major step in exoplanet characterisation. HWO will, for the first time, enable detailed studies of the atmospheres and surfaces of Earth-like exoplanets through high-contrast reflection spectroscopy across the UV, optical, and near-infrared. These wavelength ranges provide access to key molecular absorption features, including O2, O3, H2O, CO2, and CH4, as well as potential surface biosignatures such as the vegetation red edge or ocean glint, making HWO a cornerstone mission for assessing planetary habitability.
Clouds are a dominant factor in determining planetary climate and observability, yet their properties remain highly degenerate when constrained using reflected flux alone. Spectropolarimetry, a measure of the polarisation state of reflected light as a function of wavelength and orbital phase, provides a powerful complementary diagnostic. Polarisation is highly sensitive to cloud particle size, composition, shape, vertical distribution, and surface type, enabling degeneracies between atmospheric and surface models to be broken. Numerous studies have demonstrated the value of polarimetry for characterising a wide range of exoplanets, from hot Jupiters to cooler potentially habitable worlds.
HWO's proposed instrument suite includes a coronagraph, a high-resolution imager, and a candidate high-resolution spectropolarimeter, offering multiple pathways to exploit polarimetry across diverse planetary regimes. This white paper argues that incorporating polarimetric capability into HWO instruments would significantly enhance the mission's scientific return. We highlight the unique opportunity for UK leadership in both instrument development and theoretical modelling, and advocate for a strong UK role in shaping HWO's polarimetric capabilities to maximise its impact on exoplanet science.

The search for life beyond Earth will depend on detecting faint signatures in the atmospheres of potentially habitable exoplanets. Interpreting those signatures requires understanding the host planet's climate: the same molecule may signal life on one planet and abiotic chemistry on another. Global climate models (GCMs) provide this understanding, but individual runs can require up to millions of core-hours and substantial domain expert time. Machine-learning emulators could remove this bottleneck, but progress has been limited by the absence of a curated, multi-model exoclimate dataset. We introduce ThousandWorlds, an ML-ready benchmark for exoclimate emulation and for the broader regime of low-data, multi-simulator, parameter-to-field regression. The dataset contains approximately 1800 simulations from five GCMs, mapping eight planet parameters to 3D atmospheric fields including temperature, humidity, winds, clouds, and radiation. Three nested subsets define progressively harder challenges: single-simulator regression, multi-simulator regression with complete observations, and multi-simulator regression with structured missingness. We propose two evaluation protocols: one for ranking methods, and one that measures performance relative to the disagreement between GCMs themselves. We evaluate seven baselines spanning simple methods, deep learning, and Gaussian processes. GP-based methods perform best, suggesting that ThousandWorlds exposes a regime where off-the-shelf deep learning does not yet succeed.