Characterizing Transiting Exoplanet Atmospheres in the 2030s with the Hubble Space Telescope
White papers by STScI on "Building a Roadmap for Hubble science into the 2030s."
Abstract:
The Hubble Space Telescope inaugurated the era of exoplanet atmospheric characterization. While the James Webb Space Telescope has largely taken up the mantle of infrared atmospheric characterization, Hubble's unique short-wavelength capabilities remain unmatched. Recent theoretical advances in exoplanet atmospheric science combined with new observing strategies, like those offered by WFC3-UVIS/G280, have opened science cases that only Hubble can address for the foreseeable future. In this white paper, we discuss these new windows into the atmospheres of other worlds, focusing on characterization of their hydrostatic lower atmosphere, and identify the critical capabilities necessary for future observations. We highlight three overall science cases that will depend on the continued short-wavelength capabilities of Hubble: measuring aerosol scattering slopes, characterizing metal absorption in ultra-hot Jupiters, and understanding stellar activity with Transit Light Source effect decontamination and flare monitoring. Throughout, we highlight useful synergies between HST and JWST. This article is a response to the call for white papers by the Space Telescope Science Institute on "Building a Roadmap for Hubble science into the 2030s."
Cloudy mornings and clear evenings on a gas giant exoplanet
Science, Volume 392, 858-862 (2026)
Abstract:
The spectra of exoplanet atmospheres are affected byaerosols (clouds and hazes) of uncertain origin. Proposedaerosol formation mechanisms include gas condensation orphotochemical reactions. We measured the transmissionspectrum of the tidally locked gas giant exoplanet WaSP- 94a band identified asymmetry in its atmosphere. The morning limbis cooler and cloudy, whereas the evening limb is hotter andexhibits gaseous water absorption features. We interpret thisdifference as being due to the formation of cloud droplets nearthe morning limb, which evaporate during circulation to theevening limb. The dominant aerosols are clouds cyclingbetween the day and night sides of the atmosphere, notphotochemical hazes. The resulting asymmetry can severelybias chemical abundance measurements, unless limb-resolvedspectroscopy is available.
Detecting habitable exoplanet atmospheres with LIFE, the Large Interferometer for Exoplanets
White paper submitted to the UK Space Agency's initiative "UK Space Frontiers 2035"
Abstract:
A key goal of astronomers with the next generation telescopes is to detect signs of life in exoplanet atmospheres. NASA's next flagship is the Habitable Worlds Observatory (HWO). In the context of ESA's Voyage 2050 program, the Senior Committee report prioritises detecting habitable exoplanet atmospheres in the mid-IR. The most suited mission for this is the Large Interferometer for Exoplanets (LIFE) which can detect an even wider range of biosignatures than HWO and at lower concentrations. LIFE is a global science collaboration based out of ETH Zürich. With the UK's expertise in building infrared instruments we could play a leading role in realising an ambitious European-led mission. Notably, LIFE is able to detect necessary planetary context like surface temperature and pressure, along with a key discriminator molecule for biosignature false positives, methane, which will be much harder or impossible with HWO. Also, LIFE will be able to investigate many of the nearby rocky exoplanets known from radial velocity searches that are inaccessible to HWO due to its limited spatial resolution.
Exoplanet characterization with NASA's Habitable Worlds Observatory
White paper submitted to the UK Space Agency's initiative "UK Space Frontiers 2035"
Abstract:
Exoplanet atmosphere characterization has seen revolutionary advances over the last few years, providing us with unique insights into atmospheric chemistry, dynamics and planet formation mechanisms. However, true solar system analog planets remain inaccessible. A major goal for exoplanet science over the coming decades is to observe, and characterize, temperate rocky planets and cool gas giants in orbit around solar-type stars, with the prospect of detecting signs of habitability or even life. Characterization and categorization of these planets relies on direct spectroscopic observations capable of identifying molecular species in their atmospheres; however, these observations represent a substantial engineering challenge due to the extreme contrast between a temperate, Earth-sized exoplanet and its parent star. NASA's next flagship mission, the Habitable Worlds Observatory (HWO) - planned for launch in the mid-2040s - will boast a coronagraphic instrument capable of reaching the needed 10−10 contrast, on an ultrastable platform enabling long integration times to achieve the required signal to noise. HWO will cover near-ultraviolet to the near-infrared wavelengths, enabling detections of key biosignature molecules and habitability indicators such as ocean glint and a vegetation `red edge'. Via early involvement in this groundbreaking observatory, including a potential UK instrument contribution, the UK exoplanet community now has an important opportunity to influence the telescope's design. To maintain our international competitiveness, we must be at the forefront of observational campaigns with HWO when it eventually launches, and this comes with the need for parallel development in laboratory astrophysics and computational modelling. Maximising our exploitation of this transformative NASA mission requires consistent financial support in these areas across the next two decades.
Gaussian Process Latent Factor Regression for Low-Data, High-Dimensional Output Problems
Machine Learning (cs.LG)
Abstract:
In the sciences, regression tasks often require predicting high-dimensional outputs from few training examples. Multi-output Gaussian processes excel in low-data regimes but typically struggle with high-dimensional outputs. Compress-then-predict pipelines such as PCA-GP (principal component analysis plus Gaussian process regression) handle high dimensionality, but rely on bases optimized for reconstruction rather than prediction. To address this gap, we propose a model that represents each output as a linear-Gaussian decoding of a low-dimensional latent state drawn from a Gaussian process prior. By analytically marginalizing the decoder weights, we couple compression and prediction in a single objective that scales to high-dimensional outputs. We refer to this model as Gaussian process latent factor regression (GPLFR). We demonstrate GPLFR by building the first spatially resolved emulator of global climate models for rocky exoplanets.