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Producing optimized and accurate transmission spectra of exoplanets from telescope data has traditionally been a manual and labor intensive procedure. Here we present the results of the first attempt to improve and standardize this procedure by using artificial-intelligence-based (AI-based) processing of light curves and spectroscopic data from transiting exoplanets observed with the Hubble Space Telescope's (HST) Wide Field Camera (WFC3) instrument. We implement an AI-based parameter optimizer that autonomously operates the Eureka! pipeline to produce homogeneous transmission spectra of publicly available HST WFC3 datasets, spanning exoplanet types from hot Jupiters to sub-Neptunes. Surveying 42 exoplanets with temperatures between 280 and 2580 K, we confirm modeled relationships between the amplitude of the water band at 1.4 μ m of hot Jupiters and their equilibrium temperatures. We also identify a similar, novel trend in Neptune/sub-Neptune atmospheres, but shifted to cooler temperatures. Excitingly, a planet-mass versus equilibrium-temperature diagram reveals a “Clear Sky Corridor,” where planets between 700 and 1700 K (depending on the mass) show stronger 1.4 μ m H 2 O band measurements. This novel trend points to metallicity as a potentially important driver of aerosol formation. With HST sculpting this foundational understanding for aerosol formation in various exoplanet types ranging from Jupiters to sub-Neptunes, we present a compelling platform for the James Webb Space Telescope to discover similar atmospheric trends for more planets across a broader wavelength range.
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Ashtari et al. (2025) studied this question.
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