Exoplanet discoveries have motivated numerous efforts to find unseen populations of exomoons, yet they have been unsuccessful. A plausible explanation is that most discovered planets are located on close-in orbits, which would make their moons prone to tidal evolution and orbital detachment.
The most extensive survey of atmospheric chemical compositions of exoplanets to date has revealed trends that challenge current theories of planet formation and has implications for the search for water in the solar system and beyond.
The atmosphere of exoplanets has been studied extensively in recent years, using numerical models to retrieve chemical composition, dynamical circulation or temperature from data.
When NASA's James Webb Space Telescope launches in 2021, one of its most anticipated contributions to astronomy will be the study of exoplanets -- planets orbiting distant stars.
We present new observations of the transmission spectrum of the hot Jupiter WASP-6b both from the ground with the Very Large Telescope (VLT) FOcal Reducer and Spectrograph (FORS2) from 0.45-0.83 μm, and space with the Transiting Exoplanet Survey Satellite (TESS) from 0.6-1.0 μm and the Hubble Space Telescope (HST) Wide Field Camera 3 from 1.12-1.65 μm.
We explore the application of machine learning based on mixture density neural networks (MDNs) to the interior characterization of low-mass exoplanets up to 25 Earth masses constrained by mass, radius, and fluid Love number k2.
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