Aims. Characterizing the interior structure of exoplanets is essential for understanding their
diversity, formation, and evolution. As the interior of exoplanets is inaccessible to …

Context. The number of exoplanets with precise mass and radius measurements is
constantly increasing thanks to novel ground-and space-based facilities such as HARPS …

Multiplanetary systems spanning the radius valley are ideal testing grounds for exploring the
different proposed explanations for the observed bimodality in the radius distribution of close …

The next generation of telescopes will yield a substantial increase in the availability of high-
quality spectroscopic data for thousands of exoplanets. The sheer volume of data and …

Current endeavours in exoplanet characterization rely on atmospheric retrieval to quantify
crucial physical properties of remote exoplanets from observations. However, the scalability …

Context. Out of the more than 5000 detected exoplanets, a considerable number belong to a
category called “mini-Neptunes”. Interior models of these planets suggest that they have …

Context. New spectroscopic surveys will increase the number of astronomical objects in
need of characterisation by more than an order of magnitude. Machine learning tools are …

Aims. We introduce a new deep-learning approach for the reconstruction of 3D dust density
and temperature distributions from multi-wavelength dust emission observations on the …

Context. NASA's Juno mission provided exquisite measurements of Jupiter's gravity field that
together with the Galileo entry probe atmospheric measurements constrains the interior …

Aims. We introduce a new deep-learning tool that estimates stellar parameters (eg effective
temperature, surface gravity, and extinction) of young low-mass stars by coupling the …