Using a machine learning technique, a team of Instituto de Astrofísica e Ciências do Espaço researchers constrained the radius of an exoplanet with known mass. Solène Ulmer-Moll, a Ph.D. student at the Science Faculty of the University of Porto (FCUP), explains this result was obtained by using knowledge from different fields: "This novel way to forecast exoplanet radius is a perfect example of the synergy between exoplanet science and machine learning techniques." To characterize a planet, both its mass and radius are needed in order to find the planet's density, and from that, to infer its composition. But both data are only available for a reduced number of exoplanets, since the mass is often determined by radial velocity measurements, while radius is measured with the transit method. The team developed an algorithm that accurately forecasts the radius of a wide range of exoplanets, if several other planetary and stellar parameters are known, including the exoplanet's mass and equilibrium temperature.