Recently in the TRAPPIST-1 Category


The precise characterization of terrestrial atmospheres with the James Webb Space Telescope (JWST) is one of the utmost goals of exoplanet astronomy in the next decade.

A new international study led by astrophysicist Eric Agol from the University of Washington has measured the densities of the seven planets of the exoplanetary system TRAPPIST-1 with extreme precision, the values obtained indicating very similar compositions for all the planets.

Context. Planetary mass and radius data are showing a wide variety in densities of low-mass exoplanets. This includes sub-Neptunes, whose low densities can be explained with the presence of a volatile-rich layer. Water is one of the most abundant volatiles, which can be in the form of different phases depending on the planetary surface conditions.

Transiting compact multi-planet systems provide many unique opportunities to characterize the planets, including studies of size distributions, mean densities, orbital dynamics, and atmospheric compositions.

Variations in the reflective properties of the bulk material that comprises the surface of land-dominated planets will affect the planetary energy balance by interacting differently with incident radiation from the host star.

We have collected transit times for the TRAPPIST-1 system with the Spitzer Space Telescope over four years.

Recent observations of the potentially habitable planets TRAPPIST-1 e, f, and g suggest that they possess large water mass fractions of possibly several tens of wt% of water, even though the host star's activity should drive rapid atmospheric escape.

The discovery of potentially habitable planets around the ultracool dwarf star Trappist-1 naturally poses the question: could Trappist-1 planets be home to life?

Our solar system has one habitable planet -- Earth. A new study shows other stars could have as many as seven Earth-like planets in the absence of a gas giant like Jupiter.

We combine analytical understanding of resonant dynamics in two-planet systems with machine learning techniques to train a model capable of robustly classifying stability in compact multi-planet systems over long timescales of 109 orbits.