In the context of a growing number of known exoplanets, this project aims to assess systematically how the physical parameters of a star influence the fate of the planet’s atmosphere.
We will analyse how the high-energy environment created by the star affects the atmosphere of a nascent and juvenile planet on an example of exoplanets orbiting young stars and the history of the Earth. The amount of atmospheric loss will strongly depend on the star-planet distance, the mass of the planet, and the composition of the atmosphere, and therefore differ for an Earth-like planet compared to a Jupiter-like planet, which is capable of keeping the primordial H/He atmosphere due to their large masses.
However, hypothetically, a Neptune-sized planet close to a very active host star could be stripped from its entire primordial atmosphere to its potentially bare rocky core, which provides a truly alien and unexplored evolutionary pathway for Earth-like planet formation.
This master's thesis project will study the star-planet interaction using chemistry kinetic code. We will study the mass loss, atmosphere constituents dynamics and evolution of the primordial atmosphere of a young Earth-like planet. These analyses will lead to a better understanding of atmosphere evolution on terrestrial-size planets including different fates like the airless bare core for TRAPPIST-1 b, the dense lifeless atmosphere of Venus and the habitable conditions on Earth.
The work has implications for expected findings from ongoing NASA’s JWST and upcoming ESA’s Ariel missions. The candidate will learn the basic principles of exoplanet atmosphere detection from transmission spectrography, get familiar with the Python chemistry kinetic code Vulcan, and use machine learning and k-means clustering for data analyses.
They will also learn about the current understanding of exoplanet and star formation and evolution. An interest in exoplanet sciences, Python programming, and machine learning is an advantage.
Image credit: NASA/Ames/JPL-Caltech.