The number of exoplanets is now counted in thousands, which allows scientists to asses their parameters statistically. Now we can report some of the fundamental properties of exoplanets as mass, radius, orbital distance, and rotation period, and, in rare cases, take a first look into their atmospheres.
The Radius Valley is observed in the population of exoplanet radii, and it separates smaller super-Earths from larger sub-Neptunes. This most interesting feature should be explained by theoretical models. It could be due to the loss of primordial hydrogen and helium envelopes, that leave bare rocky cores. The current planet formation models predict that water-rich planets migrate from cold regions outside the snow line towards the system centre. However, the observations report the existence of planets less dense than the water at close separation from the star.
Exoplanet systems are mini-laboratories of planet formation and evolution, and analysing their system architectures can help us to constrain the physics of these processes. This master's thesis project will study the demographics of exoplanets, population trends and exoplanet system architecture.
We will study the characteristics of the known exoplanets taking into consideration complicated detection biases. and establish if the previously spotted observation trends such as the Radius Valley, deficiency of Ultrashort Hot Neptunes, the lack of small planets, and others are still present in since widely grown exoplanet population. These analyses will help prove or disprove the theories of the exoplanet system architecture creation and development, and systemwide exoplanet distribution trends. The work has implications for expected findings from ESA’s upcoming missions such as PLATO and Ariel.
The candidate will learn the basic principles of exoplanet detection methods and statistical analyses, get familiar with accessing the exoplanet databases using ADQL and Python, and use machine learning and cluster analyses in their work. They will also learn about the current understanding of exoplanet demographics by the scientific community.
An interest in exoplanet sciences, Python programming, database querying and machine learning is an advantage.
