Antonia von Stauffenberg   (Germany)

a-stauffenberg @ mpia.de

Unlocking the Properties of Exoplanet Clouds

May it be the impressive colours of Jupiter’s bands and storms, the thick sulphuric clouds covering Venus or even the clouds and hazes present on Earth, our Solar System gives examples of diverse and evolving weather systems. This indicates the extrasolar planet population should exhibit a similar complexity in their cloud coverage, however so far they have been difficult to identify and model.

My research focuses on cloud models for both transmission spectroscopy and direct imaging of exoplanets. Transmission spectroscopy is a process whereby the light coming from a host star travels through the atmosphere of the planet occulting it, and the composition and atmospheric makeup of the planet can be inferred from the absorption lines within the spectrum. Classic candidates for these techniques are also extrasolar gas giants, due to their often large atmospheric scale height which create significant signals to study their atmospheres. Direct imaging on the other hand has only been done for a handful of targets, such as self-luminous planets and more frequently for Brown Dwarfs. It allows us to investigate the spectrum of a planet (or Brown Dwarf) more directly and can yield insight into the variability of their atmospheres, which may be caused by inhomogeneous cloud covers and changing weather patterns. Over time, a number of studies have shown intricate cloud condensates including sand (silicate) and ruby (corundum) clouds that create features we can study to justify their existence and distinguish their make-up.

At this point, a number of cutting edge cloud models have been established. However, how do we determine where to introduce complexity and where to reduce it? As part of my PhD, I will be investigating different aspects of cloud models such as multi-dimensionality, inhomogeneity and particle size distributions, trying to walk the line between representative complexity and overfitting. This is essential to better fit the observed data and correctly identify the cloud species present in it. This continues to become more and more possible with the improved data quality from instruments such as JWST.

Supervisor:    Paul Molliere  (MPIA)