Disentangling the effects of spotty stars from exoplanet atmosphere observation

Project highlights:
JWST is taking us into a new era of exoplanet science, enabling us to characterise
exoplanet atmospheres with unprecedented detail and precision.
This project will develop tools that enable us to disentangle atmospheric signals from
the effects of stellar activity.
The candidate will have the opportunity to work with JWST data and be part of a
thriving UK exoplanet observation and theory community.
The candidate will develop their programming skills in Python by developing a new
model framework.
Project description:
With the current operation of JWST, we are entering a new era in the study of exoplanet
atmospheres. JWST provides new access to infrared wavelengths and, due to its large primary mirror
size, much higher precision than data from the Hubble Space Telescope. This allows us to use the
technique of transmission spectroscopy to much greater effect than previously.
When a planet passes in front of its parent star from our viewpoint, some of the starlight passes
through the planet's atmosphere. If we measure how much light is transmitted as a function of
wavelength, and compare our observations with atmospheric models using 'retrieval' algorithms, we
can infer the composition of the planet's atmosphere. Access to the infrared enables us to detect a
much greater range of molecules.
The transmission spectroscopy technique contains an implicit assumption that the part of the star
the planet crosses during transit is representative of the whole visible disc, but this isn't necessarily
true. Stars have starspots (cooler regions) and faculae (hotter regions), which are often not
uniformly distributed across the surface. If the region the planet crosses is particularly spotty (or
unspotty) relative to the rest of the disc, this can introduce artefacts in the measured planetary
spectrum that require correction.
During this project, the student will work to develop simple representations of these effects that can
be incorporated into the models used to interpret data from JWST. NEMESIS is a retrieval program
written in Python and Fortran that uses a Nested Sampling wrapper algorithm to sample spectra
generated from various atmospheric models, and provide the best fitting range of solutions for a
given observation. The majority of the work for this project is anticipated to be with the Python part
of the code, but some experience of Fortran would be an asset.
The simple models for NEMESIS will be tested against synthetic observations created using more
complex and realistic models of stellar surfaces, developed by external supervisor Dr Yvonne Unruh.
These will then be applied to JWST and Hubble data. The student will also have the opportunity to
participate in the preparation for the Ariel mission, due to launch in 2029, for which these tools will
also be required.