Project level: Honours

Some hot stars are really peculiar. For example, the mercury-manganese HgMn stars have vast clouds of mercury and manganese in their atmosphere which darken patches of the star, showing up as the star rotates in broadband photometry (such as with the TESS or Kepler space telescopes) and in spectroscopy (deeper and shallower absorption lines of that element). There are many more such types of chemically-peculiar star: the magnetic and non-magnetic Ap and Am stars, helium-weak stars, or the λ Boötis stas.

We currently have a limited understanding of what causes this effect: in some cases magnetic and radiative effects in the stellar atmosphere can cause some elements to levitate in a layer where they are over-represented in the spectrum. (The star is not just made of mercury and manganese!) In other cases, it might be because the star has recently accreted material from a companion.

For a few such stars, astronomers have been able to reconstruct maps of the distribution of chemical peculiarity across the surface of the star, with painstaking measurements of the Doppler and Zeeman shifts of spectral lines. The data from Kepler and TESS allow us to go further. By watching the star as it rotates, we can map out the pattern of starspots as they come into and out of view - and do this not just for a few stars, but for hundreds. In this project, you will use state of the art software tools to map the chemically-peculiar stars in TESS, and gain insight into the origins of this chemical peculiarity by producing the first large statistical sample of these maps.

This project will suit a student with strong Python skills well, and an interest in statistics, machine learning, and stellar astrophysics.

Project members

Dr Benjamin Pope

ARC DECRA Fellow
Physics