Naked-Eye Stars and Their Planets

This Earmarked Scholarship project is aligned with a recently awarded Category 1 research grant. It offers you the opportunity to work with leading researchers and contribute to large projects of national significance.

Supervisor – Dr Benjamin

We now know that nearly all stars host planets, and exoplanet science is now turning to bright individual systems. We aim to study the nearest, brightest stars by extending the capabilities of NASA's TESS telescope and Queensland's Mount Kent Observatory. This is significant as the best chance we have to detect planets around stars bright enough to measure the planetary and stellar properties precisely. We will search for planets transiting nearby naked-eye stars, and make crucial measurements of the masses of these stars and planets. 

On the TESS side of the project, we are interested in extending its capabilities to better study bright stars that saturate the camera - essentially, developing a 'high dynamic range' mode for a space telescope. The telescope was designed to look at thousands of faint stars, and the very brightest stars are overexposed and saturate the camera. With Dr Tim White from the University of Sydney, we have been using the technique of 'halo photometry' to recover precise light curves from K2 data and now moving on to TESS. There is a scattered light halo around the central dot of each star, from diffraction and reflection inside the telescope, covering hundreds of pixels. We build a light curve as a weighted sum of these, and optimize these weights to minimize noise in the final light curve (minimizing Total Variation by gradient descent). By borrowing these tools from machine learning we have been able to achieve remarkable results on K2 and look forward to extending these to new cases.

A second part of the project is using the Mount Kent Observatory, near Toowoomba, to measure the masses the brightest red giant stars in the sky. The Sun, like many other stars, rings like a bell, and its dominant note has a period of about 5 minutes. The turbulent motion of gas in the Sun couples to normal modes of acoustic oscillation and cause it to ring in a band of frequencies that are precisely diagnostic of conditions in the stellar interior. In order to study stars the whole sky, we want to conduct as few observations of each star as possible, while still getting enough to constrain their physics well - and as a consequence, we will typically only get sparse and irregularly-sampled time series of each star. Part of this project could therefore be to develop and apply statistical methods, such as Gaussian Processes, to doing asteroseismology with previously impossible datasets.

There are a wide range of directions possible in this project:

  • If you're interested in machine learning, you might like to work on data analysis algorithms to extract the best possible photometry or radial velocities, or to use these to infer stellar masses and planetary orbits.
  • If you want to do more classical astronomy, you can use our existing tools, find planets, characterize stars, and unravel the stories of our nearest and dearest stars.
  • If you want to work more hands on, there are opportunities to visit Mount Kent, collaborate with USQ people, and hopefully visit other observatories too.
  • We can take the methods we have developed in the context of TESS, and apply them to enhance the capabilities of the James Webb Space Telescope, the largest ever astronomical space mission. I am a co-investigator on three successful James Webb proposals, including one to study asteroids in the Solar System like Ceres which are too bright to study ordinarily with JWST. There is a great and open-ended scope for transferring ideas from this TESS project to JWST.

Preferred educational background

Applications will be judged on a competitive basis taking into account the applicant's previous academic record, publication record, honours and awards, and employment history.

A working knowledge of Python, machine learning, and stellar and planetary physics would be of benefit to someone working on this project.

The applicant will demonstrate academic achievement in the field(s) of astrophysics and the potential for scholastic success.

A background or knowledge of statistics and machine learning is highly desirable.

*The successful candidate must commence by Research Quarter 3, 2022. You should apply at least 3 months prior to the research quarter commencement date. International applicants may need to apply much earlier for visa reasons.

Apply now