Mentorship

Discover a New Exoplanet

The first exoplanet — a planet that orbits a star beyond our own solar system — was discovered less than 40 years ago. One of the easiest ways to detect exoplanets is by searching for small, periodic drops in the amount of light that we receive from stars: telltale signs that a planet is passing in front of the star. Since then, transit surveys that can continuously observe large sections of the sky have got the detection of such signals down to a fine art. Since its launch in 2018, the Transiting Exoplanet Survey Satellite (TESS) has detected almost 10,000 such candidate exoplanets. However, to validate the existence of an exoplanet, one must also obtain and analyse ground-based follow-up observations to confirm the candidate is a genuine exoplanet, rather than an eclipsing binary star system. Due to the relatively small size of the exoplanet research community, this due diligence has been performed for fewer than half of the candidate exoplanets.

In a research course created and supervised by myself in 14 hour-long sessions, I will provide students with the theoretical knowledge to discover and validate a new TESS exoplanet and assist them in applying it to real TESS data. The key stages of the research they will undertake are:

1. Writing a code to identify periodic transit-like signals in light curves, and applying it to a range of stars that TESS has observed to identify candidate exoplanets
2. Analysing ground-based follow-up observations to check whether the candidate is genuinely an exoplanet
3. Applying sophisticated statistical modelling to the available data to accurately measure a range of physical properties of the exoplanet and its host star — such as the size of the planet and how close it is to its host star — along with their uncertainties

Skills

The skills that the student will develop throughout the course include:

• An intimate knowledge of the theory underpinning transiting exoplanets and observational astronomy, delivered by a world-leading expert in the subject matter
• Literacy in statistics and the concept of uncertainty: probably the biggest change in mindset between learning theory and performing and communicating research
• Practical experience of writing code and applying it to large datasets to undertake research. Throughout the course we will be using the Python programming language, which is used abundantly in astrophysics research
• A genuine taste of what it feels like to do real scientific research — including the satisfaction of uncovering something that nobody has measured before

Platform

Google Colab is a free, browser-based platform that allows you to write and run Python code without installing anything on your computer. I use it to deliver all aspects of the course — theory is presented through interactive notebooks containing text, equations, and figures, many of which the student can manipulate directly to explore the underlying concepts. The same environment is then used for the research exercises themselves, where I provide scaffolding code to handle routine tasks, allowing the student to focus their efforts on the parts of the analysis where the real science happens.

Some Python proficiency is a firm prerequisite for the course. Students who arrive without a working knowledge of the language will find it very difficult to keep pace with the material. That said, detailed coding expertise is not required — the course is designed to develop your abilities progressively, and I will provide guidance throughout.

A small number of places are available each year. The programme is delivered online, making it accessible to students worldwide. Fees are available on request. To enquire about availability or to find out whether the programme is suitable for your student, please use the contact form.