About the project
This project tackles one of the biggest questions in astrophysics: the nature of dark energy. Using new datasets from the Rubin Observatory鈥檚 LSST and the TiDES survey, you will analyse tens of thousands of supernovae, develop expertise in data analysis and machine learning, and work at the forefront of international astrophysics.
Type Ia supernovae, thermonuclear explosions of white dwarf stars, are astronomers鈥 best tool to measure distances in the universe and trace its expansion across cosmic time. They were central to the discovery of cosmic acceleration and dark energy. New evidence now hints that dark energy may evolve, challenging Einstein鈥檚 cosmological constant, the leading theory for its nature.
Testing this possibility is a key goal of two next-generation surveys:
- the 鈥檚 Legacy Survey of Space and Time (LSST)
- the Time Domain Extragalactic Survey (TiDES) on
Both have recently achieved 鈥渇irst light鈥 and will deliver tens of thousands of distant supernovae, samples 50 times larger than those available today. The University of Southampton plays a leading role in these collaborations.
This project provides immediate access to the incoming data, placing you at the forefront as the first supernova discoveries are made. Working within an outstanding national and international team, you will investigate fundamental unknowns in supernova physics: their explosion mechanisms, progenitor systems, and links to the stars and dust in their host galaxies.
By disentangling how these factors affect supernova luminosities, and thus inferred distances, the project will enable a new, state-of-the-art measurement of dark energy. You will be among the first worldwide to explore these datasets. You will gain advanced skills in survey data analysis, statistical modelling, machine learning and AI, alongside experience in international teamwork and scientific communication - preparing you for careers in astrophysics, data science, and technology-driven industries.
As well as receiving core scientific training, you will:
- develop advanced skills in statistical modelling, Bayesian inference, and data visualisation
- gain experience with machine learning and AI methods applied to large datasets
- build expertise in high-performance computing, programming (Python), and version control
- have opportunities to present results through scientific papers, conferences, and outreach activities
- strengthen transferable skills in teamwork, project management, and communication
