Supernovae: theoretical modelling of light curves and spectra

Background

Supernovae are the explosive deaths of stars, occurring at the end of stellar evolution for very massive stars and certain types of binary star system. They are important to a number of modern astrophysical topics: most of the heavy elements are synthesised in supernova explosions, supernovae inject energy and momentum to the interstellar medium and some classes of supernovae can be used as accurate cosmic distance indicators, making it possible to map out the expansion history of the Universe.

The current generation of astronomical surveys are yielding observational data of unprecedented quantity and quality. Thanks to these, we have uncovered a startling array of new and unexpected phenomena in astronomical explosions. These new discoveries can test and challenge our theoretical picture and drive the development and exploration of supernova theory.

The project

Interpreting high-quality observational data requires theoretical modelling. The PhD project will focus on performing radiative transfer simulations to compute synthetic spectra and light curves for thermonuclear supernovae and comparing these to observational data. A particular focus of our groups' current work is the incorporation of improved atomic physics in currently state-of-the-art simulations in order to achieve the best possible scope for quantitative interpretation of observational data.

A student joining our group will learn the physical principles and computational algorithms used in Monte Carlo radiative transfer calculations and will become experienced in running and analysing the results of simulations. Depending on the student's skills/interests, particular projects can focus on a combination/balance of improving the quality of the simulations and modelling of observations. This work will involve participation in international collaborations with theorists (particularly those specialising in thermonuclear supernova explosions) and supernova observers.

More information:

Supervisor: Prof. Stuart Sim (s.sim@qub.ac.uk)