My research is focuses on maximising our big picture understanding of the Universe through observations. Type Ia Supernovae, as standard candles, are excellent distance estimators. By discovering and observing these events in both the nearby and distant universe, I try to determine how the Universe has evolved in size and density.
The physical processes that cause these explosions, how to best measure distances from them, and how to use these measurements to best determine the expansion history of the Universe are my central research themes. To do this, I am a core member of multiple major experiments including the Dark Energy Survey (DES), the Zwicky Transient Facility (ZTF) and the Legacy Survey of Space and Time (LSST).
I am also interested in a wide range of cosmological phenomena including explosions of the most massive stars ('superluminous supernova') and Gravitational Wave transients.
Beyond physics, I am keen to adapt astronomical techniques for good. I have partnered with medical clinicans in dermatology to detect skin cancer and cardio-vascular medicine to determine the best course of treatment after a heart-attack.
Measuring the size and structure of the Universe with type Ia supernova
The Universe is currently undergoing a period of rapid accelerated expansion. This discovery, suggesting that 75% of the energy budget of the Universe is unexplained represents the biggest mystery in physics today. Type Ia supernova, as bright, highly homogenous, explosions, are excellent measures of distance. Visible to vast distances, these cosmic light-bulbs are ideal measures of how the size and content of the Universe has evolved over the last 10 billion years. This PhD project aims to expand the use of these events to probe new aspects of cosmology.
Specifically, the student will exploit data collected by the international Zwicky Transient Facility (ZTF) collaboration to maximise our understanding of type Ia supernova to produce a detailed 3D map of the nearby Universe. This project represents a leap forward in this field; more than ten thousand discoveries are now made each year, compared to several hundred collected in the last twenty. The student will will develop machine learning tools to separate type Ia supernovae from other variable sources, and search for statistical correlations that can improve the measured distance to each event. The student will work closely with a team of international researchers in France, Germany, Sweden, Ireland and the USA to measure the 3D distribution of matter which will improve our understanding of Dark Energy and General Relativity.
Lancaster University has a leading role in multiple state-of-the-art supernova experiments including DES, LSST, 4MOST, Euclid and JWST. As the PhD develops, the student will be encouraged to join and collaborate on projects based upon their own interests.
