Jacob Tutt

PhD thesis: TBC

My PhD focuses on the detection of cosmology’s `needle in a haystack’, an ancient signal buried beneath noise five orders of magnitude stronger than itself. As a tracer of neutral hydrogen during the very early Universe, the redshifted 21 cm line promises an unprecedented view of the Cosmic Dark Ages, Cosmic Dawn, and Epoch of Reionisation. It offers unique insights into the earliest phases of large-scale structure formation, the properties of the first galaxies, and the nature of dark matter. Radio cosmology is driving a revolution in big data, with the Square Kilometre Array forecasted to generate over 700 PB of data annually. My research looks at leveraging machine learning and high-performance computing to accelerate Bayesian inference and solve inverse problems. These approaches aim to better handle growing data volumes, increasing model complexities, and high-dimensional parameter spaces, with applications extending across astronomy, from broader cosmology to gravitational-wave astrophysics.

Who or what inspired you to pursue your research interests?

The vast influx of data from current and upcoming observatories, such as the SKA, Rubin-LSST, JWST and LISA, coupled with exponential growth in compute and advances in machine learning places us on the cusp of one of the most exciting periods of discovery in astrophysics. I feel very fortunate to be beginning a PhD in the dawn of this era and to have been inspired by some exceptional supervisors throughout my undergraduate and master’s studies.