The Square Kilometre Array (SKA) radio telescope will begin scanning the sky in 2028, and this team is building the AI and simulators needed to decode what it finds inside neutron stars—the ultra-dense, spinning corpses of exploded stars. For 50 years, physicists have been unable to answer a basic question: what is the stuff inside a neutron star actually made of? The problem is that these objects are cosmic-scale laboratories where matter is crushed to densities far beyond anything achievable on Earth, and the only way to probe their interiors is by watching how they rotate. SKA will detect five times more neutron stars than we currently know, but the data will be so vast and complex that no existing mathematical framework can connect the raw signals to the hidden physics. This project fills that gap by building a simulator that predicts how neutron stars spin, then using machine learning to match those predictions against real observations. If successful, this is fundamental science with no immediate practical application—but it will reveal how matter behaves at the most extreme densities in the universe, a question that has driven theoretical physics for decades. Past work on such extreme states has unexpectedly fed into nuclear energy models and quantum computing; deeper knowledge here could eventually reshape our understanding of matter itself.
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After three decades of planning, the Square Kilometre Array (SKA), the most ambitious and sensitive radio telescope, finally began construction in 2022. From 2028 onwards, SKA will provide a wealth of new information about the radio Sky with unprecedented detail. A key global science driver is the study of ultra-compact neutron stars, stellar remnants that form when massive stars explode. Combining intense gravity, fast rotation, ultra-high densities, and strong magnetic fields, neutron stars are the ultimate cosmic laboratories to study matter under extreme conditions that we cannot recreate on Earth. Using the SKA, I will address the question that scientists have puzzled over for more than 50 years: What are neutron-star interiors made of, and how do they behave? SKA's unparalleled sensitivity and the fact that it will detect five times more neutron stars at radio frequencies than currently known plays a revolutionary role in this endeavour. However, the considerable increase in data quality and scale poses serious challenges. Each signal needs to be carefully studied to extract information about the matter hidden inside the interiors of ultra-dense stars. For this decoding to be successful, we require accurate mathematical frameworks that consistently incorporate physics from the smallest quantum scales to the city-sized extent of the entire star. Moreover, we need analysis methods that effectively and efficiently contrast theory and data. Neither of these crucial components currently exists, hindering our ability to uncover physics in extreme environments. During this fellowship, I will address these urgent needs by leveraging my interdisciplinary expertise to build a multi-skilled team that transcends traditional discipline boundaries and unites astronomers, physicists, Artificial Intelligence (AI) experts and software engineers. Tackling this work now, while SKA is under construction, is vital so that my team is ready to analyse the telescope's much-awaited data as soon as they become available and drive scientific returns into this global £1.8 billion investment as early as possible. My first objective is to develop a novel state-of-the-art simulator that rigorously predicts the rotational evolution of neutron stars, which we monitor with radio telescopes such as SKA, to gain unique access to the hidden interior. To this end, my team will follow a multi-pronged approach that will incorporate new knowledge of superfluids (extraordinary fluids in the interiors of ultra-compact stars that flow without friction) and limitations to how we observe neutron stars for the first time. Next, I will connect these predictions to observations by utilising recent machine learning (ML) advances. These algorithms learn from examples without being explicitly told how to do so, improve automatically and significantly accelerate scientific discovery in those areas where large amounts of data can no longer be analysed by hand. In short, they are the perfect tool for my team to contrast observations of ultra-compact stars with theoretical models to determine the physics of matter under extreme conditions. Finally, my fellowship aims to grow the representation of women and minorities in the software-development workforce. To achieve this goal, my team and I will close a critical gap in the training landscape for Early Career Researchers by designing and delivering new astronomy-based workshops that centre on inclusivity, programming and ML. To make this course freely available online and distribute it worldwide, I will work with the UK's leading body for research software, the Software Sustainability Institute. In conclusion, as a Future Leaders Fellow, I will open new pathways to interdisciplinary research and drive a breakthrough in how we study ultra-dense stars while setting new standards for inclusive leadership.
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