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UofG Nuclear Physics Consolidated Grant

In plain English

AI plain-English summary

Physicists are firing electron and photon beams at atomic nuclei to crack open the fundamental building blocks of matter—quarks and gluons—and see how they stick together to form protons, neutrons, and entire nuclei. This research addresses a core gap in fundamental physics: we know quarks and gluons exist, but we do not fully understand the force that binds them into hadrons (like protons) or how that force governs the behaviour of nuclear matter. The experiments aim to answer two linked questions: how do quarks and gluons assemble into the particles that make up every atom, and what is the true nature of the dense, strongly-interacting material inside a nucleus? This is pure fundamental science. There is no immediate practical application—no new battery, no medical device, no grid upgrade. That is honest. But historically, understanding how matter works at its most basic level has led to unexpected technologies: particle accelerators now treat cancer, and nuclear physics underpins medical imaging. A deeper grasp of the strong force could, over decades, open doors to new materials, more precise nuclear models for energy, or even novel computing architectures. For now, the goal is knowledge itself.

View original technical description
The overarching goal of our research programme is to address aspects of the broad science challenge: "What are the basic constituents of matter and how do they interact?". In particular, by performing experiments primarily with electron and photon beams, we study questions such as "How do quarks and gluons form hadrons?", and by studying these basic, strongly-interacting building blocks we are able to tackle the question "What is the nature of nuclear matter?"

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Researchers

Bjoern Seitz (Co-Investigator)Daria Sokhan (Co-Investigator)David Hamilton (Co-Investigator)David Ireland (Principal Investigator)Rachel Montgomery (Co-Investigator)

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Original classification

Research Grant

Plain English summaries and category classifications on this site are generated by AI and may not perfectly reflect the original research.