66 episodes

Members of the Rudolf Peierls Centre for Theoretical Physics host a morning of Theoretical Physics roughly three times a year on a Saturday morning. The mornings consist of three talks pitched to explain an area of our research to an audience familiar with physics at about the second-year undergraduate level and are open to all Oxford Alumni. Topics include Quantum Mechanics, Black Holes, Dark Matter, Plasma, Particle Accelerators and The Large Hadron Collider.

Theoretical Physics - From Outer Space to Plasma Oxford University

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Members of the Rudolf Peierls Centre for Theoretical Physics host a morning of Theoretical Physics roughly three times a year on a Saturday morning. The mornings consist of three talks pitched to explain an area of our research to an audience familiar with physics at about the second-year undergraduate level and are open to all Oxford Alumni. Topics include Quantum Mechanics, Black Holes, Dark Matter, Plasma, Particle Accelerators and The Large Hadron Collider.

    • video
    Machine learning techniques in modern quantum-mechanics experiments

    Machine learning techniques in modern quantum-mechanics experiments

    In this talk, Dr Elliott Bentine shall discuss how recent experiments have exploited machine-learning techniques, both to optimize the operation of these devices and to interperet the data they produce. Modern table-top experiments can engineer physical systems that are deeply into the quantum mechanical regime. These cutting-edge instruments provide new insights into fundamental physics, and a pathway to future devices that will harness the power of quantum mechanics. They typically require complex operations to prepare and control the quantum state, involving time-dependent sequences of magnetic, electric and laser fields. This presents experimental physicists with an overwhelming number of tunable parameters, which may be subject to uncertainty or fluctuations.

    • 37 min
    • video
    Machine Learning and String Theory

    Machine Learning and String Theory

    Professor Andre Lukas will discuss how string theorists have started to use methods from data science - particularly machine learning - to analyse the vast landscape of string data.

    • 52 min
    • video
    An Introduction to deep learning

    An Introduction to deep learning

    Professor Ard Louis gives a basic introduction to deep learning for physicists and addresses a few questions such as: Is the hype around deep learning justified, or are we about to hit some fundamental limitations? In less than ten years, machine learning techniques based on deep neural networks have moved from relative obscurity to central stage in the AI industry. Large firms such as Google and Facebook are pouring billions into research and development of these new technologies. The use of deep learning in physics is also growing exponentially. Can physics help us understand why deep learning works so well? And conversely: How can deep learning provide new insight into the world around us?

    • 52 min
    • video
    Welcome by Ian Shipsey Head of the Department of Physics

    Welcome by Ian Shipsey Head of the Department of Physics

    Ian Shipsey give an update on the department and introduces the next three talk on 'AI in Physics'.

    • 6 min
    • video
    Cosmic acceleration revealed by Type la supernovae?

    Cosmic acceleration revealed by Type la supernovae?

    In this talk Subir Sarkar will explain how deflagration supernovae have been used to infer that the Hubble expansion rate is accelerating, and critically assess whether the acceleration is real and due to `dark energy’.

    • 40 min
    • video
    Supernova Explosions and their Role in the Universe

    Supernova Explosions and their Role in the Universe

    In this talk, Philipp Podsiadlowski will explain how this energy (sometimes) creates a visible fireball, before going on to explain the role of supernovae in the production of the heaviest elements in the periodic table.

    • 48 min

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