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Graph Neural Networks at LHC

Graph Neural Networks are used for many important physics tasks at the LHC. This requires the machine learning and high energy physics communities to work together.

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  • In 1973, Philip Anderson published a paper introducing the resonating valence bond state, which can be recognized in retrospect as a topologically ordered phase of matter — one that cannot be classified in the conventional way according to its patterns of spontaneously broken symmetry. Steven Kivelson and Shivaji Sondhi reflect on the impact of this paper over the past 50 years.

    • Steven Kivelson
    • Shivaji Sondhi
    Comment

  • 75 years ago Richard Feynman developed a new approach to non-relativistic quantum mechanics: the path integral formulation.

    • Alison Wright
    Research Highlight

  • Dmitry Krotov discusses recent theoretical advances in Hopfield networks and their broader impact in the context of energy-based neural architectures.

    • Dmitry Krotov
    Comment

  • One hundred years ago, Louis de Broglie posed a question: could matter particles behave like waves? This duality was already known for light; extending it to electrons and indeed all matter had huge implications, especially for the development of quantum mechanics.

    • Alison Wright
    Feature

  • A paper in Physical Review Letters measures the spatial confinement of chiral edge modes and presents a model to show how it varies depending on disorder.

    • Ankita Anirban
    Research Highlight

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