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Physics in the AI era

24–27 Sept 2024
University of Pisa
Europe/Rome timezone

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Session

Particle Physics

26 Sept 2024, 09:00
Aula Gerace, building C, second floor (Dipartimento di Fisica, Università di Pisa)

Aula Gerace, building C, second floor

Dipartimento di Fisica, Università di Pisa

Conveners

Particle Physics

  • Andrea Wulzer (ICREA and IFAE, Barcellona)
  • Tilman Plehn (Heidelberg University)

Particle Physics

  • Stefano Forte (Milan University)
  • Sam Bright-Thonney (Cornell Univeristy)

Particle Physics

  • Bryce Fore (Argonne National Lab)
  • Claudius Krause
  • Filippo Cattafesta (Pisa University and INFN Pisa)

Presentation materials

There are no materials yet.

  1. 24. Statistical Learning for Statistical Inference
    Prof. Andrea Wulzer (ICREA and IFAE, Barcellona)
    26/09/2024, 09:00
  2. 45. Modern ML in LHC Physics
    Prof. Tilman Plehn (Heidelberg University)
    26/09/2024, 09:40
  3. 55. Machine Learning and precision high energy physics
    Stefano Forte (Milan University)
    26/09/2024, 10:50
  4. 56. Towards Robust AI in HEP
    Samuel Bright-Thonney (IAIFI Cambridge)
    26/09/2024, 11:30
  5. 46. Generative Models in Particle Physics
    Claudius Krause
    27/09/2024, 14:00

    Simulation is the crucial connection between particle physics theory and experiment. Our ability to simulate particle collision based on first principles allows us to analyze and understand the vast amount of data of the Large Hadron Collider (LHC) experiments. This, however, comes at a cost: A lot of computational resources are needed to simulate all necessary interactions to the required...

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  6. 6. Modeling Low-Density Nuclear Matter with Neural-Network Quantum States
    Bryce Fore (Argonne National Lab)
    27/09/2024, 14:40
    Poster + application for talk session

    The structure of low-nuclear density nuclear matter is of great importance to the physics of neutron star crusts. One of the important aspects of this structure is the transition from roughly spherical neutron rich nuclei to uniform matter. Models for both of these extremes exist but the transition is less easily understood. In this presentation I will discuss my results from variational Monte...

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  7. 20. CMS FlashSim: end-to-end simulation with ML
    Filippo Cattafesta (Scuola Normale Superiore & INFN Pisa)
    27/09/2024, 15:20
    Poster + application for talk session

    Detailed event simulation at the LHC is taking a large fraction of computing budget. CMS developed an end-to-end ML based simulation that can speed up the time for production of analysis samples of several orders of magnitude with a limited loss of accuracy. As the CMS experiment is adopting a common analysis level format, the NANOAOD, for a larger number of analyses, such an event...

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