S'authentifier

IN2P3/IRFU Machine Learning workshop 2025

26–28 nov. 2025
LPC Caen and GANIL
Fuseau horaire Europe/Paris

Organisers

Session

Transformers and Attention-Based Models

transformers
28 nov. 2025, 12:00
G. Iltis (LPC) and Seminar room maison d'hôtes (GANIL) (LPC Caen and GANIL)

G. Iltis (LPC) and Seminar room maison d'hôtes (GANIL)

LPC Caen and GANIL

6, Bd Marechal Juin 14050 Caen

Présidents de session

Transformers and Attention-Based Models

  • Bertrand Laforge (LPNHE Paris, Sorbonne Université)

Documents de présentation

Aucun document.

  1. 146. Jet Classification with Particle Transformers: A Multiclass Learning Approach
    Andrés Duque (Laboratoire de Physique de Clermont Auvergne)
    28/11/2025 12:00
    Object detection and reconstruction

    In high-energy collisions, jets, which are collimated sprays of particles, can originate from various fundamental particles, including W and Z bosons, top quarks, and the Higgs boson. Accurately identifying these jets is crucial for studying Standard Model processes and investigating new physics beyond its framework. This study, conducted within the ATLAS collaboration at the Large Hadron...

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  2. 157. Enhancing low energy reconstruction and classification in KM3NeT/ORCA with transformers
    Ivan Mozun
    28/11/2025 12:20
    Analysis : event classification, statistical analysis and inference, anomaly detection

    KM3NeT is a new research infrastructure housing the next generation of neutrino telescopes in the Mediterranean deep sea. This facility comprises two detectors: KM3NeT/ARCA and KM3NeT/ORCA, consisting of vertically-arranged detection units, 230 and 115, respectively, each equipped with 18 digital optical modules. The photomultipliers within each optical module detect Cherenkov light emitted by...

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  3. 145. JetParticle-JEPA: Self-Supervised Representation Learning for Jet Tagging in High-Energy Physics
    Guillaume Letellier (Université de Caen Normandie | GREYC)
    28/11/2025 12:40
    Analysis : event classification, statistical analysis and inference, anomaly detection

    The Large Hadron Collider (LHC) is designed to probe the limits of the Standard Model and search for new phenomena. Machine Learning (ML) has become a powerful tool in this endeavor, particularly for jet tagging tasks. Large-scale datasets such as JetClass, which contains over 100 million simulated jet events, enable not only the training of supervised models but also the development of...

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