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Learning Symmetry-Independent Jet Representations via Jet-Based Joint Embedding Predictive Architecture

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arxiv 2412.05333 v1 pith:G4NZ7EAS submitted 2024-12-05 hep-ph cs.LGhep-exphysics.data-an

Learning Symmetry-Independent Jet Representations via Jet-Based Joint Embedding Predictive Architecture

Subash Katel , Haoyang Li , Zihan Zhao , Raghav Kansal , Farouk Mokhtar , Javier Duarte This is my paper
classification hep-ph cs.LGhep-exphysics.data-an
keywords learningrepresentationshand-craftedj-jepataskswithoutarchitectureaugmentation
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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In high energy physics, self-supervised learning (SSL) methods have the potential to aid in the creation of machine learning models without the need for labeled datasets for a variety of tasks, including those related to jets -- narrow sprays of particles produced by quarks and gluons in high energy particle collisions. This study introduces an approach to learning jet representations without hand-crafted augmentations using a jet-based joint embedding predictive architecture (J-JEPA), which aims to predict various physical targets from an informative context. As our method does not require hand-crafted augmentation like other common SSL techniques, J-JEPA avoids introducing biases that could harm downstream tasks. Since different tasks generally require invariance under different augmentations, this training without hand-crafted augmentation enables versatile applications, offering a pathway toward a cross-task foundation model. We finetune the representations learned by J-JEPA for jet tagging and benchmark them against task-specific representations.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. AeroJEPA: Learning Semantic Latent Representations for Scalable 3D Aerodynamic Field Modeling

    cs.LG 2026-05 unverdicted novelty 6.0

    AeroJEPA applies joint-embedding predictive learning to produce scalable, semantically organized latent representations for 3D aerodynamic fields that support both field reconstruction and downstream design tasks.

  2. Enhancing next token prediction based pre-training for jet foundation models

    hep-ph 2025-12 conditional novelty 6.0

    Using continuous particle features as input and combining next-token with masked-token pre-training markedly improves classification accuracy of the OmniJet jet foundation model without visibly hurting its generative quality.

  3. Pretrained Event Classification Model for High Energy Physics Analysis

    hep-ph 2024-12 unverdicted novelty 6.0

    A GNN pretrained on 120M simulated HEP events generalizes to unseen processes and ATLAS data; fine-tuning boosts accuracy especially with small datasets, with CKA showing preserved encoders but altered intermediate layers.

  4. Discovering the Gell-Mann-Okubo Formula with Kolmogorov-Arnold Networks

    hep-ph 2026-01 reject novelty 3.0

    A KAN network's fitted polynomials are hand-rearranged into the known Gell-Mann-Okubo mass relations, so the claimed autonomous rediscovery is not demonstrated.