JetParticle-JEPA: An Efficient Self-Supervised Representation Learning method for Jet Tagging in High-Energy Physics
📰 ArXiv cs.AI
Learn how JetParticle-JEPA enables efficient self-supervised representation learning for jet tagging in high-energy physics, reducing computational costs and improving robustness
Action Steps
- Implement JetParticle-JEPA using PyTorch or TensorFlow to learn jet representations from particle clouds
- Apply self-supervised learning techniques to reduce reliance on labeled datasets
- Configure the Joint-Embedding Predictive Architecture to optimize performance on jet tagging tasks
- Test JP-JEPA on simulated datasets to evaluate its robustness to detector mismodeling
- Compare the performance of JP-JEPA with existing deep learning models for jet tagging
Who Needs to Know This
Physicists and machine learning engineers working on high-energy physics projects can benefit from this method to improve jet tagging efficiency and accuracy
Key Insight
💡 Self-supervised learning can improve the efficiency and robustness of jet tagging in high-energy physics by reducing reliance on labeled datasets
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💡 Introducing JetParticle-JEPA: a self-supervised representation learning method for efficient jet tagging in high-energy physics! #AI #Physics
Full Article
Title: JetParticle-JEPA: An Efficient Self-Supervised Representation Learning method for Jet Tagging in High-Energy Physics
Abstract:
arXiv:2606.14813v1 Announce Type: cross Abstract: Jet tagging at the Large Hadron Collider increasingly relies on deep learning models trained on massive simulated datasets, leading to high computational costs and limited robustness to detector mismodeling. We introduce JetParticle-JEPA (JP-JEPA), a self-supervised Joint-Embedding Predictive Architecture that learns physically meaningful jet representations directly from continuous particle clouds without tokenization or reconstruction of raw in
Abstract:
arXiv:2606.14813v1 Announce Type: cross Abstract: Jet tagging at the Large Hadron Collider increasingly relies on deep learning models trained on massive simulated datasets, leading to high computational costs and limited robustness to detector mismodeling. We introduce JetParticle-JEPA (JP-JEPA), a self-supervised Joint-Embedding Predictive Architecture that learns physically meaningful jet representations directly from continuous particle clouds without tokenization or reconstruction of raw in
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