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

arXiv: https://arxiv.org/abs/2412.05333 · HTML: https://mlsanity.com/paper/2412.05333/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Dec 2024.

## Built on by (most cited first, 12 of 12)

- [Foundation models for high-energy physics](https://mlsanity.com/paper/2509.21434/) (2025)
- [Fine-tuning machine-learned particle-flow reconstruction for new detector geometries in future colliders](https://mlsanity.com/paper/2503.00131/) (2025)
- [Pretrained Event Classification Model for High Energy Physics Analysis](https://mlsanity.com/paper/2412.10665/) (2024)
- [RINO: Renormalization Group Invariance with No Labels](https://mlsanity.com/paper/2509.07486/) (2025)
- [AeroJEPA: Learning Semantic Latent Representations for Scalable 3D Aerodynamic Field Modeling](https://mlsanity.com/paper/2605.05586/) (2026)
- [Enhancing next token prediction based pre-training for jet foundation models](https://mlsanity.com/paper/2512.04149/) (2025)
- [JetParticle-JEPA: An Efficient Self-Supervised Representation Learning method for Jet Tagging in High-Energy Physics](https://mlsanity.com/paper/2606.14813/) (2026)
- [jBOT: Semantic Jet Representation Clustering Emerges from Self-Distillation](https://mlsanity.com/paper/2601.11719/) (2026)
- [Towards Engineering Scaling Laws with Pretraining Data Composition](https://mlsanity.com/paper/2606.19781/) (2026)
- [Prompting Particle Physics: Tokenized Multi-modal Foundation Models for Combinatorially Many Tasks](https://mlsanity.com/paper/2609.31862/) (2026)
- [Comparison of Image Processing Models in Quark Gluon Jet Classification](https://mlsanity.com/paper/2602.00141/) (2026)
- [RINO: Renormalization Group Distillation with No Labels](https://mlsanity.com/paper/2610.15919/) (2026)

## Abstract (arXiv)

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.