无需人工数据增强，用联合嵌入架构学习通用喷注表示
Learning Symmetry-Independent Jet Representations via Jet-Based Joint Embedding Predictive Architecture
- 设计喷注级联合嵌入预测架构，直接从物理上下文预测目标
- 在喷注分类任务上优于特定任务的表示方法
- 避免人工增强带来的偏差，适合跨任务通用模型
在高能物理中，自监督学习（SSL）有望在无需标注数据的情况下支持多种任务，包括喷注相关任务——即夸克和胶子在高能粒子碰撞中产生的窄粒子喷流。本文提出一种基于喷注的联合嵌入预测架构（J-JEPA），通过不依赖人工构造的数据增强来学习喷注表示，旨在从信息性上下文中预测多种物理目标。由于该方法无需手工设计的增强手段，避免了可能损害下游任务的偏差。鉴于不同任务通常需要对不同变换保持不变性，这种无手动增强的训练方式使模型具备更强的泛化能力，为构建跨任务基础模型提供路径。我们对J-JEPA学习到的表示进行微调，并在喷注标签任务上与特定任务表示进行了基准对比。
原文摘要 · Abstract (English)
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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