无需标注数据，直接从粒子云学出高能物理喷注特征
JetParticle-JEPA: An Efficient Self-Supervised Representation Learning method for Jet Tagging in High-Energy Physics
- 用自监督学习直接处理连续粒子流，跳过传统分词和重建
- 在少量标签下性能超越监督模型，且对探测器误差更鲁棒
- 适合缺乏标注数据的高能物理研究，尤其适合未来大型强子对撞机
大型强子对撞机中的喷注分类越来越多地依赖于在大规模模拟数据上训练的深度学习模型，带来高昂计算成本并难以应对探测器建模偏差。我们提出JetParticle-JEPA（JP-JEPA），一种基于粒子变换器骨干网络的自监督联合嵌入预测架构，可直接从连续粒子云中学习具有物理意义的喷注表征，无需对输入进行分词或重建。该方法通过预测被掩码粒子的潜在表示，同时保留精细的运动学关联。在JetClass基准上，JP-JEPA在全数据集上达到与顶尖监督模型相当的性能，在低标签场景下优于监督基线，并显著超越现有自监督方法；在顶夸克和夸克-胶子分类任务中，其表现也与监督方法持平。所学表征对缺失探测器信息具有强鲁棒性，不确定性行为更优，表明其是实现高效、鲁棒喷注物理分析的有前景的基础模型框架。
原文摘要 · Abstract (English)
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 inputs. Built on a Particle Transformer backbone, JP-JEPA predicts latent representations of masked particles while preserving fine-grained kinematic correlations. On the JetClass benchmark, JP-JEPA achieves performance comparable to fully supervised state-of-the-art methods on the full dataset, surpasses supervised baselines in low-label regimes, and significantly outperforms existing SSL approaches. On Top Quark and Quark-Gluon Tagging benchmarks, it remains on par with supervised methods. The learned representations also exhibit strong robustness to missing detector information and improved uncertainty behavior, highlighting JP-JEPA as a promising foundation-model framework for robust and data-efficient jet physics at the LHC.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。