对比多种表示学习方法在粒子物理中的表现，提供可复现的基准。
An Evaluation of Representation Learning Methods in Particle Physics Foundation Models
- 统一框架下比较对比学习、掩码建模和生成重建方法。
- 引入改进架构，在喷注分类任务上达到当前最优性能。
- 为粒子物理基础模型发展提供透明可复现的参考基准。
我们提出一个系统性评估框架，用于粒子物理中的表示学习目标。研究采用共享的基于Transformer的粒子云编码器，结合标准化预处理、匹配采样和一致的评估协议，在喷注分类数据集上对比监督与自监督对比学习、掩码粒子建模以及生成重构目标。此外，我们引入针对性的监督架构改进，在基准评测中实现最先进的性能。该受控对比实验分离了学习目标的贡献，揭示其各自优劣，并提供可复现的基线。本工作定位为粒子物理基础模型发展的参考点，推动领域内更透明、更稳健的进步。
原文摘要 · Abstract (English)
We present a systematic evaluation of representation learning objectives for particle physics within a unified framework. Our study employs a shared transformer-based particle-cloud encoder with standardized preprocessing, matched sampling, and a consistent evaluation protocol on a jet classification dataset. We compare contrastive (supervised and self-supervised), masked particle modeling, and generative reconstruction objectives under a common training regimen. In addition, we introduce targeted supervised architectural modifications that achieve state-of-the-art performance on benchmark evaluations. This controlled comparison isolates the contributions of the learning objective, highlights their respective strengths and limitations, and provides reproducible baselines. We position this work as a reference point for the future development of foundation models in particle physics, enabling more transparent and robust progress across the community.
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