发表机构
University of Pennsylvania; Kyoto University（宾夕法尼亚大学; 京都大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出用VICReg自监督预训练Transformer编码器，用于对撞机中重共振质量回归，在2.5-6.5 TeV级联衰变下，相比监督模型重建峰更尖锐且对损坏更稳健。
AI 中文摘要
从衰变产物中重建带有缺失能量的重共振质量，是直接决定对撞机实验中新物理搜索灵敏度的核心任务之一。监督学习方法在此问题上常因各种系统不确定性和分布偏移而难以良好泛化。穷尽标记数据中的所有可能变化可能非常计算密集，而模型泛化失败会破坏重建的共振宽度，这对峰值搜寻分析至关重要。在本工作中，遵循基础模型范式，我们采用自监督方法，使用VICReg预训练一个Transformer编码器，以学习对各种损坏不变的嵌入，然后针对质量在2.5至6.5 TeV范围内的重共振以及类似SUSY的级联衰变至十一体末态进行微调以进行质量回归。我们表明，与相同架构在相同增强数据上从头训练的监督模型相比，预训练模型重建出更尖锐的共振峰，并在各种实际损坏下具有更稳定的性能。
英文摘要
Reconstructing the mass of a heavy resonance from its decay products with missing energy is one of the central tasks that directly determine the sensitivity in new physics searches at collider experiments. Supervised learning approaches to this problem often struggle to generalize well due to the presence of various systematic uncertainties and distribution shifts. Exhausting all possible variations in the labeled data can be very compute-intensive, while a failure of the model to generalize can corrupt the reconstructed resonance widths that are critical in peak-hunting analyses. In this work, following the foundation model paradigm, we use a self-supervised approach to pre-train a transformer encoder with VICReg to learn an embedding invariant to various corruptions, then fine-tune it for mass regression on a heavy resonance with masses ranging from 2.5 to 6.5 TeV and a SUSY-like cascade decay into an eleven-body final state. We show that the pre-trained model reconstructs sharper resonance peaks and has a more stable performance under various realistic corruptions, compared to a supervised model of the same architecture trained on the same augmented data from scratch.
Comments7 pages
