Self-supervised learning strategies for jet physics

P. Rieck

doi:10.1088/2632-2153/ae1100

2025-10-24

DOI：10.1088/2632-2153/ae1100

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摘要

En 中文

我们通过利用标准模拟链的马尔可夫属性，将基于重模拟的自监督学习（re-simulation-based self-supervised learning）方法扩展到学习对撞机中强子喷注（hadronic jets）的表征。与掩码、裁剪或其他数据增强形式不同，该方法模拟事件对，其中模拟的初始部分共享，但后续模拟阶段独立演化。当与对比损失函数（contrastive loss function）结合时，这自然会导致表征捕捉到模拟初始阶段的物理信息。具体而言，我们强制硬散射（hard scattering）和部分子级联（parton shower）共享，并让强子化（hadronization）以及与探测器的相互作用独立演化。随后，我们评估这些表征在下游任务中的效用。

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machine learning: science and technology

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university of wisconsin-madison

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