English(EN) Vision Transformers for End-to-End Quark-Gluon Jet Classification from Calorimeter Images

视觉Transformer在大型强子对撞机喷注分类方面优于CNN

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-19 04:00

研究人员探索了将视觉Transformer (ViTs) 应用于使用大型强子对撞机量热仪图像进行夸克-胶子喷注分类。他们的研究利用了模拟的CMS Open Data，构建了多通道喷注视图图像，以实现端到端学习方法。研究结果表明，基于ViT的模型，特别是像ViT+MaxViT和ViT+ConvNeXt这样的混合架构，在准确率、F1分数和ROC-AUC方面优于传统的卷积神经网络，因为它们能有效地捕捉喷注子结构内的长距离空间相关性。 AI

影响为将先进视觉模型应用于高能物理数据分析设定了新基准。

排序理由该项目是一篇研究论文，详细介绍了现有模型在科学问题上的新应用。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.CV TIER_1 English(EN) · Md Abrar Jahin, Shahriar Soudeep, Arian Rahman Aditta, M. F. Mridha, Nafiz Fahad, Md. Jakir Hossen · 2026-08-19 04:00

用于从量能器图像进行端到端夸克-胶子喷注分类的视觉Transformer

arXiv:2506.14934v2 Announce Type: replace Abstract: Distinguishing between quark- and gluon-initiated jets is a critical and challenging task in high-energy physics, pivotal for improving new physics searches and precision measurements at the Large Hadron Collider. While deep lea…

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用于从量能器图像进行端到端夸克-胶子喷注分类的视觉Transformer

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