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English(EN) Combinatorial Residual Network: a decay-chain-aware classifier without explicit jet assignment

研究人员开发出用于粒子物理学的新型组合残差网络分类器

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发出一种名为组合残差网络（CoReNet）的新型分类器，旨在处理粒子物理学中的复杂组合问题，特别是识别重粒子的衰变链。该网络架构编码了假定的衰变层级，使其能够使用共享权重评估所有喷注分配假设，并使用学习到的权重将它们组合起来。CoReNet 在模拟的希格斯玻色子对产生和 $HH o bar{b}W^+W^-$ 事件上表现出强劲的性能，优于更大的基于 Transformer 的网络，同时所需的参数和训练时间显著减少。 AI

影响 为物理学中的复杂组合问题引入了一种新颖的神经网络架构，有望提高粒子识别的效率和准确性。

排序理由 该集群包含一篇详细介绍粒子物理学新模型架构的研究论文。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv cs.LG 阅读 →

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研究人员开发出用于粒子物理学的新型组合残差网络分类器

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该集群包含一篇详细介绍粒子物理学新模型架构的研究论文。[lever_c_demoted from research: ic=1 ai=0.7]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · John Alison, Patrick Bryant, Alejandro Gomez Espinosa, Christian Guinto-Brody, Aniket Khanal, Chuyuan Liu, Sindhu Murthy, Guanglei Zhao ·

    Combinatorial Residual Network: a decay-chain-aware classifier without explicit jet assignment

    arXiv:2610.40749v1 Announce Type: cross Abstract: Final states with several jets from the decays of heavy particles pose a combinatorial problem, since the jets must be assigned, at least implicitly, to their parent particles. We describe the Combinatorial Residual Network (CoReN…

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