English(EN) KIGNet: Physics-Motivated Multi-Graph Representation Learning for Explainable Jet Tagging

KIGNet：面向可解释喷注识别的物理驱动图学习

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-25 04:00

研究人员开发了KIGNet，一种用于高能物理中可解释喷注识别的新图神经网络。KIGNet将角向分离、相对横向动量、动量分数和不变质量平方等运动学变量整合到其分类过程中。该模型表明它学习到了物理上可解释的表示，其中角向分离和相对横向动量是分类中最主要的因素，这与QCD辐射的理论预测一致。KIGNet在基准数据集上取得了最先进的性能，与现有方法相比，在准确性和表示质量方面都有显著提高。 AI

影响增强了物理学AI模型的可解释性，有望提高科学发现和模型的可信度。

排序理由该集群包含一篇详细介绍新模型及其在科学基准测试中性能的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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arXiv cs.LG TIER_1 English(EN) · Md Raqibul Islam, Adrita Khan, Mir Sazzat Hossain, Choudhury Ben Yamin Siddiqui, Md. Zakir Hossan, Tanjib Khan, M. Arshad Momen, Amin Ahsan Ali, AKM Mahbubur Rahman · 2026-06-25 04:00

KIGNet：用于可解释射流标记的物理驱动多图表示学习

arXiv:2512.07420v3 Announce Type: replace-cross Abstract: Jet identification plays a central role in analyzing data from high-energy collider experiments. While deep learning has improved jet classification, it often lacks interpretability. We introduce the Kinematic Interaction …

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KIGNet：用于可解释射流标记的物理驱动多图表示学习

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