English(EN) Hadronic Mono-Z Dark Matter Sensitivity with Flow Matching on CMS Open Data

利用流匹配在CMS开放数据上探索暗物质灵敏度

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-04 04:00

研究人员开发了一种利用CMS开放数据中的流匹配技术来研究暗物质的方法。该方法使用在选定事件上训练的条件流匹配连续归一化流来模拟背景。该方法在特定暗物质基准测试中达到了高达7.62σ的预期显著性，并且一项消融研究表明，额外射流的运动学对区分有显著贡献。 AI

影响这项研究展示了生成模型技术在高能物理学中的新颖应用，可能影响未来的数据分析方法。

排序理由该项目是一篇学术论文，详细介绍了一种用于粒子物理学研究的新方法。[lever_c_demoted from research: ic=1 ai=0.4]

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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arXiv cs.LG TIER_1 English(EN) · Hitesh Rasineni (VIT-AP University, Amaravati, India), Bhavishya Chebrolu (Mohan Babu University, Tirupati, India) · 2026-09-04 04:00

基于CMS开放数据的流匹配的强子单Z暗物质灵敏度

arXiv:2609.02923v1 Announce Type: cross Abstract: We present a projected sensitivity study for hadronic mono-$Z$ dark-matter production using CMS Run~2015D HTMHT open data corresponding to 2.256382381~\invfb, from which 1{,}439{,}523 events satisfy the hadronic mono-$Z$ selection…