“变革”LHCb：重味衰变的自监督映射
"Transforming" LHCb: self-supervised maps of heavy-flavour decays
- Brown University（布朗大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出自监督Transformer架构，通过掩蔽粒子识别和喷注补全学习重味衰变映射，在LHCb数据上实现约10%标记能力，提升异常衰变发现潜力。
AI中文摘要：
美和粲强子的衰变为超越标准模型的物理提供了灵敏的探针，包括具有不可见粒子的衰变，其中部分末态不留下任何可重建的探测器信号。LHCb实验在CERN LHC上记录的大量重味数据样本，加上其精确的径迹、位移顶点重建和粒子识别能力，使其特别适合直接从数据中学习重建的重强子衰变环境映射。我们提议将ATLAS和CMS在喷注味标记方面的最新进展引入LHCb，以显著改进当前LHCb标记器的性能，并将其扩展到末态具有多个不可见粒子的重味衰变重建。为此，我们引入了一种自监督的Transformer架构，该架构通过推断被掩蔽的粒子识别信息并完成被移除成分的喷注，在无味标签或独占衰变标签的情况下学习衰变映射。在模拟LHCb开放数据的五个分类任务中，自监督模型优于具有随机权重的相同Transformer，并与完全监督的Transformer表现相当。我们实现了约10%的标记能力。此外，从重建的独占衰变中移除成分也系统地增加了模型的异常分数，相对于从相同重强子中随机移除的情况。我们直接在2017年LHCb质子-质子碰撞开放数据中确认了这一行为：所有八个研究的重味通道的分数均增加，信号区域响应超过相邻边带。这些研究提供了一个原理证明，即通过喷注映射重味衰变环境可以变革LHCb中的味标记，并扩展对不完整或其他异常衰变的发现潜力。
英文摘要：
Decays of beauty and charm hadrons provide sensitive probes of physics beyond the standard model, including decays with invisible particles, in which part of the final state leaves no reconstructed detector signature. The large heavy-flavour data samples recorded by the LHCb experiment at the CERN LHC, together with its precise tracking, displaced vertex reconstruction, and particle identification, make it particularly well suited to learning a map of reconstructed heavy-hadron decay environments directly from data. We propose to bring recent advances in jet flavour tagging at ATLAS and CMS to significantly improve on the performance of the current LHCb taggers and extend them to the reconstruction of heavy-flavour decays with several invisible particles in the final state. To achieve this, we introduce a self-supervised transformer architecture that learns the decay maps without flavour or exclusive-decay labels by inferring masked particle identification information and completing jets from which constituents have been removed. Across five classification tasks in simulated LHCb Open Data, the self-supervised model outperforms an otherwise identical transformer with random weights, and performs comparably to a fully supervised transformer. We achieve a tagging power of about 10\%. In addition, removing constituents from reconstructed exclusive decays also systematically increases the model anomaly score relative to random removals from the same heavy hadrons. We confirm this behaviour directly in 2017 LHCb proton-proton collision Open Data: the score increases for all eight studied heavy-flavour channels, and the signal region response exceeds that in the adjacent sidebands. These studies provide a proof of principle that mapping heavy-flavour decay environments through jets can transform flavour tagging in LHCb and extend the discovery reach for incomplete or otherwise unusual decays.
