用机器学习重建海底中微子探测器的μ子簇，提升宇宙射线研究精度
Reconstruction of muon bundles in KM3NeT detectors using machine learning methods
- 采用机器学习模型处理探测器采集的μ子簇数据
- 可预测μ子数量、总能量及原初宇宙射线能量
- 适用于中微子天文与振荡研究，适合高能物理方向
KM3NeT合作组正在地中海海底安装ARCA和ORCA中微子探测器。ARCA侧重中微子天文学，而ORCA则优化用于中微子振荡研究。两个探测器目前已处于中间运行状态并持续收集数据，包括大气中宇宙射线相互作用产生的μ子。本文探讨了机器学习模型在μ子簇（多μ子事件）重建中的潜力，使用了中间配置的ARCA和ORCA实测数据，以及未来最终配置的模拟数据。研究实现了对μ子簇中μ子总数、总能量，甚至原初宇宙射线能量的预测。
原文摘要 · Abstract (English)
The KM3NeT Collaboration is installing the ARCA and ORCA neutrino detectors at the bottom of the Mediterranean Sea. The focus of ARCA is neutrino astronomy, while ORCA is optimised for neutrino oscillation studies. Both detectors are already operational in their intermediate states and collect valuable data, including the measurements of the muons produced by cosmic ray interactions in the atmosphere. This work explores the potential of machine learning models for the reconstruction of muon bundles, which are multi-muon events. For this, data collected with intermediate detector configurations of ARCA and ORCA was used in addition to simulated data from the envisaged final configurations of those detectors. Prediction of the total number of muons in a bundle as well as their total energy and even the energy of the primary cosmic ray is presented.
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