IceCube测量大气μ子谱的机器学习分析链开发
Development of a Machine Learning Based Analysis Chain for the Measurement of Atmospheric Muon Spectra with IceCube
摘要：
使用最先进的机器学习算法选择来自IceCube中检测到的空气簇射事件的高能μ子。使用mRMR算法选择用于区分低能量μ子束背景的HE μ子事件的属性，并通过随机森林模型对事件进行分类。在随后的分析步骤中，使用展开软件TRUEE将获得的样品用于重建大气μ子能谱。重建的光谱覆盖从$10^4\,$ GeV到$10^6\,$ GeV的能量范围。一般的分析方案，包括使用的第一年的数据与冰立方在其完整的配置与$86$仪表字符串。
英文摘要：
High-energy muons from air shower events detected in IceCube are selected using state of the art machine learning algorithms. Attributes to distinguish a HE-muon event from the background of low-energy muon bundles are selected using the mRMR algorithm and the events are classified by a random forest model. In a subsequent analysis step the obtained sample is used to reconstruct the atmospheric muon energy spectrum, using the unfolding software TRUEE. The reconstructed spectrum covers an energy range from $10^4\,$GeV to $10^6\,$GeV. The general analysis scheme is presented, including results using the first year of data taken with IceCube in its complete configuration with $86$ instrumented strings.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
