论文标题
基于机器学习的量热饱和度校正方法，用于使用Dampe实验
Machine learning-based method of calorimeter saturation correction for helium flux analysis with DAMPE experiment
论文作者
论文摘要
Dampe是一个太空传播实验，用于测量每个核子的能量，宇宙射线通量的能量约为100 teV。在几十个TEV以上的能量下，dampe量热计的电子设备将饱和，而某些条没有记录的能量。在目前的工作中，我们讨论了机器学习技术用于处理Dampe数据的应用，以补偿因饱和度损失的热量计能量。
DAMPE is a space-borne experiment for the measurement of the cosmic-ray fluxes at energies up to around 100 TeV per nucleon. At energies above several tens of TeV, the electronics of DAMPE calorimeter would saturate, leaving certain bars with no energy recorded. In the present work we discuss the application of machine learning techniques for the treatment of DAMPE data, to compensate the calorimeter energy lost by saturation.
