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arXiv 2201.12185astro-ph.HE
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基于机器学习的DAMPE氦通量分析量热计饱和校正方法

Machine learning-based method of calorimeter saturation correction for helium flux analysis with DAMPE experiment

Mikhail Stolpovskiy, Xin Wu, Andrii Tykhonov, Maksym Deliyergiyev, Chiara Perrina, Maria Munoz, David Droz, Arshia Ruina, Enrico Catanzani

摘要:

DAMPE是一个空间实验,用于测量能量高达每核子100 TeV左右的宇宙射线通量。在能量超过几十TeV时,DAMPE量热计的电子会饱和,留下某些没有能量记录的条。在目前的工作中,我们讨论了机器学习技术的应用程序的DAMPE数据的处理,以补偿热量计的饱和能量损失。

英文摘要:

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.

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。

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