Practical considerations in modeling the low light response of photomultiplier tubes in large batch testing
Practical considerations in modeling the low light response of photomultiplier tubes in large batch testing
复制标题
大批量测试中光电倍增管低光响应建模的实际考虑
DOI：
10.1016/j.nima.2019.03.001
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Shaver, W.
中科院分区：
文献类型：
--
作者：
Coquelin, D.;Jobin, T.;Kemmerer, W.;Maxwell, P.;Merten, S.;Moller, E.;Morris, W.;Niculescu, G.;Niculescu, I.;Shaver, W.
光电倍增管仍然是一种可靠、经济高效的检测亚原子粒子与探测器相互作用产生的光的方法。对于预期光输出适中的探测器来说，表征灯管的低光响应至关重要。存在解决这个问题的几种现象学模型。本文对三种此类方法进行了并排比较，因为它们源自对杰斐逊实验室环形成像切伦科夫探测器使用的管的大规模测试。发现电子管的主要特性（例如增益）在所有考虑的模型的预期不确定性范围内是一致的。利用该研究的广泛性质，开发并训练了一种基于人工神经网络的机器学习算法，该算法能够直接从原始 ADC 数据获取管特性。经过训练的神经网络产生的结果与所考虑的三个模型完全兼容，大大节省了计算时间和实验人员的开销。
Photomultiplier tubes continue to be a reliable, cost-effective means of detecting light produced by the interaction of subatomic particles with detectors. For detectors where the expected light yield is modest, characterizing the low light response of the tube is of paramount importance. Several phenomenological models addressing this issue exist. This paper presents side-by-side comparison between three such approaches as they arose from a large scale testing of tubes to be used by a Ring Imaging Cherenkov detector at Jefferson Lab. The main characteristics of the tubes, such as the gain, were found to be consistent within the expected uncertainties for all models considered. Leveraging the extensive nature of the study, a machine learning algorithm based on an artificial neural network capable of obtaining the tube characteristics directly from the raw ADC data was developed and trained. The trained neural network produced results fully compatible with the three models considered, with substantial savings in both computation time and experimenter overhead.
光电倍增管对超低信号响应的精确分析
DOI：
10.1016/j.nima.2017.07.053
发表时间：
2016
影响因子：
1.4
作者：
P. Degtiarenko
通讯作者：
P. Degtiarenko