Evidence of fast neutron sensitivity for 3He detectors and comparison with Boron-10 based neutron detectors.

arXiv: Instrumentation and Detectors(2019)

引用 5|浏览22
暂无评分
摘要
The 3He-based neutron detectors are no longer the default solution for neutron scattering applications. Both the inability of fulfilling the requirements in performance, needed for the new instruments, and the shortage of 3He, drove a series of research programs aiming to find new technologies for neutron detection. The characteristics of the new detector technologies have been extensively tested to prove their effectiveness with respect to the state-of-the-art technology. Among these, the background rejection capability is crucial to determine. The signal-to-background ratio is strongly related to the performance figure-of-merit for most instruments. These are designed to exploit the high flux expected from the new high intensity neutron sources. Therefore, an inadequate background rejection could significantly affect the measurements, leading to detector saturation and misleading events. This is of particular importance for the kind of techniques in which the signals are rather weak. For the first time, the sensitivity of 3He detectors to fast neutrons, up to En = 10 MeV, has been estimated. Two independent measurements are presented: a direct calculation based on a subtraction method used to disentangle the thermal and the fast neutron contribution, while a further evidence is calculated indirectly through a comparison with the recently published data from a 10B-based detector. Both investigations give a characterization on the order of magnitude for the sensitivity. A set of simulations is presented as well in order to support and to validate the results of the measurements. A sensitivity of 4x10-3 is observed from the data. This is two orders of magnitude higher than that previously observed in 10B-based detectors.
更多
查看译文
关键词
Neutron detectors (cold and thermal neutrons), Fast neutron, Gaseous detectors, Boron-10, Helium-3, Neutron Spallation Sources
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要