论文标题
机器学习技术以提高ISMRAN实验的抗神经检测效率
Machine learning technique to improve anti-neutrino detection efficiency for the ISMRAN experiment
论文作者
论文摘要
用于反应堆抗中性检测的印度闪烁体基质 - ISMRAN实验旨在检测通过反反应器通过反β衰减反应（IBD）从反应堆发出的电子抗神经（$ \barν_e$）。该设置由1吨分段的Gadolinium Foil包裹的塑料闪光灯阵列组成，计划用于远程反应堆监测和无菌中微子搜索。从IBD中检测提示正电子和延迟中子将提供ISMRAN中$ \barν_e$ event的签名。具有能源存款（$ \ mathrm {n_ {bars}} $）和这些沉积能量的总和的段数（$ \ mathrm {n_ {bars}} $）的数量被用作识别及时正电子事件和延迟中子捕获事件的判别因子。但是，由于$ \ mathrm的重叠区域{n_ {bars}} $，基于上述变量的简单选择会导致低$ \barν_e$信号检测效率，并为提示和延迟的事件提供总和能量。在这种情况下，使用适当调整的变量进行了适当调整的变量，多变量分析（MVA）工具在这种情况下很有用。在这项工作中，我们将人工神经网络的应用（MLP）（尤其是贝叶斯扩展-MLPBNN）应用于ISMRAN的模拟信号和背景事件，从而报告了人工神经网络的应用结果。据报道，MLP在氢，Gadolinuim核以及典型的反应堆$γ$ ray和快速中子背景的中子捕获事件和快速中子背景中应用的迅速捕获事件分类的结果。 $ \ sim $ 91 $ \％$的提高效率，背景拒绝$ \ sim $ 73 $ \％$及时选择，$ \ sim $ 89 $ \％$的效率为$ \ sim $ \ sim $ 71 $ \％$ $ \％$ \％$用于延迟捕获事件，使用mlpbbnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnn classifier实验。
The Indian Scintillator Matrix for Reactor Anti-Neutrino detection - ISMRAN experiment aims to detect electron anti-neutrinos ($\barν_e$) emitted from a reactor via inverse beta decay reaction (IBD). The setup, consisting of 1 ton segmented Gadolinium foil wrapped plastic scintillator array, is planned for remote reactor monitoring and sterile neutrino search. The detection of prompt positron and delayed neutron from IBD will provide the signature of $\barν_e$ event in ISMRAN. The number of segments with energy deposit ($\mathrm{N_{bars}}$) and sum total of these deposited energies are used as discriminants for identifying prompt positron event and delayed neutron capture event. However, a simple cut based selection of above variables leads to a low $\barν_e$ signal detection efficiency due to overlapping region of $\mathrm{N_{bars}}$ and sum energy for the prompt and delayed events. Multivariate analysis (MVA) tools, employing variables suitably tuned for discrimination, can be useful in such scenarios. In this work we report the results from an application of artificial neural network -- the multilayer perceptron (MLP), particularly the Bayesian extension -- MLPBNN, to the simulated signal and background events in ISMRAN. The results from application of MLP to classify prompt positron events from delayed neutron capture events on Hydrogen, Gadolinium nuclei and also from the typical reactor $γ$-ray and fast neutron backgrounds is reported. An enhanced efficiency of $\sim$91$\%$ with a background rejection of $\sim$73$\%$ for prompt selection and an efficiency of $\sim$89$\%$ with a background rejection of $\sim$71$\%$ for the delayed capture event, is achieved using the MLPBNN classifier for the ISMRAN experiment.
