论文标题
中微子核互动中的顶点发现：模型架构比较
Vertex finding in neutrino-nucleus interaction: A Model Architecture Comparison
论文作者
论文摘要
我们比较了机器学习（ML）算法的不同神经网络体系结构，旨在识别Minerva检测器中中微子相互作用顶点位置。将手工开发和优化的体系结构与使用“多节点进化神经网络的深度学习”（MENNDL）（Oak Ridge National Laboratory（ORNL）开发）的软件包进行了比较。这两个体系结构产生了类似的性能，表明与优化网络体系结构相关的系统学很小。此外，我们发现，虽然域专家手工调整网络是表现最好的网络，但差异可以忽略不计，并且自动生成的网络表现良好。人类和用于网络优化的计算机资源之间始终存在权衡，这项工作表明，假设资源可用，为节省大量专家时间提供了一种令人信服的方法。
We compare different neural network architectures for Machine Learning (ML) algorithms designed to identify the neutrino interaction vertex position in the MINERvA detector. The architectures developed and optimized by hand are compared with the architectures developed in an automated way using the package "Multi-node Evolutionary Neural Networks for Deep Learning" (MENNDL), developed at Oak Ridge National Laboratory (ORNL). The two architectures resulted in a similar performance which suggests that the systematics associated with the optimized network architecture are small. Furthermore, we find that while the domain expert hand-tuned network was the best performer, the differences were negligible and the auto-generated networks performed well. There is always a trade-off between human, and computer resources for network optimization and this work suggests that automated optimization, assuming resources are available, provides a compelling way to save significant expert time.
