论文标题
熊猫软件触发的深度机器学习
Deep Machine Learning for the PANDA Software Trigger
论文作者
论文摘要
使用Monte Carlo模拟基于Geant的检测器仿真框架Pandaroot的蒙特卡洛模拟数据，已经研究了Fair Panda实验的软件触发方法的深度机器学习方法。已经在四个不同的抗蛋白束力矩上研究了涵盖主要物理主题的十个物理通道，包括电磁，外来，炭，开放式魅力和重型反应通道。已经研究了二进制和多类分类以及七个不同的网络体系结构。最后，由于其可扩展性，性能和稳定性，选择了一个在二元分类方案中具有四个残留块的残留卷积神经网络。提出的研究代表了完全基于软件的触发系统的可行性研究。与传统的选择方法相比，深度机学习方法的效率增长高达200 \％，同时使背景降低因子保持在所需的1/1000水平。此外，结果表明，其他输入变量的使用可以改善数据质量以进行后续分析。这项研究表明，熊猫软件触发器可以从深度机器学习方法中受益匪浅。
Deep machine learning methods have been studied for the software trigger of the future PANDA experiment at FAIR, using Monte Carlo simulated data from the GEANT-based detector simulation framework PandaRoot. Ten physics channels that cover the main physics topics, including electromagnetic, exotic, charmonium, open charm, and baryonic reaction channels, have been investigated at four different anti-proton beam momenta. Binary and multi-class classification together with seven different network architectures have been studied. Finally a residual convolutional neural network with four residual blocks in a binary classification scheme has been chosen due to its extendability, performance and stability. The presented study represents a feasibility study of a completely software-based trigger system. Compared to a conventional selection method, the deep machine learning approach achieved a significant efficiency gain of up to 200\%, while keeping the background reduction factor at the required level of 1/1000. Furthermore, it is shown that the use of additional input variables can improve the data quality for subsequent analysis. This study shows that the PANDA software trigger can benefit greatly from the deep machine learning methods.
