论文标题
使用图像分类的端到端分析
End-to-end analysis using image classification
论文作者
论文摘要
近年来，出现了使用机器和深度学习技术的高能物理实验的数据端到端分析。这些分析使用深度学习算法直接从低级检测器信息直接转到对相互作用进行分类的高级数量。这些分析的最流行的算法类是卷积神经网络，这些网络以图像格式化的实验数据运行。端到端分析是传统工作流的跳过阶段，其中包括对交互中产生的粒子的重建，因此不受事件重建过程中不准确性的效率损失和不准确性来源的限制。在许多情况下，与以前的最新方法相比，深度学习端到端分析的性能显着提高。
End-to-end analyses of data from high-energy physics experiments using machine and deep learning techniques have emerged in recent years. These analyses use deep learning algorithms to go directly from low-level detector information directly to high-level quantities that classify the interactions. The most popular class of algorithms for these analyses are convolutional neural networks that operate on experimental data formatted as images. End-to-end analyses skip stages of the traditional workflow that includes the reconstruction of particles produced in the interactions, and as such are not limited by efficiency losses and sources of inaccuracy throughout the event reconstruction process. In many cases, deep learning end-to-end analyses have been shown to have significantly increased performance compared to previous state-of-the-art methods.
