论文标题
使用卷积神经网络的夸克 - 格鲁恩喷气歧视
Quark-Gluon Jet Discrimination Using Convolutional Neural Networks
论文作者
论文摘要
目前，正在研究新开发的人工智能技术，特别是卷积神经网络，用于用于粒子物理撞机数据的数据处理和分类。这样一个挑战的任务是将夸克引发的喷气式与Gluon发射的喷气式区分开。在先前的工作之后，我们通过将轨道信息和热量表沉积物像素化作为图像作为图像，并作为检测器重建。我们通过培训Quark-Gluon歧视任务的几个最近开发的最先进的卷积神经网络来测试深度学习范式。我们比较了使用Quark-Gluon歧视训练的各种网络体系结构获得的结果，以及对摘要变量进行培训的增强决策树（BDT）。
Currently, newly developed artificial intelligence techniques, in particular convolutional neural networks, are being investigated for use in data-processing and classification of particle physics collider data. One such challenging task is to distinguish quark-initiated jets from gluon-initiated jets. Following previous work, we treat the jet as an image by pixelizing track information and calorimeter deposits as reconstructed by the detector. We test the deep learning paradigm by training several recently developed, state-of-the-art convolutional neural networks on the quark-gluon discrimination task. We compare the results obtained using various network architectures trained for quark-gluon discrimination and also a boosted decision tree (BDT) trained on summary variables.
