论文标题
在粒子物理实验中使用卷积神经网络进行信号背景分类
The use of Convolutional Neural Networks for signal-background classification in Particle Physics experiments
论文作者
论文摘要
图像分类中卷积神经网络（CNN）的成功促进了研究其用于分类粒子物理实验中获得的图像数据的使用。在这里，我们讨论了将CNN应用于粒子物理实验中的2D和3D图像数据的努力，以从背景中对信号进行分类。 在这项工作中，我们提出了广泛的卷积神经结构搜索，基于Ice Cube Neutminino天文台和类似Atlas的检测器的模拟数据，可以实现HEP分类用例的信号/背景区分的高度精度。我们证明了与具有更少参数的CNN的复杂重新结构体系结构相同的精度，并且目前对计算要求，培训和推理时间的比较进行了比较。
The success of Convolutional Neural Networks (CNNs) in image classification has prompted efforts to study their use for classifying image data obtained in Particle Physics experiments. Here, we discuss our efforts to apply CNNs to 2D and 3D image data from particle physics experiments to classify signal from background. In this work we present an extensive convolutional neural architecture search, achieving high accuracy for signal/background discrimination for a HEP classification use-case based on simulated data from the Ice Cube neutrino observatory and an ATLAS-like detector. We demonstrate among other things that we can achieve the same accuracy as complex ResNet architectures with CNNs with less parameters, and present comparisons of computational requirements, training and inference times.
