论文标题
基于图像的喷射分析
Image-Based Jet Analysis
论文作者
论文摘要
基于图像的喷射分析是建立在喷气机的喷气图像表示基础上的，该喷气式会导致高能物理与计算机视觉和深度学习领域之间的直接连接。通过这种联系，已经出现了各种各样的新喷气分析技术。在本文中，我们调查了基于JET图像的分类模型，主要基于卷积神经网络的使用，研究了这些模型所学的方法以及它们对不确定性的敏感性是什么，并回顾了将这些模型从现象学研究转移到LHC实验中现实世界应用的最新成功。除了喷气分类外，还讨论了基于喷气图像的技术的其他几种应用，包括能量估计，降低降噪，数据生成和异常检测。
Image-based jet analysis is built upon the jet image representation of jets that enables a direct connection between high energy physics and the fields of computer vision and deep learning. Through this connection, a wide array of new jet analysis techniques have emerged. In this text, we survey jet image based classification models, built primarily on the use of convolutional neural networks, examine the methods to understand what these models have learned and what is their sensitivity to uncertainties, and review the recent successes in moving these models from phenomenological studies to real world application on experiments at the LHC. Beyond jet classification, several other applications of jet image based techniques, including energy estimation, pileup noise reduction, data generation, and anomaly detection, are discussed.
