论文标题
使用深层神经网络从背景噪声中提取希格斯玻色子的信号
Extracting Signals of Higgs Boson From Background Noise Using Deep Neural Networks
论文作者
论文摘要
希格斯玻色子是一种基本粒子，希格斯信号的分类是高能量物理学中的一个众所周知的问题。 HIGGS信号的识别是一项具有挑战性的任务，因为它的信号与背景信号相似。这项研究建议使用随机森林，自动编码器和深度自动编码器的新型组合进行HIGGS信号分类，以构建坚固而广义的Higgs玻色子预测系统，以将HIGGS信号与背景噪声区分开。提出的合奏技术基于实现决策空间的多样性，结果显示了私人排行榜上的良好歧视能力。在接收器操作特性曲线下达到0.9的区域，中位显着性得分为3.429。
Higgs boson is a fundamental particle, and the classification of Higgs signals is a well-known problem in high energy physics. The identification of the Higgs signal is a challenging task because its signal has a resemblance to the background signals. This study proposes a Higgs signal classification using a novel combination of random forest, auto encoder and deep auto encoder to build a robust and generalized Higgs boson prediction system to discriminate the Higgs signal from the background noise. The proposed ensemble technique is based on achieving diversity in the decision space, and the results show good discrimination power on the private leaderboard; achieving an area under the Receiver Operating Characteristic curve of 0.9 and an Approximate Median Significance score of 3.429.
