论文标题
LHC的基准测试机器学习技术与Di-Higgs生产
Benchmarking Machine Learning Techniques with Di-Higgs Production at the LHC
论文作者
论文摘要
高能物理分析的许多领域开始探索机器学习技术。强大的方法可用于识别和测量以前无法克服的背景的稀有过程。在LHC中仍可发现的最深刻的标准模型特征之一是通过Higgs自耦合的Higgs玻色子的一对生产。该过程的小横截面即使对于具有最大分支分数的衰减通道（$ hh \ rightarrow b \ bar {b} b \ bar {b} $）也使检测非常困难。本文基准了各种方法（增强决策树，各种神经网络体系结构，半监督算法），以分类为HL-LHC方法时代的高能量物理学家可用的一些各种技术。
Many domains of high energy physics analysis are starting to explore machine learning techniques. Powerful methods can be used to identify and measure rare processes from previously insurmountable backgrounds. One of the most profound Standard Model signatures still to be discovered at the LHC is the pair production of Higgs bosons through the Higgs self-coupling. The small cross section of this process makes detection very difficult even for the decay channel with the largest branching fraction ($hh\rightarrow b\bar{b}b\bar{b}$). This paper benchmarks a variety of approaches (boosted decision trees, various neural network architectures, semi-supervised algorithms) against one another to catalog a few of the various techniques available to high energy physicists as the era of the HL-LHC approaches.
