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arXiv 2009.06754hep-phhep-ex
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在LHC上使用Di-Higgs产生对机器学习技术进行基准测试

Benchmarking Machine Learning Techniques with Di-Higgs Production at the LHC

Benjamin Tannenwald, Christopher Neu, Ang Li, Gracemarie Buehlmann, Anna Cuddeback, Leigh Hatfield, Ruhi Parvatam, Colby Thompson

摘要:

高能物理分析的许多领域都开始探索机器学习技术。强大的方法可以用来识别和测量罕见的过程,从以前无法克服的背景。在大型强子对撞机上,最深刻的标准模型特征之一是通过希格斯玻色子的自耦合产生希格斯玻色子玻色子对。该过程的小横截面使得即使对于具有最大分支分数($hh\rightarrow b\bar{b}b\bar{b}$)的衰变通道也非常难以检测。本文对各种方法(提升决策树,各种神经网络架构,半监督算法)进行了相互比较,以分类高能物理学家可用的各种技术中的一些,作为HL-LHC方法的时代。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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