提高J/ψ质量估计的精度:集成和深度学习方法的研究

IF 7.2 2区 物理与天体物理 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Serpil Yalcin Kuzu , Ayben Karasu Uysal , Mustafa Kaya
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引用次数: 0

摘要

本研究评估了集成学习方法和深度神经网络(dnn)在LHC质子-质子碰撞中识别J/ψ→μ+μ−事件的方法,重点研究了倾斜数据集中的介子衰变通道。为此,我们实施了基于随机森林(RF)、梯度增强决策树(GBDT)和深度神经网络的8种不同的机器学习模型,以研究确定调和事件的最有效方法。采用了精度、召回率、F-1评分、几何平均(G-mean)和平衡精度(BAcc)等性能指标,并通过StratifiedKFold交叉验证验证了模型在倾斜数据场景下的稳健性。结果表明深度神经网络是最熟练的,强调了它们在粒子物理复杂数据分析中的潜力。利用晶体球(CB)函数对深度神经网络的结果,估计了J/ψ质量的精度。这项研究不仅提高了人们对机器学习在高能粒子碰撞中的应用的理解,而且为该领域更深入的研究奠定了基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enhancing precision in J/ψ mass estimation: A study of ensemble and deep learning methods
This study evaluates ensemble learning methods and Deep Neural Networks (DNNs) for identifying J/ψμ+μ events in proton-proton collisions at the LHC, focusing on the dimuon decay channel within a skewed dataset. For this purpose, 8 different machine learning models based on Random Forest (RF), Gradient Boosting Decision Trees (GBDT), and DNNs were implemented to investigate the most effective approach for charmonium event determination. Performance metrics such as precision, recall, F-1 Score, geometric mean (G-mean), and balanced accuracy (BAcc) are employed, with StratifiedKFold cross-validation verifying the models' robustness in skewed data scenarios. Results demonstrate DNNs as the most proficient, underscoring their potential in complex data analysis in particle physics. Utilizing the Crystal Ball (CB) function on the results of DNNs, the precision of the J/ψ mass was estimated. This study not only enhances understanding of machine learning applications in high-energy particle collisions but also sets the stage for more advanced research in this field.
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来源期刊
Computer Physics Communications
Computer Physics Communications 物理-计算机:跨学科应用
CiteScore
12.10
自引率
3.20%
发文量
287
审稿时长
5.3 months
期刊介绍: The focus of CPC is on contemporary computational methods and techniques and their implementation, the effectiveness of which will normally be evidenced by the author(s) within the context of a substantive problem in physics. Within this setting CPC publishes two types of paper. Computer Programs in Physics (CPiP) These papers describe significant computer programs to be archived in the CPC Program Library which is held in the Mendeley Data repository. The submitted software must be covered by an approved open source licence. Papers and associated computer programs that address a problem of contemporary interest in physics that cannot be solved by current software are particularly encouraged. Computational Physics Papers (CP) These are research papers in, but are not limited to, the following themes across computational physics and related disciplines. mathematical and numerical methods and algorithms; computational models including those associated with the design, control and analysis of experiments; and algebraic computation. Each will normally include software implementation and performance details. The software implementation should, ideally, be available via GitHub, Zenodo or an institutional repository.In addition, research papers on the impact of advanced computer architecture and special purpose computers on computing in the physical sciences and software topics related to, and of importance in, the physical sciences may be considered.
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