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arXiv 2105.09271hep-ph
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机器学习方法用于包容性$|V_{ub}|$确定的潜力和局限性

Potential and limitations of machine-learning approaches to inclusive $|V_{ub}|$ determinations

Anke Biekötter, Ka Wang Kwok, Benjamin D. Pecjak

摘要:

确定包含半轻子$B \to X_u \ell ν$衰变中的$|V_{ub}|$将是Belle II的关键任务之一。在本文中,我们研究了机器学习方法的潜力和局限性,这些方法试图通过将$B\to X_u \ellν$信号的实验可访问的基准区域扩展到$B\to X_c \ellν$背景占主导地位的区域来减少理论的不确定性。我们发现,与使用物理学家设计的高级特征的BDT设置相比,在低级单粒子特征上训练的深度神经网络在分离信号与背景方面提供了适度的改进。我们进一步说明,虽然这种深度神经网络的信号接受在信号很小的运动学区域中恶化,例如在高强子不变质量下,但排除运动学特征的神经网络在运动学上更平坦,但在固定运动学上对专有强子最终状态的采样中不那么包容。这两个设置之间的权衡是有点蒙特卡洛依赖,我们研究这个问题,除了广泛使用的$B$ -物理工具EvtGen的多用途事件发生器夏尔巴。

英文摘要:

The determination of $|V_{ub}|$ in inclusive semileptonic $B \to X_u \ell ν$ decays will be among the pivotal tasks of Belle II. In this paper we study the potential and limitations of machine learning approaches that attempt to reduce theory uncertainties by extending the experimentally accessible fiducial region of the $B\to X_u \ellν$ signal into regions where the $B\to X_c \ellν$ background is dominant. We find that a deep neural network trained on low-level single particle features offers modest improvement in separating signal from background, compared to BDT set-ups using physicist-engineered high-level features. We further illustrate that while the signal acceptance of such a deep neural network deteriorates in kinematic regions where the signal is small, such as at high hadronic invariant mass, neural networks which exclude kinematic features are flatter in kinematics but less inclusive in the sampling of exclusive hadronic final states at fixed kinematics. The trade-off between these two set-ups is somewhat Monte Carlo dependent, and we study this issue using the multipurpose event generator Sherpa in addition to the widely used $B$-physics tool EvtGen.

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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