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arXiv 2407.21777hep-lat
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Lanczos算法用于晶格QCD矩阵元

Lanczos algorithm for lattice QCD matrix elements

Daniel C. Hackett, Michael L. Wagman

AI总结:

本文扩展Lanczos算法框架至三点关联函数,用于从晶格QCD中提取矩阵元,通过虚假态过滤和抗离群值估计,在无噪声模拟及核子奇异标量流矩阵元中展示了优于有效质量的收敛性和多态估计能力。

摘要:

近期研究发现,基于Lanczos算法的分析框架能够从欧几里得关联函数中提取能级,其基态收敛速度优于有效质量,可从单个关联函数中为多个态提供收敛估计,并给出双侧误差界。在过滤掉虚假本征值并在嵌套自助法框架内使用抗离群值估计器后,Lanczos估计器的行为更类似于多态拟合结果,而非有效质量——但无需进行统计拟合。我们将此框架扩展至从三点关联函数确定矩阵元,并提供了涉及限制到厄米子空间的“虚假态过滤”的物理图像。通过将方法应用于无噪声模拟数据以及具有核子量子数的基态和激发态之间奇异标量流的(裸)前向矩阵元的示例,我们展示了矩阵元与谱学相比具有类似的优势。

英文摘要:

Recent work found that an analysis formalism based on the Lanczos algorithm allows energy levels to be extracted from Euclidean correlation functions with faster ground-state convergence than effective masses, convergent estimators for multiple states from a single correlator, and two-sided error bounds. After filtering out spurious eigenvalues and using outlier-robust estimators within a nested bootstrap framework, Lanczos estimators behave more like multi-state fit results than effective masses -- but without involving statistical fitting. We extend this formalism to the determination of matrix elements from three-point correlation functions and provide a physical picture of "spurious state filtering" involving restriction to a Hermitian subspace. We demonstrate similar advantages for matrix elements as for spectroscopy through example applications to noiseless mock-data and (bare) forward matrix elements of the strange scalar current between both ground and excited states with the quantum numbers of the nucleon.

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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