Exponential Error Reduction for Glueball Calculations Using a Two-Level Algorithm in Pure Gauge Theory

Lorenzo Barca, Francesco Knechtli, Sofie Martins, Michael Peardon, Stefan Schaefer, Juan Andrés Urrea-Niño
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Abstract

This study explores the application of a two-level algorithm to enhance the signal-to-noise ratio of glueball calculations in four-dimensional $\mathrm{SU(3)}$ pure gauge theory. Our findings demonstrate that the statistical errors exhibit an exponential reduction, enabling reliable extraction of effective masses at distances where current standard methods would demand exponentially more samples. However, at shorter distances, standard methods prove more efficient due to a saturation of the variance reduction using the multi-level method. We discuss the physical distance at which the multi-level sampling is expected to outperform the standard algorithm, supported by numerical evidence across different lattice spacings and glueball channels. Additionally, we construct a variational basis comprising 35 Wilson loops up to length 12 and 5 smearing sizes each, presenting results for the first state in the spectrum for the scalar, pseudoscalar, and tensor channels.
在纯量子理论中使用两级算法减少胶球计算的指数误差
本研究探索了如何应用两级算法来提高四维$mathrm{SU(3)}$纯规理论中胶团计算的信噪比。我们的研究结果表明,统计误差呈现指数级下降,从而能够在当前标准方法需要指数级更多样本的距离上可靠地提取有效质量。然而,在较短的距离上,由于使用多层次方法减少的方差达到饱和,标准方法被证明更为有效。我们讨论了多层次采样有望在何种物理距离上优于标准算法,并通过不同晶格间距和胶球通道的数值证据予以支持。此外,我们还构建了一个包含 35 个长度为 12 的威尔逊环和每个环 5 个涂抹大小的变分基础,并给出了标量、伪标量和张量通道的谱中第一态的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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