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arXiv 2403.04836gr-qc
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一种用于自旋泡沫交织子的蒙特卡洛算法

A Monte Carlo algorithm for spin foam intertwiners

Sebastian Steinhaus

AI总结:

本文提出了一种用于相干边界交织子的新概率分布,定义了马尔可夫链蒙特卡洛算法,在自旋泡沫量子引力中以极低成本实现了SU(2)相干顶点振幅的收敛高精度计算,并有效控制了符号问题。

摘要:

蒙特卡洛算法在自旋泡沫量子引力中几乎未被考虑。由于自旋泡沫振幅的量子本质,人们无法直接应用它们,且当前的符号问题威胁到收敛性,从而影响效率。然而,符号问题在自旋泡沫中的严重程度最终仍是未知的。在本文中,我们为相干(边界)交织子提出了一种新的概率分布,并利用它定义了一个马尔可夫链蒙特卡洛算法。我们将该算法应用于各种 Regge 型边界数据的 SU(2) 相干顶点振幅,发现其能以远低于显式计算的成本获得收敛且准确的结果。节省的资源转而被用于增加边界自旋的尺寸,从而弥合了与渐近公式的差距。尽管符号问题尚未解决,但在绝大多数情况下它已得到控制。最后,我们讨论了如何将该算法扩展到具有边界的更大三角剖分以及 4d Lorentzian 自旋泡沫模型。更具推测性的是,我们讨论了如何使用该算法来采样(Regge 几何的)体交织子。

英文摘要:

Monte Carlo algorithms are barely considered in spin foam quantum gravity. Due to the quantum nature of spin foam amplitudes one cannot readily apply them, and the present sign problem is a threat to convergence and thus efficiency. Yet, ultimately the severity of the sign problem in spin foams is not known. In this article we propose a new probability distribution for coherent (boundary) intertwiners, which we use to define a Markov Chain Monte Carlo algorithm. We apply this algorithm to the SU(2) coherent vertex amplitude for various Regge-type boundary data and find convergent, accurate results at far lower costs than the explicit calculation. The resources are instead used to increase the size of boundary spins, bridging the gap to the asymptotic formulae. While the sign problem is not solved, it is under control in the vast majority of cases. We close by discussing how this algorithm can be extended to larger triangulations with boundary and 4d Lorentzian spin foam models. More speculatively, we discuss how this algorithm can be used to sample (Regge-geometric) bulk intertwiners.

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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