标题： 在$O(N)$非线性σ模型中抑制NSPT涨落：大$N$范围内的模拟 显示英文标题

标题： Taming NSPT fluctuations in $O(N)$ Non-Linear Sigma Model: simulations in the large $N$ regime

摘要： 非线性σ模型(NLSM)是目标空间表现出复杂几何结构的场论的一个例子。 NLSM的一个显著特性是渐近自由，这引发了对微扰计算的兴趣。 在NLSM的格点公式中，人们自然会依赖数值随机微扰理论(NSPT)来进行高阶计算。 然而，当处理低维系统时，NSPT随着阶数越来越高，显示出越来越大的统计波动。 这当然并不令人意外，只要噪声不会完全掩盖信号，人们就可以接受这一点，但不幸的是，在一些模型中确实会发生这种情况。 我们研究了在$O(N)$背景下，这种行为强烈依赖于$N$。 正如预期的那样，较大的$N$值使得高阶计算成为可能。 显示英文摘要

摘要： The Non-Linear Sigma Model (NLSM) is an example of a field theory on a target space exhibiting intricate geometry. One remarkable characteristic of the NLSM is asymptotic freedom, which triggers interest in perturbative calculations. In the lattice formulation of NLSM, one would naturally rely on Numerical Stochastic Perturbation Theory (NSPT) to conduct high-order computations. However, when dealing with low-dimensional systems, NSPT reveals increasing statistical fluctuations with higher and higher orders. This of course does not come as a surprise and one is ready to live with this, as long as the noise is not going to completely kill the signal, which unfortunately in some models does take place. We investigate how, in the $O(N)$ context, this behaviour strongly depends on $N$. As expected, larger $N$ values make higher-order computations feasible.