标题： $\texttt{GENGARS}$: N体模拟中具有任意三阶谱的精确非高斯初始条件 显示英文标题

标题： $\texttt{GENGARS}$: Accurate non-Gaussian initial conditions with arbitrary bispectrum for N-body simulations

摘要： 原初非高斯性被各种暴胀模型所预测，而N体模拟是研究其在大尺度结构上印记的关键工具。 在本工作中，我们介绍了\texttt{GENGARS}（GEnerator of Non-Gaussian ARbitrary Shapes），一个用于生成N体模拟的精确非高斯初始条件的框架。 它基于Wagner & Verde（2012）提出的公式，能够生成具有所需可分离三阶谱的原初引力势$B_{\Phi}(k_1,k_2,k_3)$。 对于局部、等边和正交的非高斯模板，我们将我们的方法与已建立的\texttt{2LPT-PNG}代码进行对比。 我们表明，\texttt{GENGARS}通过抑制原初功率谱中的虚假贡献，实现了更高的精度和更低的噪声。 本文旨在介绍该方法，量化其性能，并说明其相对于现有方法的优势和适用案例。 显示英文摘要

摘要： Primordial non-Gaussianity is predicted by various inflationary models, and N-body simulations are a crucial tool for studying its imprints on large-scale structure. In this work, we present \texttt{GENGARS} ( GEnerator of Non-Gaussian ARbitrary Shapes), a framework for generating accurate non-Gaussian initial conditions for N-body simulations. It builds upon the formulation introduced by Wagner \& Verde (2012), enabling to generate a primordial gravitational potential with a desired separable bispectrum $B_{\Phi}(k_1,k_2,k_3)$. For the local, equilateral and orthogonal non-Gaussian templates, we benchmark our method against the well-established \texttt{2LPT-PNG} code. We show that \texttt{GENGARS} achieves improved accuracy and lower noise by suppressing spurious contributions to the primordial power spectrum. This paper aims at presenting the method, quantifying its performance and illustrating the benefits and applicable use cases over existing approaches.