一条线运行它们：在$N$体模拟中的SuperEasy大质量中微子线性响应
One line to run them all: SuperEasy massive neutrino linear response in $N$-body simulations
摘要：
在这项工作中，我们提出了一种新的，但非常简单的方法，将大质量中微子的宇宙学$N$体模拟的影响。这种所谓的“SuperEasy线性响应”方法是基于无碰撞玻尔兹曼方程在聚类和自由流限制下的解析解，然后通过有理函数插值函数与宇宙学相关系数连接，这些系数由宇宙学模型参数的简单代数表达式给出。结果是对引力势的{ \it单线修改}，其仅需要冷物质密度对比作为实时输入，并且其可以并入具有粒子网格组件的任何$N$体代码中，而不需要额外的实现成本。为了证明它的力量，我们实现了公开可用的\gadgetcode{}程序的SuperEasy方法，并表明，中微子质量和不超过$\sum m_ν\simeq 1$ eV，总物质和冷物质的功率谱是在低于1和低于0.1的协议，从最先进的线性响应模拟文献。除了其最小的实现成本，与现有的大规模中微子模拟方法相比，SuperEasy方法具有更好的内存效率，相对于标准的$Λ$ CDM模拟没有运行时开销，并且不需要后处理。该方法的最小性质允许有限的计算资源被转移到对感兴趣的其他物理效应进行建模，例如，流体力学的重子物理学
英文摘要：
We present in this work a novel and yet extremely simple method for incorporating the effects of massive neutrinos in cosmological $N$-body simulations. This so-called "SuperEasy linear response" approach is based upon analytical solutions to the collisionless Boltzmann equation in the clustering and free-streaming limits, which are then connected by a rational function interpolation function with cosmology-dependent coefficients given by simple algebraic expressions of the cosmological model parameters. The outcome is a {\it one-line modification} to the gravitational potential that requires only the cold matter density contrast as a real-time input, and that can be incorporated into any $N$-body code with a Particle--Mesh component with no additional implementation cost. To demonstrate its power, we implement the SuperEasy method in the publicly available \gadgetcode{} code, and show that for neutrino mass sums not exceeding $\sum m_ν\simeq 1$ eV, the total matter and cold matter power spectra are in sub-1\% and sub-0.1\% agreement with those from state-of-the-art linear response simulations in literature. Aside from its minimal implementation cost, compared with existing massive neutrino simulation methods, the SuperEasy approach has better memory efficiency, incurs no runtime overhead relative to a standard $Λ$CDM simulation, and requires no post-processing. The minimal nature of the method allows limited computational resources to be diverted to modelling other physical effects of interest, e.g., baryonic physics via hydrodynamics.
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