论文标题
使用机器学习来压缩物质传输功能$ t（k）$
Using machine learning to compress the matter transfer function $T(k)$
论文作者
论文摘要
线性物质功率谱$ P（K，Z）$将理论与宇宙学中的大规模结构观测联系起来。它的比例依赖性完全编码在物质传输函数$ t（k）$中，该$可以由Boltzmann求解器数值计算，也可以通过使用拟合函数（例如著名的Bardeen-Bond-Kaiser-Szalay（BBKS）和Eisenstein-Hu（Eh（EH）（eH）形式进行半分析计算。但是，BBK和EH公式都有一些重要的缺点。一方面，尽管BBK是一个简单的表达式，但它仅准确至$ 10 \％$，远高于即将进行的调查的$ 1 \％$精确目标。另一方面，尽管EH是即将进行的实验所需的准确性，但它是一个相当长且复杂的表达。在这里，我们使用一种特定的机器学习技术遗传算法（GAS）来得出传输函数$ t（k）$的简单准确的拟合公式。当还考虑了大规模中微子的影响时，我们的表达在EH公式上略有改善，而相比之下，我们的表达却大大缩短。
The linear matter power spectrum $P(k,z)$ connects theory with large scale structure observations in cosmology. Its scale dependence is entirely encoded in the matter transfer function $T(k)$, which can be computed numerically by Boltzmann solvers, and can also be computed semi-analytically by using fitting functions such as the well-known Bardeen-Bond-Kaiser-Szalay (BBKS) and Eisenstein-Hu (EH) formulae. However, both the BBKS and EH formulae have some significant drawbacks. On the one hand, although BBKS is a simple expression, it is only accurate up to $10\%$, which is well above the $1\%$ precision goal of forthcoming surveys. On the other hand, while EH is as accurate as required by upcoming experiments, it is a rather long and complicated expression. Here, we use the Genetic Algorithms (GAs), a particular machine learning technique, to derive simple and accurate fitting formulae for the transfer function $T(k)$. When the effects of massive neutrinos are also considered, our expression slightly improves over the EH formula, while being notably shorter in comparison.
