SPINE：用于预测ΛCDM非线性功率谱演化的符号模型
$\texttt{SPINE}$: Symbolic Models to Predict the Evolution of the $Λ$CDM Nonlinear Power Spectrum
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中文总结 AI 辅助
该研究提出SPINE和SPINEX两个分析模拟器，基于符号回归技术，可在0.01-2 h Mpc⁻¹波数范围内高精度预测ΛCDM非线性功率谱，精度优于5%，为宇宙学功率谱建模提供高效可解释的替代方案。
中文摘要 AI 辅助
我们提出了SPINE和SPINEX这一对分析模拟器，用于在线性功率谱及多个关键宇宙学参数的基础上，预测波数范围为0.01 h Mpc⁻¹ < k < 2 h Mpc⁻¹内的非线性功率谱。这两个模型的主要区别在于参数化方式。我们的方法基于Peacock和Dodds（1996）最初提出的映射关系，两个模型均由符号回归推导得出的清晰数学表达式定义，符号回归是一种利用遗传编程识别能准确表征底层数据的解析方程的机器学习技术。该方法相比传统数值方法提供了更具可解释性和效率的替代方案，且在训练范围之外应表现出更优的外推性能。这些模拟器在ΛCDM Quijote拉丁超立方模拟数据集上完成训练，我们针对三种不同场景提供了两个模型的拟合结果：普朗克2018观测值20σ范围内的宇宙学模型、指定的基准宇宙学模型，以及从Quijote模拟中采样的更广泛范围的宇宙学模型。研究发现，SPINE和SPINEX在大多数情况下保持了优于5%的精度。这些模拟器为数值方法提供了快速替代方案，未来工作将聚焦于开发包含星系偏置和红移空间畸变的表达式，旨在提升多红移下红移空间功率谱的建模水平，使其可应用于大规模宇宙学巡天。
英文摘要
We present $\texttt{SPINE}$ and $\texttt{SPINEX}$, a pair of analytical emulators developed to predict the nonlinear power spectrum based on its linear counterpart and several essential cosmological parameters within the range of $0.01\;h\;\mathrm{Mpc}^{-1} <k< 2\;h\;\mathrm{Mpc}^{-1} $. The primary difference between the two models is their parameterisation. Our methodology is grounded in the mapping originally proposed by Peacock and Dodds (1996). Both models are defined by clear mathematical expressions derived from symbolic regression, a machine learning technique that utilises genetic programming to identify analytical equations that accurately represent the underlying data. This approach provides a more interpretable and efficient alternative to conventional numerical methods that should also exhibit superior extrapolation behaviour beyond the training range. The emulators have been trained on the $Λ$CDM Quijote Latin Hypercube simulations. We provide fits for both emulators across three distinct scenarios: cosmologies within 20$σ$ of the Planck-2018 observations, a designated fiducial cosmology, and a broader range of cosmologies sampled from the Quijote simulations. Our findings indicate that $\texttt{SPINE}$ and $\texttt{SPINEX}$ maintain an accuracy of better than 5% in the majority of cases. These emulators provide a rapid alternative to numerical methods, and future initiatives will focus on developing expressions that incorporate galaxy bias and redshift-space distortions. This advancement aims to enhance the modelling of redshift space power spectra across multiple redshifts, enabling their application in large-scale cosmological surveys.
