AI辅助超分辨率宇宙学模拟 V：宇宙学感知的超分辨率
AI-assisted super-resolution cosmological simulations V: Cosmology-aware super-resolution
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中文总结 AI 辅助
提出一种宇宙学感知的GAN超分辨率模型，联合生成粒子位移和速度，在Quijote模拟上训练，跨宇宙学再现物质分布与晕丰度，功率谱误差均值3.6%，为非线性结构和场级推断提供6D相空间实现。
中文摘要 AI 辅助
我们提出了一种用于粒子相空间中宇宙学N体模拟超分辨率（SR）的宇宙学感知生成模型。该模型联合生成位移和速度，将基于生成对抗网络（GAN）的生成器和判别器以演化的低分辨率（LR）场和五个ΛCDM参数$(\Omega_m,\Omega_b,h,n_s,\sigma_8)$为条件。我们在Quijote拉丁超立方体套件在$z=0$时的100对匹配的高低分辨率对上训练单一模型，将$1\\,h^{-1}\\,\mathrm{Gpc}$盒子中的$512^3$个粒子映射到$1024^3$个粒子。在从SR训练中排除的十个宇宙学上，该模型再现了广泛的物质分布以及聚类和暗物质晕丰度的宇宙学变化。在$0.1<k<3\\,h\\,\mathrm{Mpc}^{-1}$范围内，每个宇宙学的最大绝对功率谱误差的平均值为$3.6\\%$，而FoF暗物质晕丰度在分析的低于$10^{14.8}\\,h^{-1}M_\odot$的质量区间内与HR一致，误差在$15\\%$以内。卫星丰度和占据数保留了较大的、质量依赖的差异。在一个近基准宇宙学上测量的校正应用于其他九个测试宇宙学时，减少了这些系统性误差的共享部分。基于功率谱的推断测试也表明，校准减少了所有参数的平均偏移。这些结果将我们的SR模型扩展到不同宇宙学，并为后续的非线性结构和场级宇宙学推断研究提供了6D相空间实现。
英文摘要
We present a cosmology-aware generative model for super-resolution (SR) of cosmological $N$-body simulations in particle phase space. The model jointly generates displacements and velocities, conditioning a generative adversarial network (GAN) based generator and discriminator on evolved low-resolution (LR) fields and five $Λ$CDM parameters $(Ω_m,Ω_b,h,n_s,σ_8)$. We train a single model on 100 matched low-high resolution pairs from the Quijote Latin-hypercube suite at $z = 0$, mapping $512^3$ to $1024^3$ particles in $1\,h^{-1}\,\mathrm{Gpc}$ boxes. On ten cosmologies excluded from SR training, the model reproduces the broad matter distribution and the cosmological variation of clustering and halo abundances. Over $0.1<k<3\,h\,\mathrm{Mpc}^{-1}$, the mean of the per-cosmology maximum absolute power-spectrum errors is $3.6\%$, while FoF halo abundances agree with HR to within $15\%$ in the analyzed mass bins below $10^{14.8}\,h^{-1}M_\odot$. Satellite abundances and occupations retain larger, mass-dependent discrepancies. Corrections measured at one near-fiducial cosmology reduce a shared component of these systematic errors when applied to the other nine test cosmologies. A power spectrum based inference test also shows that calibration reduces the mean shifts in all parameters. These results extend our SR model across cosmologies and provide 6D phase space realizations for subsequent studies of non-linear structure and field-level cosmological inference.
发表机构
- Carnegie Mellon University（卡内基梅隆大学）
- Flatiron Institute（平顿研究所）
- Princeton University（普林斯顿大学）
机构由 AI 辅助整理，请以论文原文为准。
