We use the emulation framework CosmoPower to construct and publicly release neural network emulators of cosmological observables, including the Cosmic Microwave Background (CMB) temperature and polarization power spectra, matter power spectrum, distance-redshift relation, baryon acoustic oscillation (BAO) and redshift-space distortion (RSD) observables, and derived parameters. We train our emulators on Einstein-Boltzmann calculations obtained with high-precision numerical convergence settings, for a wide range of cosmological models including $Λ$CDM, $w$CDM, $Λ$CDM+$N_\mathrm{eff}$, and $Λ$CDM+$Σm_ν$. Our CMB emulators are accurate to better than 0.5% out to $\ell=10^4$ which is sufficient for Stage-IV data analysis, and our $P(k)$ emulators reach the same accuracy level out to $k=50 \,\, \mathrm{Mpc}^{-1}$, which is sufficient for Stage-III data analysis. We release the emulators via an online repository CosmoPower Organisation, which will be continually updated with additional extended cosmological models. Our emulators accelerate cosmological data analysis by orders of magnitude, enabling cosmological parameter extraction analyses, using current survey data, to be performed on a laptop. We validate our emulators by comparing them to CLASS and CAMB and by reproducing cosmological parameter constraints derived from Planck TT, TE, EE, and CMB lensing data, as well as from the Atacama Cosmology Telescope Data Release 4 CMB data, Dark Energy Survey Year-1 galaxy lensing and clustering data, and Baryon Oscillation Spectroscopic Survey Data Release 12 BAO and RSD data.
Modeling nonlinear scales for dynamical dark energy cosmologies with COLA
CBPF - Brazilian Center for Research in Physics（巴西物理研究中心）；Instituto de Física Teórica da Universidade Estadual Paulista（圣保罗州立大学理论物理研究所）；Department of Physics & Astronomy, Stony Brook University（石溪大学物理与天文学系）；C. N. Yang Institute for Theoretical Physics, Stony Brook University（石溪大学陈省身理论物理研究所）
Gaussian-Process Emulation of the Redshift-Space Halo Power Spectrum Monopole in Cosmologies with Massive Neutrinos
National Astronomical Observatories, Chinese Academy of Sciences（中国科学院国家天文台）；School of Astronomy and Space Science, University of Chinese Academy of Sciences（中国科学院大学天文与空间科学学院）；Institute for Frontiers in Astronomy and Astrophysics, Beijing Normal University（北京师范大学天文与前沿科学研究院）；Chinese Academy of Sciences South America Center for Astronomy (CASSACA), National Astronomical Observatories, Chinese Academy of Sciences（中国科学院南美天文中心）
MG-NECOLA: A Field-Level Emulator for $f(R)$ Gravity and Massive Neutrino Cosmologies
Universidad del Valle（谷大学）；Michigan Technological University（密歇根理工大学）；Università di Bologna（博洛尼亚大学）；INAF, Osservatorio di Astrofisica e Scienza dello Spazio di Bologna（意大利国家天体物理研究所博洛尼亚天文与空间科学观测站）
Dark Quest II: A Wide-Coverage Neural Network Emulator of the Nonlinear Matter Power Spectrum Across Extended Cosmologies
Graduate School of Social Data Science, Hitotsubashi University（立命馆大学社会科学数据科学研究生院）；Center for Gravitational Physics and Quantum Information, Yukawa Institute for Theoretical Physics, Kyoto University（京都大学重力物理与量子信息中心，山梨大学理论物理研究所）；Department of Astrophysics and Atmospheric Sciences, Faculty of Science, Kyoto Sangyo University（京都 Sangyo 大学科学系天文学与大气科学系）；Kavli Institute for the Physics and Mathematics of the Universe (WPI), The University of Tokyo Institutes for Advanced Study (UTIAS), The University of Tokyo（物理学与数学宇宙研究所（WPI），东京大学先进研究所（UTIAS），东京大学）；RIKEN Center for Advanced Intelligence Project（理化学研究院先进智能项目中心）
Aletheia: Emulating the halo mass function with evolution mapping
Max-Planck-Institut für extraterrestrische Physik（马克斯·普朗克地外物理研究所）；Ludwig-Maximilians-Universität München（慕尼黑大学）；Instituto de Astronomía Teórica y Experimental, CONICET-UNC（理论与实验天文学研究所，阿根廷国家科学研究委员会-科尔多瓦大学）；Universidad Nacional de Córdoba（科尔多瓦国立大学）
$\texttt{SPINE}$: Symbolic Models to Predict the Evolution of the $Λ$CDM Nonlinear Power Spectrum
University of Hull（赫尔大学）；E. A. Milne Centre for Astrophysics, University of Hull（赫尔大学 E. A. 米尔内天体物理中心）；Centre of Excellence for Data Science, AI, & Modelling (DAIM), University of Hull（赫尔大学数据科学、人工智能与建模卓越中心）；Max-Planck-Institut für extraterrestrische Physik（马克斯·普朗克地外物理研究所）；Universitäts-Sternwarte München（慕尼黑大学天文台）；Newcastle University（纽卡斯尔大学）
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。
Field Level Neural Network Emulator for Cosmological N-body Simulations
Max-Planck-Institut für Astrophysik（马克斯·普朗克天体物理研究所）；Stony Brook University（石溪大学）；Flatiron Institute（熨斗研究所）；Universidade Federal do Espírito Santo（圣埃斯皮里图联邦大学）；Princeton University（普林斯顿大学）；Simons Foundation（西蒙斯基金会）
A precise symbolic emulator of the linear matter power spectrum
CNRS（法国国家科学研究中心）；Sorbonne Université（索邦大学）；Institut d’Astrophysique de Paris (IAP)（巴黎天体物理研究所）；Heuristic and Evolutionary Algorithms Laboratory（启发式与进化算法实验室）；University of Applied Sciences Upper Austria（上奥地利应用科学大学）；Institute of Cosmology & Gravitation（宇宙学与引力研究所）；University of Portsmouth（朴茨茅斯大学）；University of Oxford（牛津大学）；Center for Computational Astrophysics（计算天体物理中心）；Flatiron Institute（弗拉蒂伦研究所）
syren-halofit: A fast, interpretable, high-precision formula for the $Λ$CDM nonlinear matter power spectrum
CNRS（法国国家科学研究中心）；Sorbonne Université（索邦大学）；Center for Computational Astrophysics, Flatiron Institute（弗拉蒂伦研究所计算天体物理中心）；University of Oxford（牛津大学）；Institute of Cosmology & Gravitation, University of Portsmouth（朴茨茅斯大学宇宙学与引力研究所）
Satellite galaxy abundance dependency on cosmology in Magneticum simulations
Alma Mater Studiorum Università di Bologna（博洛尼亚大学）；INAF - Osservatorio Astronomico di Trieste（意大利国家天体物理研究所的里雅斯特天文台）；IFPU - Institute for Fundamental Physics of the Universe（宇宙基础物理研究所）；University of Trieste（的里雅斯特大学）；INFN - National Institute for Nuclear Physics（意大利国家核物理研究所）；Ludwig-Maximilians-Universität München（慕尼黑大学）；Max-Planck-Institut für Astrophysik（马克斯·普朗克天体物理研究所）
