State-of-the-art galaxy formation simulations generate data within weeks or months. Their results consist of a random sub-sample of possible galaxies with a fixed number of stars. We propose a ML based method, GalacticFlow, that generalizes such results. We use normalizing flows to learn the extended distribution function of galaxies conditioned on global galactic parameters. GalacticFlow then provides a continuized and condensed representation of the ensemble of galaxies in the data. Thus, essentially compressing large amounts of explicit simulation data into a small implicit generative model. Our model is able to evaluate any galaxy eDF given by a set of global parameters and allows generating arbitrarily many stars from it. We show that we can learn such a representation, embodying the entire mass range from dwarf to Milky Way mass, from only 90 galaxies in $\sim18$ hours on a single RTX 2080Ti and generate a new galaxy of one million stars within a few seconds.
JFlow: Model-Independent Spherical Jeans Analysis using Equivariant Continuous Normalizing Flows
Particle Theory and Cosmology Group, Center for Theoretical Physics of the Universe, Institute for Basic Science (IBS)（基本科学研究院（IBS）粒子理论与宇宙学组，宇宙理论物理中心）；Department of Physics and Astronomy, Rutgers, The State University of New Jersey（新泽西州立大学罗格斯大学物理与天文学系）；National Institute of Technology, Sendai College（Sendai学院国立技术研究所）；Astronomical Institute, Tohoku University（东北大学天文研究所）；ICRR, The University of Tokyo（东京大学ICRR）；Kavli Institute for the Physics and Mathematics of the Universe (WPI), The University of Tokyo Institutes for Advanced Study, The University of Tokyo（东京大学Kavli研究所（WPI），东京大学先进研究所，东京大学）；Theory Center, IPNS, KEK（IPNS理论中心，KEK）；Graduate University for Advanced Studies (SOKENDAI)（高级研究大学（SOKENDAI））
A normalizing flow approach for the inference of star cluster properties from unresolved broadband photometry I: Comparison to spectral energy distribution fitting
Universität Heidelberg（海德堡大学）；Université Côte d’Azur（蔚蓝海岸大学）；Universidad Nacional Autónoma de México（墨西哥国立自治大学）；INAF – Arcetri Astrophysical Observatory（意大利国家天体物理研究所阿切特里天文台）；University of Wyoming（怀俄明大学）；Australian National University（澳大利亚国立大学）；The Johns Hopkins University（约翰斯·霍普金斯大学）
Emulating the complex galactic-scale orbital dynamics of LISA massive black hole pairs with normalizing flows
Université de Genève（日内瓦大学）；ETH Zurich（苏黎世联邦理工学院）；Sapienza, Università di Roma（罗马大学）；INAF, Osservatorio Astronomico di Roma（意大利国家天体物理研究所罗马天文台）；INFN, Sezione di Roma I（意大利国家核物理研究所罗马第一分部）；INAF, Osservatorio Astronomico di Padova（意大利国家天体物理研究所帕多瓦天文台）；Università degli Studi di Milano-Bicocca（米兰比可卡大学）；INFN, Sezione di Milano-Bicocca（意大利国家核物理研究所米兰比可卡分部）；INAF, Osservatorio Astronomico di Brera（意大利国家天体物理研究所布雷拉天文台）
Universidad Técnica Federico Santa María（费德里科·圣玛丽亚技术大学）；Argonne National Laboratory（阿贡国家实验室）；Berkeley Center for Cosmological Physics, University of California, Berkeley（加州大学伯克利分校宇宙学物理中心）；Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）；Universidade de São Paulo（圣保罗大学）；Universidad Andres Bello（安德烈斯贝略大学）
LIMFAST. IV. Learning High-Redshift Galaxy Formation from Multiline Intensity Mapping with Implicit Likelihood Inference
Northwestern University（西北大学）；NSF--Simons AI Institute for the Sky (SkAI)（美国国家科学基金会-西蒙斯天空人工智能研究所）；University of Pennsylvania（宾夕法尼亚大学）；Jet Propulsion Laboratory, California Institute of Technology（加州理工学院喷气推进实验室）；California Institute of Technology（加州理工学院）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）
The z = 9.625 Cosmic Gems Galaxy was a "Compact Blue Monster" Propelled by Massive Star Clusters
INAF – OAS, Osservatorio di Astrofisica e Scienza dello Spazio di Bologna（意大利国家天体物理研究所-天文与空间科学观测所博洛尼亚）；Department of Astronomy, Oskar Klein Centre, Stockholm University（斯德哥尔摩大学奥谢尔克莱因中心天文学系）；Center for Frontier Science, Chiba University（千叶大学前沿科学中心）；Department of Physics, Graduate School of Science, Chiba University（千叶大学理学研究科物理学系）；Department of Astronomy, University of Michigan（密歇根大学天文学系）；Space Telescope Science Institute (STScI)（太空望远镜科学研究所）；Dipartimento di Fisica, Università degli Studi di Milano（米兰大学物理系）
