Tomographic three-dimensional 21 cm images from the epoch of reionization contain a wealth of information about the reionization of the intergalactic medium by astrophysical sources. Conventional power spectrum analysis cannot exploit the full information in the 21 cm data because the 21 cm signal is highly non-Gaussian due to reionization patchiness. We perform a Bayesian inference of the reionization parameters where the likelihood is implicitly defined through forward simulations using density estimation likelihood-free inference (DELFI). We adopt a trained 3D Convolutional Neural Network (CNN) to compress the 3D image data into informative summaries (DELFI-3D CNN). We show that this method recovers accurate posterior distributions for the reionization parameters. Our approach outperforms earlier analysis based on two-dimensional 21 cm images. In contrast, an MCMC analysis of the 3D lightcone-based 21 cm power spectrum alone and using a standard explicit likelihood approximation results in less accurate credible parameter regions than inferred by the DELFI-3D CNN, both in terms of the location and shape of the contours. Our proof-of-concept study implies that the DELFI-3D CNN can effectively exploit more information in the 3D 21 cm images than a 2D CNN or power spectrum analysis. This technique can be readily extended to include realistic effects and is therefore a promising approach for the scientific interpretation of future 21 cm observation data.
TORRCH: Tomographic reconstruction of the reionization of cosmic hydrogen with Ly$α$ emitters and non-Ly$α$-selected galaxies
Tata Institute of Fundamental Research（塔塔基础研究所）；IIT Kanpur（坎普尔印度理工学院）；KU Leuven（荷语鲁汶大学）；Istituto Nazionale di Astrofisica -Osservatorio Astronomico di Trieste（意大利国家天体物理研究所-的里雅斯特天文台）；SISSA International School for Advanced Studies（国际高等研究院）；INFN - Sezione di Trieste（意大利国家核物理研究所-的里雅斯特分部）；IFPU, Institute for Fundamental Physics of the Universe（宇宙基础物理研究所）；ICSC - Centro Nazionale di Ricerca in High Performance Computing, Big Data e Quantum Computing（高性能计算、大数据与量子计算国家研究中心）；Kavli Institute for Cosmology and Institute of Astronomy（卡弗里宇宙学研究所和天文学研究所）；University of Nottingham（诺丁汉大学）；Institute for Astronomy, University of Edinburgh（爱丁堡大学天文学研究所）
Interpreting the HI 21-cm cosmology maps through Largest Cluster Statistics III: Impact of the lightcone effect
Indian Institute of Technology Indore（印度印多尔理工学院）；Stellenbosch University（斯泰伦博斯大学）；University of Groningen（格罗宁根大学）；The Open University of Israel（以色列开放大学）
Simulation-based Inference of Reionization Parameters from 3D Tomographic 21 cm Light-cone Images -- II: Application of Solid Harmonic Wavelet Scattering Transform
Stacking 21-cm Maps around Lyman-$α$ Emitters during Reionization: Prospects for a Cross-correlation Detection with the Hydrogen Epoch of Reionization Array
Imaging the 21-cm Signal from the Cosmic Dawn & Epoch of Reionization and the Connection with the Global Signal
Technical University of Munich（慕尼黑工业大学）；École Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院）；ETH Zurich（苏黎世联邦理工学院）；Indian Institute of Technology Madras（马德拉斯理工学院）；University of Cambridge（剑桥大学）；Indian Institute of Technology Indore（印多尔理工学院）；Jadavpur University（贾达普大学）；The Open University of Israel（以色列开放大学）；University of Groningen（格罗宁根大学）；Université PSL（巴黎文理研究大学）；Sorbonne Université（索邦大学）；Université Paris Cité（巴黎西岱大学）；Shanghai Astronomical Observatory, Chinese Academy of Sciences（中国科学院上海天文台）
