CoLFI: Cosmological Likelihood-free Inference with Neural Density Estimators
文献类型：期刊论文
| 作者 | Wang, Guo-Jian7,8,9; Cheng, Cheng6,8,9 ; Ma, Yin-Zhe5,7,8,9; Xia, Jun-Qing4; Abebe, Amare3,7; Beesham, Aroonkumar1,2,7
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| 刊名 | ASTROPHYSICAL JOURNAL SUPPLEMENT SERIES
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| 出版日期 | 2023-09-01 |
| 卷号 | 268期号:1页码:20 |
| ISSN号 | 0067-0049 |
| DOI | 10.3847/1538-4365/ace113 |
| 通讯作者 | Ma, Yin-Zhe(ma@ukzn.ac.za) |
| 英文摘要 | In previous works, we proposed to estimate cosmological parameters with an artificial neural network (ANN) and a mixture density network (MDN). In this work, we propose an improved method called a mixture neural network (MNN) to achieve parameter estimation by combining ANN and MDN, which can overcome shortcomings of the ANN and MDN methods. Besides, we propose sampling parameters in a hyperellipsoid for the generation of the training set, which makes the parameter estimation more efficient. A high-fidelity posterior distribution can be obtained using O(10(2)) forward simulation samples. In addition, we develop a code named CoLFI for parameter estimation, which incorporates the advantages of MNN, ANN, and MDN, and is suitable for any parameter estimation of complicated models in a wide range of scientific fields. CoLFI provides a more efficient way for parameter estimation, especially for cases where the likelihood function is intractable or cosmological models are complex and resource-consuming. It can learn the conditional probability density p(?|d(0)) using samples generated by models, and the posterior distribution p(?|d(0)) can be obtained for a given observational data d(0). We tested the MNN using power spectra of the cosmic microwave background and Type Ia supernovae and obtained almost the same result as the Markov Chain Monte Carlo method. The numerical difference only exists at the level of 0 (10(-2)s ). The method can be extended to higher-dimensional data. |
| WOS关键词 | APPROXIMATE BAYESIAN COMPUTATION ; REIONIZATION PARAMETERS ; CONSTRAINTS ; IA |
| 资助项目 | National Research Foundation[150580] ; National Research Foundation[120385] ; National Research Foundation[120378] ; National Science Foundation of China[U1931202] |
| WOS研究方向 | Astronomy & Astrophysics |
| 语种 | 英语 |
| 出版者 | IOP Publishing Ltd |
| WOS记录号 | WOS:001052840400001 |
| 资助机构 | National Research Foundation ; National Science Foundation of China |
| 源URL | [http://ir.xao.ac.cn/handle/45760611-7/5424]  |
| 专题 | 研究单元未命名 |
| 通讯作者 | Ma, Yin-Zhe |
| 作者单位 | 1.Mangosuthu Univ Technol, Fac Nat Sci, POB 12363, ZA-4052 Jacobs, South Africa 2.Univ Zululand, Dept Math Sci, Private Bag X1001, ZA-3886 Kwa Dlangezwa, South Africa 3.North West Univ, Ctr Space Res, ZA-2520 Potchefstroom, South Africa 4.Beijing Normal Univ, Dept Astron, Beijing 100875, Peoples R China 5.Stellenbosch Univ, Dept Phys, ZA-7602 Matieland, South Africa 6.Chinese Acad Sci, Xinjiang Astron Observ, Urumqi 830011, Peoples R China 7.Natl Inst Theoret & Comp Sci NITheCS, Johannesburg, South Africa 8.Univ KwaZulu Natal, NAOC UKZN Comp Astrophys Ctr NUCAC, ZA-4000 Durban, South Africa 9.Univ KwaZulu Natal, Sch Chem & Phys, Westville Campus Private Bag X54001, ZA-4000 Durban, South Africa |
| 推荐引用方式 GB/T 7714 | Wang, Guo-Jian,Cheng, Cheng,Ma, Yin-Zhe,et al. CoLFI: Cosmological Likelihood-free Inference with Neural Density Estimators[J]. ASTROPHYSICAL JOURNAL SUPPLEMENT SERIES,2023,268(1):20. |
| APA | Wang, Guo-Jian,Cheng, Cheng,Ma, Yin-Zhe,Xia, Jun-Qing,Abebe, Amare,&Beesham, Aroonkumar.(2023).CoLFI: Cosmological Likelihood-free Inference with Neural Density Estimators.ASTROPHYSICAL JOURNAL SUPPLEMENT SERIES,268(1),20. |
| MLA | Wang, Guo-Jian,et al."CoLFI: Cosmological Likelihood-free Inference with Neural Density Estimators".ASTROPHYSICAL JOURNAL SUPPLEMENT SERIES 268.1(2023):20. |
入库方式： OAI收割
来源：新疆天文台
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