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Journal Article
CoLFI: cosmological likelihood-free inference with neural density estimators
Author(s): Wang, Guo-jian; Cheng, Cheng; Ma, Yin-zhe; Xia, Jun-qing; Abebe, Amare; Beesham, Aroonkumar
Attachments [1]
Issue Date: 2023
Language: en
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Abstract: 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 ( ) 102 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(θ|d) using samples generated by models, and the posterior distribution p(θ|d0) can be obtained for a given observational data d0. 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 ( ) s - 10 2 . The method can be extended to higher-dimensional data.
Citation: Wang, G.J., Cheng, C., Ma, Y.Z., Xia, J.Q., Abebe, A. and Beesham, A., 2023. CoLFI: cosmological likelihood-free inference with neural density estimators. The Astrophysical Journal Supplement Series, 268(1), pp.1-20.
Other: https://doi.org/10.3847/1538-4365/ace113
Journal Title: The Astrophysical Journal Supplement Series
Journal Issue / Part Number: 268 / 7
Peer reviewed: Yes
International Conference: issn
Keynote / Plenary: issn
In Proceedings: issn
ISSN: 1538-4365 (online)
Other: https://doi.org/10.3847/1538-4365/ace113
Publisher: American Astronomical Society
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