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Lossless, scalable implicit likelihood inference for cosmological fields
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DOI：10.1088/1475-7516/2021/11/049.png)
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We present a comparison of simulation-based inference to full, field-based ana-lytical inference in cosmological data analysis. To do so, we explore parameter inference for two cases where the information content is calculable analytically: Gaussian random fields whose covariance depends on parameters through the power spectrum; and correlated log-normal fields with cosmological power spectra. We compare two inference techniques: i) ex-plicit field-level inference using the known likelihood and ii) implicit likelihood inference with maximally informative summary statistics compressed via Information Maximising Neural Networks (IMNNs). We find that a) summaries obtained from convolutional neural network compression do not lose information and therefore saturate the known field information con -tent, both for the Gaussian covariance and the lognormal cases, b) simulation-based inference using these maximally informative nonlinear summaries recovers nearly losslessly the exact posteriors of field-level inference, bypassing the need to evaluate expensive likelihoods or invert covariance matrices, and c) even for this simple example, implicit, simulation-based likelihood incurs a much smaller computational cost than inference with an explicit like-lihood. This work uses a new IMNN implementation in Jax that can take advantage of fully-differentiable simulation and inference pipeline. We also demonstrate that a single re-training of the IMNN summaries effectively achieves the theoretically maximal information, enhancing the robustness to the choice of fiducial model where the IMNN is trained.
Keyword:
cosmological parameters from LSS
cosmological simulations
dark matter simulations
power spectrum
期刊
IF:
5.9
论文数:
1.3W
被引数:
4.7W
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