Lossless, scalable implicit likelihood inference for cosmological fields

DOI10.1088/1475-7516/2021/11/049zbMath1487.83068arXiv2107.07405OpenAlexW3217622780MaRDI QIDQ5062305

Justin Alsing, Benjamin D. Wandelt, T. Lucas Makinen, Tom Charnock

Publication date: 15 March 2022

Published in: Journal of Cosmology and Astroparticle Physics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2107.07405

zbMATH Keywords

power spectrumcosmological simulationscosmological parameters from LSSdark matter simulations

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Relativistic cosmology (83F05) Analysis of variance and covariance (ANOVA) (62J10) Approximation procedures, weak fields in general relativity and gravitational theory (83C25) Dark matter and dark energy (83C56) Mathematical modeling or simulation for problems pertaining to relativity and gravitational theory (83-10)

Related Items (4)

The power of locality: primordial non-Gaussianity at the map level ⋮ Fast and credible likelihood-free cosmology with truncated marginal neural ratio estimation ⋮ Erratum: Lossless, scalable implicit likelihood inference for cosmological fields ⋮ Analytic correlation of inflationary potential to power spectrum shape: limits of validity, and `no-go' for small field model analytics

Uses Software

• emcee
• Adam
• Inception-v4

Cites Work

• Learning to predict the cosmological structure formation
• Mining gold from implicit models to improve likelihood-free inference
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