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Found 4 papers, 2 papers with code
Fixed-kinetic Neural Hamiltonian Flows for enhanced interpretability and reduced complexity
Normalizing Flows (NF) are Generative models which are particularly robust and allow for exact sampling of the learned distribution.
Neural physical engines for inferring the halo mass distribution function
Here we demonstrate a method for determining the halo mass distribution function by learning the tracer bias between density fields and halo catalogues using a neural bias model.
Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics
Primordial power spectrum and cosmology from black-box galaxy surveys
We propose a new, likelihood-free approach to inferring the primordial matter power spectrum and cosmological parameters from arbitrarily complex forward models of galaxy surveys where all relevant statistics can be determined from numerical simulations, i. e. black-boxes.
Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics
A rigorous EFT-based forward model for large-scale structure
We further show that the information captured by this likelihood is equivalent to the combination of the next-to-leading order galaxy power spectrum, leading-order bispectrum, and BAO reconstruction.
Cosmology and Nongalactic Astrophysics
