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Found 7 papers, 4 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.
Bayesian forward modelling of cosmic shear data
In this case, the density field samples are generated with a power spectrum that deviates from the prior, and the method recovers the true lensing power spectrum.
Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics
Perfectly parallel cosmological simulations using spatial comoving Lagrangian acceleration
Existing cosmological simulation methods lack a high degree of parallelism due to the long-range nature of the gravitational force, which limits the size of simulations that can be run at high resolution.
Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics
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
Painting halos from 3D dark matter fields using Wasserstein mapping networks
We present a novel halo painting network that learns to map approximate 3D dark matter fields to realistic halo distributions.
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
Automatic physical inference with information maximising neural networks
We anticipate that the automatic physical inference method described in this paper will be essential to obtain both accurate and precise cosmological parameter estimates from complex and large astronomical data sets, including those from LSST and Euclid.
Instrumentation and Methods for Astrophysics
