Search Results for author:
Found 6 papers, 5 papers with code
The Cosmic Graph: Optimal Information Extraction from Large-Scale Structure using Catalogues
We a) demonstrate the high sensitivity of modular graph structure to the underlying cosmology in the noise-free limit, b) show that graph neural network summaries automatically combine mass and clustering information through comparisons to traditional statistics, c) demonstrate that networks can still extract information when catalogues are subject to noisy survey cuts, and d) illustrate how nonlinear IMNN summaries can be used as asymptotically optimal compressed statistics for Bayesian simulation-based inference.
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
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
Marginal Likelihoods from Monte Carlo Markov Chains
In this paper, we present a method for computing the marginal likelihood, also known as the model likelihood or Bayesian evidence, from Markov Chain Monte Carlo (MCMC), or other sampled posterior distributions.
Computation Cosmology and Nongalactic Astrophysics
An accurate halo model for fitting non-linear cosmological power spectra and baryonic feedback models
We are able to fit all feedback models investigated at the 5 per cent level using only two free parameters, and we place limits on the range of these halo parameters for feedback models investigated by the OWLS simulations.
Cosmology and Nongalactic Astrophysics
Massive Lossless Data Compression and Multiple Parameter Estimation from Galaxy Spectra
We show that, if the noise in the data is independent of the parameters, we can form $M$ linear combinations of the data which contain as much information about all the parameters as the entire dataset, in the sense that the Fisher information matrices are identical; i. e. the method is lossless.
astro-ph Rings and Algebras Data Analysis, Statistics and Probability
