1.5k citations · 4.8k across the 52 of their papers we have counts for
34 papers · 1 filter
A simulation-based inference pipeline for cosmic shear with the Kilo-Degree Survey
Kiyam Lin, Maximilian von Wietersheim-Kramsta, Benjamin Joachimi +1
The standard approach to inference from cosmic large-scale structure data employs summary statistics that are compared to analytic models in a Gaussian likelihood with pre-computed…
Unbiased likelihood-free inference of the Hubble constant from light standard sirens
Francesca Gerardi, Stephen M. Feeney, Justin Alsing
Multi-messenger observations of binary neutron star mergers offer a promising path towards resolution of the Hubble constant () tension, provided their constraints are shown t…
Prospects for Measuring the Hubble Constant with Neutron-Star-Black-Hole Mergers
Stephen M. Feeney, Hiranya V. Peiris, Samaya M. Nissanke +1
Gravitational wave (GW) and electromagnetic (EM) observations of neutron-star-black-hole (NSBH) mergers can provide precise local measurements of the Hubble constant (), ideal…
Cosmic Shear: Inference from Forward Models
Peter L. Taylor, Thomas D. Kitching, Justin Alsing +3
Density-estimation likelihood-free inference (DELFI) has recently been proposed as an efficient method for simulation-based cosmological parameter inference. Compared to the standa…
Fast likelihood-free cosmology with neural density estimators and active learning
Justin Alsing, Tom Charnock, Stephen Feeney +1
Likelihood-free inference provides a framework for performing rigorous Bayesian inference using only forward simulations, properly accounting for all physical and observational eff…
Unbiased Hubble constant estimation from binary neutron star mergers
Daniel J. Mortlock, Stephen M. Feeney, Hiranya V. Peiris +2
Gravitational wave (GW) observations of binary neutron star (BNS) mergers can be used to measure luminosity distances and hence, when coupled with estimates for the mergers' host r…