1.5k citations · 4k across the 41 of their papers we have counts for
24 papers · 1 filter
pop-cosmos: Forward modeling KiDS-1000 redshift distributions using realistic galaxy populations
Boris Leistedt, Hiranya V. Peiris, Anik Halder +13
The accuracy of the cosmological constraints from Stage~IV galaxy surveys will be limited by how well the galaxy redshift distributions can be inferred. We have addressed this chal…
Impact of redshift distribution uncertainties on Lyman-break galaxy cosmological parameter inference
Francesco Petri, Boris Leistedt, Daniel J. Mortlock +5
A significant number of Lyman-break galaxies (LBGs) with redshifts 3 < z < 5 are expected to be observed by the upcoming Vera C. Rubin Observatory Legacy Survey of Space and Time (…
pop-cosmos: Scaleable inference of galaxy properties and redshifts with a data-driven population model
Stephen Thorp, Justin Alsing, Hiranya V. Peiris +5
We present an efficient Bayesian method for estimating individual photometric redshifts and galaxy properties under a pre-trained population model (pop-cosmos) that was calibrated…
Dark Energy Survey Year 3 results: likelihood-free, simulation-based CDM inference with neural compression of weak-lensing map statistics
N. Jeffrey, L. Whiteway, M. Gatti +88
We present simulation-based cosmological CDM inference using Dark Energy Survey Year 3 weak-lensing maps, via neural data compression of weak-lensing map summary statistics: pow…
Measuring the thermal and ionization state of the low- IGM using likelihood free inference
Teng Hu, Vikram Khaire, Joseph F. Hennawi +6
We present a new approach to measure the power-law temperature density relationship and the UV background photoionization rate of the IGM base…
Forward modeling of galaxy populations for cosmological redshift distribution inference
Justin Alsing, Hiranya Peiris, Daniel Mortlock +2
We present a forward modeling framework for estimating galaxy redshift distributions from photometric surveys. Our forward model is composed of: a detailed population model describ…
Lossless, Scalable Implicit Likelihood Inference for Cosmological Fields
T. Lucas Makinen, Tom Charnock, Justin Alsing +1
We present a comparison of simulation-based inference to full, field-based analytical inference in cosmological data analysis. To do so, we explore parameter inference for two case…
COSMOPOWER: emulating cosmological power spectra for accelerated Bayesian inference from next-generation surveys
A. Spurio Mancini, D. Piras, J. Alsing +2
We present , a suite of neural cosmological power spectrum emulators providing orders-of-magnitude acceleration for parameter estimation from two-point statistics…
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…
Non-parametric spatial curvature inference using late-universe cosmological probes
Suhail Dhawan, Justin Alsing, Sunny Vagnozzi
Inferring high-fidelity constraints on the spatial curvature parameter, , under as few assumptions as possible, is of fundamental importance in cosmology. We propose a m…
Likelihood-free inference with neural compression of DES SV weak lensing map statistics
Niall Jeffrey, Justin Alsing, François Lanusse
In many cosmological inference problems, the likelihood (the probability of the observed data as a function of the unknown parameters) is unknown or intractable. This necessitates…
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…
Nuisance hardened data compression for fast likelihood-free inference
Justin Alsing, Benjamin Wandelt
In this paper we show how nuisance parameter marginalized posteriors can be inferred directly from simulations in a likelihood-free setting, without having to jointly infer the hig…
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…
Prospects for resolving the Hubble constant tension with standard sirens
Stephen M. Feeney, Hiranya V. Peiris, Andrew R. Williamson +4
The Hubble constant () estimated from the local Cepheid-supernova (SN) distance ladder is in 3- tension with the value extrapolated from cosmic microwave background (CMB) d…
Massive optimal data compression and density estimation for scalable, likelihood-free inference in cosmology
Justin Alsing, Benjamin Wandelt, Stephen Feeney
Many statistical models in cosmology can be simulated forwards but have intractable likelihood functions. Likelihood-free inference methods allow us to perform Bayesian inference f…
Generalized massive optimal data compression
Justin Alsing, Benjamin Wandelt
Data compression has become one of the cornerstones of modern astronomical data analysis, with the vast majority of analyses compressing large raw datasets down to a manageable num…
The Limits of Cosmic Shear
Thomas D. Kitching, Justin Alsing, Alan F. Heavens +3
In this paper we discuss the commonly-used limiting cases, or approximations, for two-point cosmic shear statistics. We discuss the most prominent assumptions in this statistic: th…
Cosmological parameters, shear maps and power spectra from CFHTLenS using Bayesian hierarchical inference
Justin Alsing, Alan F. Heavens, Andrew H. Jaffe
We apply two Bayesian hierarchical inference schemes to infer shear power spectra, shear maps and cosmological parameters from the CFHTLenS weak lensing survey - the first applicat…
Bayesian hierarchical modelling of weak lensing - the golden goal
Alan Heavens, Justin Alsing, Andrew Jaffe +3
To accomplish correct Bayesian inference from weak lensing shear data requires a complete statistical description of the data. The natural framework to do this is a Bayesian Hierar…
Hierarchical Cosmic Shear Power Spectrum Inference
Justin Alsing, Alan Heavens, Andrew H. Jaffe +3
We develop a Bayesian hierarchical modelling approach for cosmic shear power spectrum inference, jointly sampling from the posterior distribution of the cosmic shear field and its…
Weak Lensing with Sizes, Magnitudes and Shapes
Justin Alsing, Donnacha Kirk, Alan Heavens +1
Weak lensing can be observed through a number of effects on the images of distant galaxies; their shapes are sheared, their sizes and fluxes (magnitudes) are magnified and their po…
3D Cosmic Shear: Cosmology from CFHTLenS
T. D. Kitching, A. F. Heavens, J. Alsing +19
This paper presents the first application of 3D cosmic shear to a wide-field weak lensing survey. 3D cosmic shear is a technique that analyses weak lensing in three dimensions usin…
Combining Size and Shape in Weak Lensing
Alan Heavens, Justin Alsing, Andrew Jaffe
Weak lensing alters the size of images with a similar magnitude to the distortion due to shear. Galaxy size probes the convergence field, and shape the shear field, both of which c…