52 citations · 248 across the 20 of their papers we have counts for
20 papers
Dark Energy Survey Year 3 results: optimized CDM simulation-based inference with weak lensing map-level hybrid statistics
J. Williamson, T. L. Makinen, N. Porqueres +85
We present cosmological constraints from the Dark Energy Survey Year 3 (DES Y3) weak lensing data using hierarchical hybrid statistics within a Bayesian simulation-based inference…
Nonparametric Variational Inference Reconstruction of the Cosmic Expansion History from SNe Ia -- the charm2 code
Iason Saganas, Matteo Guardiani, Natalia Porqueres +1
Cosmological analyses using the latest set of type Ia SNe data weakly favor an evolving dark energy (EDE) model without strongly disfavoring the standard LCDM paradigm. Nonparametr…
Differentiable Forward Modeling for Efficient and Accurate Shear Inference
Ismael Mendoza, Axel Guinot, Matthew R. Becker +6
Forthcoming Stage-IV dark energy optical surveys, such as LSST, have the ambitious goal of measuring cosmological parameters at sub-percent precision. Realizing their full scientif…
Bias from small-scale leakage in Pulsar Timing Array maps
Federico Semenzato, Nicola Bellomo, Alvise Raccanelli +1
Pulsar Timing Array experiments are rapidly approaching the era of gravitational wave background anisotropy detection. The timing residuals of each pulsar are an integrated measure…
Euclid preparation. LXXXV. Toward a DR1 application of higher-order weak lensing statistics
Euclid Collaboration, S. Vinciguerra, F. Bouchè +284
This is the second paper in the HOWLS (higher-order weak lensing statistics) series exploring the usage of non-Gaussian statistics for cosmology inference within Euclid. With respe…
Euclid: Field-level inference of primordial non-Gaussianity and cosmic initial conditions
A. Andrews, J. Jasche, G. Lavaux +147
A primary target of the \Euclid space mission is to constrain early-universe physics by searching for deviations from a primordial Gaussian random field. A significant detection of…
Hybrid Summary Statistics
T. Lucas Makinen, Ce Sui, Benjamin D. Wandelt +2
We present a way to capture high-information posteriors from training sets that are sparsely sampled over the parameter space for robust simulation-based inference. In physical inf…
Hybrid summary statistics: neural weak lensing inference beyond the power spectrum
T. Lucas Makinen, Alan Heavens, Natalia Porqueres +3
In inference problems, we often have domain knowledge which allows us to define summary statistics that capture most of the information content in a dataset. In this paper, we pres…
Accuracy requirements on intrinsic alignments for Stage-IV cosmic shear
Anya Paopiamsap, Natalia Porqueres, David Alonso +2
In the context of cosmological weak lensing studies, intrinsic alignments (IAs) are one the most complicated astrophysical systematic to model, given the poor understanding of the…
DISCO-DJ I: a differentiable Einstein-Boltzmann solver for cosmology
Oliver Hahn, Florian List, Natalia Porqueres
We present the Einstein-Boltzmann module of the DISCO-DJ (DIfferentiable Simulations for COsmology - Done with JAX) software package. This module implements a fully differentiable…
Field-level inference of cosmic shear with intrinsic alignments and baryons
Natalia Porqueres, Alan Heavens, Daniel Mortlock +2
We construct a field-based Bayesian Hierarchical Model for cosmic shear that includes, for the first time, the important astrophysical systematics of intrinsic alignments and baryo…
LyAl-Net: A high-efficiency Lyman- forest simulation with a neural network
Chotipan Boonkongkird, Guilhem Lavaux, Sebastien Peirani +3
The inference of cosmological quantities requires accurate and large hydrodynamical cosmological simulations. Unfortunately, their computational time can take millions of CPU hours…
Lifting weak lensing degeneracies with a field-based likelihood
Natalia Porqueres, Alan Heavens, Daniel Mortlock +1
We present a field-based approach to the analysis of cosmic shear data to infer jointly cosmological parameters and the dark matter distribution. This forward modelling approach sa…
Bayesian forward modelling of cosmic shear data
Natalia Porqueres, Alan Heavens, Daniel Mortlock +1
We present a Bayesian hierarchical modelling approach to infer the cosmic matter density field, and the lensing and the matter power spectra, from cosmic shear data. This method us…
A hierarchical field-level inference approach to reconstruction from sparse Lyman- forest data
Natalia Porqueres, Oliver Hahn, Jens Jasche +1
We address the problem of inferring the three-dimensional matter distribution from a sparse set of one-dimensional quasar absorption spectra of the Lyman- forest. Using a Bayesi…
Inferring high redshift large-scale structure dynamics from the Lyman-alpha forest
Natalia Porqueres, Jens Jasche, Guilhem Lavaux +1
One of the major science goals over the coming decade is to test fundamental physics with probes of the cosmic large-scale structure out to high redshift. Here we present a fully B…
Explicit Bayesian treatment of unknown foreground contaminations in galaxy surveys
Natalia Porqueres, Doogesh Kodi Ramanah, Jens Jasche +1
The treatment of unknown foreground contaminations will be one of the major challenges for galaxy clustering analyses of coming decadal surveys. These data contaminations introduce…
Imprints of the large-scale structure on AGN formation and evolution
Natàlia Porqueres, Jens Jasche, Torsten A. Enßlin +1
Black hole masses are found to correlate with several global properties of their host galaxies, suggesting that black holes and galaxies have an intertwined evolution and that acti…
NIFTy 3 - Numerical Information Field Theory - A Python framework for multicomponent signal inference on HPC clusters
Theo Steininger, Jait Dixit, Philipp Frank +10
NIFTy, "Numerical Information Field Theory", is a software framework designed to ease the development and implementation of field inference algorithms. Field equations are formulat…
Cosmic expansion history from SNe Ia data via information field theory -- the charm code
Natàlia Porqueres, Torsten A. Enßlin, Maksim Greiner +4
We present charm (cosmic history agnostic reconstruction method), a novel inference algorithm that reconstructs the cosmic expansion history as encoded in the Hubble parameter $H(z…