Dark scattering: accelerated constraints from KiDS-1000 with ReACT and CosmoPower
We present constraints on the dark scattering model through cosmic shear measurements
from the Kilo Degree Survey (KiDS-1000), using an accelerated pipeline with novel …
from the Kilo Degree Survey (KiDS-1000), using an accelerated pipeline with novel …
Representation learning approach to probe for dynamical dark energy in matter power spectra
We present DE-VAE, a variational autoencoder (VAE) architecture to search for a
compressed representation of dynamical dark energy (DE) models in observational studies …
compressed representation of dynamical dark energy (DE) models in observational studies …
Automatic Functional Differentiation in JAX
M Lin - The Twelfth International Conference on Learning …, 2023 - openreview.net
We extend JAX with the capability to automatically differentiate higher-order functions
(functionals and operators). By representing functions as infinite dimensional generalization …
(functionals and operators). By representing functions as infinite dimensional generalization …
Cosmic cartography: Bayesian reconstruction of the galaxy density informed by large-scale structure
The dark sirens method combines gravitational waves and catalogs of galaxies to constrain
the cosmological expansion history, merger rates and mass distributions of compact objects …
the cosmological expansion history, merger rates and mass distributions of compact objects …
jaxspec: A fast and robust Python library for X-ray spectral fitting
S Dupourqué, D Barret, CM Diez, S Guillot… - Astronomy & …, 2024 - aanda.org
Context. Inferring spectral parameters from X-ray data is one of the cornerstones of high-
energy astrophysics, and is achieved using software stacks that have been developed over …
energy astrophysics, and is achieved using software stacks that have been developed over …
[HTML][HTML] Differentiable and accelerated spherical harmonic and Wigner transforms
Many areas of science and engineering encounter data defined on spherical manifolds.
Modelling and analysis of spherical data often necessitates spherical harmonic transforms …
Modelling and analysis of spherical data often necessitates spherical harmonic transforms …
The future of cosmological likelihood-based inference: accelerated high-dimensional parameter estimation and model comparison
We advocate for a new paradigm of cosmological likelihood-based inference, leveraging
recent developments in machine learning and its underlying technology, to accelerate …
recent developments in machine learning and its underlying technology, to accelerate …
A complete framework for cosmological emulation and inference with CosmoPower
HT Jense, I Harrison, E Calabrese… - RAS Techniques …, 2025 - academic.oup.com
We present a coherent, re-usable python framework building on the CosmoPower emulator
code for high-accuracy calculations of cosmological observables with Einstein-Boltzmann …
code for high-accuracy calculations of cosmological observables with Einstein-Boltzmann …
Assessment of gradient-based samplers in standard cosmological likelihoods
We assess the usefulness of gradient-based samplers, such as the no-U-turn sampler
(NUTS), by comparison with traditional Metropolis–Hastings (MH) algorithms, in …
(NUTS), by comparison with traditional Metropolis–Hastings (MH) algorithms, in …
Learned harmonic mean estimation of the Bayesian evidence with normalizing flows
We present the learned harmonic mean estimator with normalizing flows-a robust, scalable
and flexible estimator of the Bayesian evidence for model comparison. Since the estimator is …
and flexible estimator of the Bayesian evidence for model comparison. Since the estimator is …