We extend JAX with the capability to automatically differentiate higher-order functions
(functionals and operators). By representing functions as infinite dimensional generalization …

We present DE-VAE, a variational autoencoder (VAE) architecture to search for a
compressed representation of dynamical dark energy (DE) models in observational studies …

Abstract We present the Einstein-Boltzmann module of the Disco-Dj (DIfferentiable
Simulations for COsmology—Done with J ax) software package. This module implements a …

Many areas of science and engineering encounter data defined on spherical manifolds.
Modelling and analysis of spherical data often necessitates spherical harmonic transforms …

We advocate for a new paradigm of cosmological likelihood-based inference, leveraging
recent developments in machine learning and its underlying technology, to accelerate …

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 …

We develop a set of machine-learning-based cosmological emulators, to obtain fast model
predictions for the C (ℓ) angular power spectrum coefficients, characterizing tomographic …

With the rapid advance of wide-field surveys it is increasingly important to perform combined
cosmological probe analyses. We present a new pipeline for simulation-based multi-probe …

We present candl, an automatically differentiable python likelihood for analysing cosmic
microwave background power spectrum measurements. candl is powered by JAX, which …

We assess the usefulness of gradient-based samplers, such as the No-U-Turn Sampler
(NUTS), by comparison with traditional Metropolis-Hastings algorithms, in tomographic …