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 …
Representation learning approach to probe for dynamical dark energy in matter power spectra
D Piras, L Lombriser - Physical Review D, 2024 - APS
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 …
DISCO-DJ I: a differentiable Einstein-Boltzmann solver for cosmology
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 …
Simulations for COsmology—Done with J ax) software package. This module implements a …
[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 …
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 …
Fast emulation of two-point angular statistics for photometric galaxy surveys
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 …
predictions for the C (ℓ) angular power spectrum coefficients, characterizing tomographic …
[HTML][HTML] 12× 2 pt combined probes: pipeline, neutrino mass, and data compression
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 …
cosmological probe analyses. We present a new pipeline for simulation-based multi-probe …
candl: cosmic microwave background analysis with a differentiable likelihood
L Balkenhol, C Trendafilova, K Benabed… - Astronomy & …, 2024 - aanda.org
We present candl, an automatically differentiable python likelihood for analysing cosmic
microwave background power spectrum measurements. candl is powered by JAX, which …
microwave background power spectrum measurements. candl is powered by JAX, which …
Assessment of Gradient-Based Samplers in Standard Cosmological Likelihoods
A Mootoovaloo, J Ruiz-Zapatero… - arXiv preprint arXiv …, 2024 - arxiv.org
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 …
(NUTS), by comparison with traditional Metropolis-Hastings algorithms, in tomographic …