35 citations · 56 across the 12 of their papers we have counts for
12 papers
Differentiable astrophysics at scale: solving and differentiating ODE ensembles on the GPU
A. Spurio Mancini
Astronomers increasingly fit their models with gradient-based methods, such as Hamiltonian Monte Carlo, which need the derivatives of the model with respect to its parameters. In m…
GRADSOLVE: fast exact gradients for ODE ensembles on GPUs
Alessio Spurio Mancini
Ordinary differential equations (ODEs) underlie models in science and engineering, and many applications need derivatives of their solutions with respect to parameters. Ensembles o…
High-Dimensional Bayesian Model Comparison in Cosmology with GPU-accelerated Nested Sampling and Neural Emulators
Toby Lovick, David Yallup, Davide Piras +2
We demonstrate a GPU-accelerated nested sampling framework for efficient high-dimensional Bayesian inference in cosmology. Using JAX-based neural emulators and likelihoods for cosm…
CLASS_SZ II: Notes and Examples of Fast and Accurate Calculations of Halo Model, Large Scale Structure and Cosmic Microwave Background Observables
Boris Bolliet, Aleksandra Kusiak, Fiona McCarthy +16
These notes are very much work-in-progress and simply intended to showcase, in various degrees of details (and rigour), some of the cosmology calculations that class_sz can do. We…
Savage-Dickey density ratio estimation with normalizing flows for Bayesian model comparison
Kiyam Lin, Alicja Polanska, Davide Piras +2
A core motivation of science is to evaluate which scientific model best explains observed data. Bayesian model comparison provides a principled statistical approach to comparing sc…
Transfer learning for multifidelity simulation-based inference in cosmology
Alex A. Saoulis, Davide Piras, Niall Jeffrey +3
Simulation-based inference (SBI) enables cosmological parameter estimation when closed-form likelihoods or models are unavailable. However, SBI relies on machine learning for neura…
Full-waveform earthquake source inversion using simulation-based inference
A. A. Saoulis, D. Piras, A. Spurio Mancini +2
This paper presents a novel framework for full-waveform seismic source inversion using simulation-based inference (SBI). Traditional probabilistic approaches often rely on simplify…
Testing interacting dark energy with Stage IV cosmic shear surveys through differentiable neural emulators
Karim Carrion, Alessio Spurio Mancini, Davide Piras +1
We employ a novel framework for accelerated cosmological inference, based on neural emulators and gradient-based sampling methods, to forecast constraints on dark energy models fro…
A complete framework for cosmological emulation and inference with CosmoPower
H. T. Jense, I. Harrison, E. Calabrese +4
We present a coherent, re-usable python framework which further builds on the cosmological emulator code CosmoPower. In the current era of high-precision cosmology, we require high…
CosmoPower-JAX: high-dimensional Bayesian inference with differentiable cosmological emulators
D. Piras, A. Spurio Mancini
We present CosmoPower-JAX, a JAX-based implementation of the CosmoPower framework, which accelerates cosmological inference by building neural emulators of cosmological power spect…
On the degeneracies between baryons, massive neutrinos and f(R) gravity in Stage IV cosmic shear analyses
A. Spurio Mancini, B. Bose
Modelling nonlinear structure formation is essential for current and forthcoming cosmic shear experiments. We combine the halo model reaction formalism, implemented in the REACT co…
Machine learning assisted Bayesian model comparison: learnt harmonic mean estimator
Jason D. McEwen, Christopher G. R. Wallis, Matthew A. Price +1
We resurrect the infamous harmonic mean estimator for computing the marginal likelihood (Bayesian evidence) and solve its problematic large variance. The marginal likelihood is a k…