181 citations · 377 across the 22 of their papers we have counts for
6 papers · 1 filter
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…
Psi-GAN: A power-spectrum-informed generative adversarial network for the emulation of large-scale structure maps across cosmologies and redshifts
Prabh Bhambra, Benjamin Joachimi, Ofer Lahav +1
Simulations of the dark matter distribution throughout the Universe are essential in order to analyse data from cosmological surveys. -body simulations are computationally expen…
Deep learning insights into non-universality in the halo mass function
Ningyuan Guo, Luisa Lucie-Smith, Hiranya V. Peiris +2
The abundance of dark matter haloes is a key cosmological probe in forthcoming galaxy surveys. The theoretical understanding of the halo mass function (HMF) is limited by our incom…
The future of cosmological likelihood-based inference: accelerated high-dimensional parameter estimation and model comparison
Davide Piras, Alicja Polanska, Alessio Spurio Mancini +2
We advocate for a new paradigm of cosmological likelihood-based inference, leveraging recent developments in machine learning and its underlying technology, to accelerate Bayesian…
Learned harmonic mean estimation of the Bayesian evidence with normalizing flows
Alicja Polanska, Matthew A. Price, Davide Piras +2
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…