181 citations · 377 across the 22 of their papers we have counts for
22 papers
Nonlinear velocity power spectrum: modeling the cosmological dependence on the Hubble constant and cold dark matter density
Francesca Lepori, Ruth Durrer, Martin Kunz +3
In this paper we present a semi-analytical model for the velocity power spectrum in $\La$CDM cosmology for wave numbers Mpc. We mainly concentrate on the dominant divergence…
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…
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…
Anchors no more: Using peculiar velocities to constrain and the primordial Universe without calibrators
Davide Piras, Francesco Sorrenti, Ruth Durrer +1
We develop a novel approach to constrain the Hubble parameter and the primordial power spectrum amplitude using type Ia supernovae (SNIa) data. By considering…
Constraining the primordial power spectrum using a differentiable likelihood
Subarna Chaki, Andrina Nicola, Alessio Spurio Mancini +2
The simplest inflationary models predict the primordial power spectrum (PPS) of curvature perturbations to be nearly scale-invariant. However, various other models of inflation pre…
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…
A representation learning approach to probe for dynamical dark energy in matter power spectra
Davide Piras, Lucas Lombriser
We present DE-VAE, a variational autoencoder (VAE) architecture to search for a compressed representation of dynamical dark energy (DE) models in observational studies of the cosmi…
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…
A robust estimator of mutual information for deep learning interpretability
Davide Piras, Hiranya V. Peiris, Andrew Pontzen +3
We develop the use of mutual information (MI), a well-established metric in information theory, to interpret the inner workings of deep learning models. To accurately estimate MI f…
Fast and realistic large-scale structure from machine-learning-augmented random field simulations
Davide Piras, Benjamin Joachimi, Francisco Villaescusa-Navarro
Producing thousands of simulations of the dark matter distribution in the Universe with increasing precision is a challenging but critical task to facilitate the exploitation of cu…
Discovering the building blocks of dark matter halo density profiles with neural networks
Luisa Lucie-Smith, Hiranya V. Peiris, Andrew Pontzen +3
The density profiles of dark matter halos are typically modeled using empirical formulae fitted to the density profiles of relaxed halo populations. We present a neural network mod…
COSMOPOWER: emulating cosmological power spectra for accelerated Bayesian inference from next-generation surveys
A. Spurio Mancini, D. Piras, J. Alsing +2
We present , a suite of neural cosmological power spectrum emulators providing orders-of-magnitude acceleration for parameter estimation from two-point statistics…
Towards fast machine-learning-assisted Bayesian posterior inference of microseismic event location and source mechanism
Davide Piras, Alessio Spurio Mancini, Ana M. G. Ferreira +2
Bayesian inference applied to microseismic activity monitoring allows the accurate location of microseismic events from recorded seismograms and the estimation of the associated un…
Representation Learning for High-Dimensional Data Collection under Local Differential Privacy
Alex Mansbridge, Gregory Barbour, Davide Piras +4
The collection of individuals' data has become commonplace in many industries. Local differential privacy (LDP) offers a rigorous approach to preserving privacy whereby the individ…
Accelerating Bayesian microseismic event location with deep learning
A. Spurio Mancini, D. Piras, A. M. G. Ferreira +2
We present a series of new open source deep learning algorithms to accelerate Bayesian full waveform point source inversion of microseismic events. Inferring the joint posterior pr…
The mass dependence of dark matter halo alignments with large-scale structure
Davide Piras, Benjamin Joachimi, Björn Malte Schäfer +3
Tidal gravitational forces can modify the shape of galaxies and clusters of galaxies, thus correlating their orientation with the surrounding matter density field. We study the dep…