181 citations · 377 across the 22 of their papers we have counts for
16 papers · 1 filter
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…
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…
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…
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…
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…