181 citations · 265 across the 10 of their papers we have counts for
11 papers
KiDS-DR5: Exploring dust transport in dark matter halos of galaxies
Eray Genc, Angus H. Wright, Hendrik Hildebrandt +12
We analyse a well-defined sample of galaxies from the final data release of the Kilo-Degree Survey (KiDS-DR5), selected using magnitude, photometric redshift, and stellar mass cuts…
A guide to choosing data compression methods for cosmological inference
Susan Pyne, Benjamin Joachimi, Calum Gordon +1
We provide a pedagogical guide to help researchers choose an appropriate compression method for cosmological inference and numerical covariance estimation, trading off the balance…
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…
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…
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…
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…
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…
Relations between three-point configuration space shear and convergence statistics
X. Shi, P. Schneider, B. Joachimi
With the growing interest in and ability of using weak lensing studies to probe the non-Gaussian properties of the matter density field, there is an increasing need for the study o…
Controlling intrinsic-shear alignment in three-point weak lensing statistics
X. Shi, B. Joachimi, P. Schneider
Three-point weak lensing statistics provide cosmic information complementary to that of two-point statistics. However, both statistics suffer from intrinsic-shear alignment, which…