17 citations · 17 across the 1 of their papers we have counts for
8 papers
Weak lensing mass reconstruction using sparsity and a Gaussian random field
J. -L. Starck, K. E. Themelis, N. Jeffrey +2
We introduce a novel approach to reconstruct dark matter mass maps from weak gravitational lensing measurements. The cornerstone of the proposed method lies in a new modelling of t…
Constraining neutrino masses with weak-lensing multiscale peak counts
Virginia Ajani, Austin Peel, Valeria Pettorino +3
Massive neutrinos influence the background evolution of the Universe as well as the growth of structure. Being able to model this effect and constrain the sum of their masses is on…
The impact of baryonic physics and massive neutrinos on weak lensing peak statistics
Matthew Fong, Miyoung Choi, Victoria Catlett +5
We study the impact of baryonic processes and massive neutrinos on weak lensing peak statistics that can be used to constrain cosmological parameters. We use the BAHAMAS suite of c…
The Role of Machine Learning in the Next Decade of Cosmology
Michelle Ntampaka, Camille Avestruz, Steven Boada +27
In recent years, machine learning (ML) methods have remarkably improved how cosmologists can interpret data. The next decade will bring new opportunities for data-driven cosmologic…
Distinguishing standard and modified gravity cosmologies with machine learning
Austin Peel, Florian Lalande, Jean-Luc Starck +5
We present a convolutional neural network to classify distinct cosmological scenarios based on the statistically similar weak-lensing maps they generate. Modified gravity (MG) mode…
On the dissection of degenerate cosmologies with machine learning
Julian Merten, Carlo Giocoli, Marco Baldi +5
Based on the DUSTGRAIN-pathfinder suite of simulations, we investigate observational degeneracies between nine models of modified gravity and massive neutrinos. Three types of mach…