385 citations · 968 across the 23 of their papers we have counts for
13 papers · 1 filter
Cosmological multifield emulator
Sambatra Andrianomena, Sultan Hassan, Francisco Villaescusa-Navarro
We demonstrate the use of deep network to learn the distribution of data from state-of-the-art hydrodynamic simulations of the CAMELS project. To this end, we train a generative ad…
HIDM: Emulating Large Scale HI Maps using Score-based Diffusion Models
Sultan Hassan, Sambatra Andrianomena
Efficiently analyzing maps from upcoming large-scale surveys requires gaining direct access to a high-dimensional likelihood and generating large-scale fields with high fidelity, w…
Latent space representations of cosmological fields
Sambatra Andrianomena, Sultan Hassan
We investigate the possibility of learning the representations of cosmological multifield dataset from the CAMELS project. We train a very deep variational encoder on images which…
Invertible mapping between fields in CAMELS
Sambatra Andrianomena, Sultan Hassan, Francisco Villaescusa-Navarro
We build a bijective mapping between different physical fields from hydrodynamic CAMELS simulations. We train a CycleGAN on three different setups: translating dark matter to neutr…
Emulating cosmological multifields with generative adversarial networks
Sambatra Andrianomena, Francisco Villaescusa-Navarro, Sultan Hassan
We explore the possibility of using deep learning to generate multifield images from state-of-the-art hydrodynamic simulations of the CAMELS project. We use a generative adversaria…
Predictive uncertainty on improved astrophysics recovery from multifield cosmology
Sambatra Andrianomena, Sultan Hassan
We investigate how the constraints on cosmological and astrophysical parameters (, , , ) vary when exploiting information from multiple…
HIFlow: Generating Diverse HI Maps and Inferring Cosmology while Marginalizing over Astrophysics using Normalizing Flows
Sultan Hassan, Francisco Villaescusa-Navarro, Benjamin Wandelt +11
A wealth of cosmological and astrophysical information is expected from many ongoing and upcoming large-scale surveys. It is crucial to prepare for these surveys now and develop to…
Constraining the astrophysics and cosmology from 21cm tomography using deep learning with the SKA
Sultan Hassan, Sambatra Andrianomena, Caitlin Doughty
Future Square Kilometre Array (SKA) surveys are expected to generate huge datasets of 21cm maps on cosmological scales from the Epoch of Reionization (EoR). We assess the viability…
Cosmology with Phase 1 of the Square Kilometre Array; Red Book 2018: Technical specifications and performance forecasts
Square Kilometre Array Cosmology Science Working Group, David J. Bacon, Richard A. Battye +44
We present a detailed overview of the cosmological surveys that will be carried out with Phase 1 of the Square Kilometre Array (SKA1), and the science that they will enable. We hig…
Testing General Relativity with the Doppler magnification effect
Sambatra Andrianomena, Camille Bonvin, David Bacon +4
The apparent sizes and brightnesses of galaxies are correlated in a dipolar pattern around matter overdensities in redshift space, appearing larger on their near side and smaller o…
Fundamental Physics with the Square Kilometre Array
A. Weltman, P. Bull, S. Camera +50
The Square Kilometre Array (SKA) is a planned large radio interferometer designed to operate over a wide range of frequencies, and with an order of magnitude greater sensitivity an…
Dipolar modulation in the size of galaxies: The effect of Doppler magnification
Camille Bonvin, Sambatra Andrianomena, David Bacon +4
Objects falling into an overdensity appear larger on its near side and smaller on its far side than other objects at the same redshift. This produces a dipolar pattern of magnifica…
Cosmology with Doppler Lensing
David J. Bacon, Sambatra Andrianomena, Chris Clarkson +2
Doppler lensing is the apparent change in object size and magnitude due to peculiar velocities. Objects falling into an overdensity appear larger on its near side, and smaller on i…