305 citations · 962 across the 30 of their papers we have counts for
30 papers
A Hierarchy of Normalizing Flows for Modelling the Galaxy-Halo Relationship
Christopher C. Lovell, Sultan Hassan, Daniel Anglés-Alcázar +8
Using a large sample of galaxies taken from the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project, a suite of hydrodynamic simulations varying both cosm…
An Exploration of AGN and Stellar Feedback Effects in the Intergalactic Medium via the Low Redshift Lyman- Forest
Megan Taylor Tillman, Blakesley Burkhart, Stephanie Tonnesen +9
We explore the role of galactic feedback on the low redshift Lyman- (Ly)~forest () statistics and its potential to alter the thermal state of the intergalactic…
JWST constraints on the UV luminosity density at cosmic dawn: implications for 21-cm cosmology
Sultan Hassan, Christopher C. Lovell, Piero Madau +10
An unprecedented array of new observational capabilities are starting to yield key constraints on models of the epoch of first light in the Universe. In this Letter we discuss the…
Emulating radiation transport on cosmological scale using a denoising Unet
Mosima P. Masipa, Sultan Hassan, Mario G. Santos +2
Semi-numerical simulations are the leading candidates for evolving reionization on cosmological scales. These semi-numerical models are efficient in generating large-scale maps of…
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…
HIGlow: Conditional Normalizing Flows for High-Fidelity HI Map Modeling
Roy Friedman, Sultan Hassan
Extracting the maximum amount of cosmological and astrophysical information from upcoming large-scale surveys remains a challenge. This includes evaluating the exact likelihood, pa…
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…
Towards a non-Gaussian Generative Model of large-scale Reionization Maps
Yu-Heng Lin, Sultan Hassan, Bruno Régaldo-Saint Blancard +2
High-dimensional data sets are expected from the next generation of large-scale surveys. These data sets will carry a wealth of information about the early stages of galaxy formati…
Bridging the Gap between Cosmic Dawn and Reionization favors Faint Galaxies-dominated Models
Ankita Bera, Sultan Hassan, Aaron Smith +4
It has been claimed that traditional models struggle to explain the tentative detection of the 21\,cm absorption trough centered at measured by the EDGES collaboration. O…
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…
Wavelet Moments for Cosmological Parameter Estimation
Michael Eickenberg, Erwan Allys, Azadeh Moradinezhad Dizgah +10
Extracting non-Gaussian information from the non-linear regime of structure formation is key to fully exploiting the rich data from upcoming cosmological surveys probing the large-…
The CAMELS project: public data release
Francisco Villaescusa-Navarro, Shy Genel, Daniel Anglés-Alcázar +44
The Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project was developed to combine cosmology with astrophysics through thousands of cosmological hydrodynami…
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…
The CAMELS Multifield Dataset: Learning the Universe's Fundamental Parameters with Artificial Intelligence
Francisco Villaescusa-Navarro, Shy Genel, Daniel Angles-Alcazar +25
We present the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) Multifield Dataset, CMD, a collection of hundreds of thousands of 2D maps and 3D grids containi…
Robust marginalization of baryonic effects for cosmological inference at the field level
Francisco Villaescusa-Navarro, Shy Genel, Daniel Angles-Alcazar +11
We train neural networks to perform likelihood-free inference from 2D maps containing the total mass surface density from thousands of hydrodynamic simula…
Multifield Cosmology with Artificial Intelligence
Francisco Villaescusa-Navarro, Daniel Anglés-Alcázar, Shy Genel +10
Astrophysical processes such as feedback from supernovae and active galactic nuclei modify the properties and spatial distribution of dark matter, gas, and galaxies in a poorly und…
Reionization with Simba: How much does astrophysics matter in modeling cosmic reionization?
Sultan Hassan, Romeel Davé, Matthew McQuinn +5
Traditional large-scale models of reionization usually employ simple deterministic relations between halo mass and luminosity to predict how reionization proceeds. We here examine…
Hybrid analytic and machine-learned baryonic property insertion into galactic dark matter haloes
Ben Moews, Romeel Davé, Sourav Mitra +2
While cosmological dark matter-only simulations relying solely on gravitational effects are comparably fast to compute, baryonic properties in simulated galaxies require complex hy…
The CAMELS project: Cosmology and Astrophysics with MachinE Learning Simulations
Francisco Villaescusa-Navarro, Daniel Anglés-Alcázar, Shy Genel +19
We present the Cosmology and Astrophysics with MachinE Learning Simulations --CAMELS-- project. CAMELS is a suite of 4,233 cosmological simulations of volu…
Can galaxy evolution mimic cosmic reionization?
Sultan Hassan, Max Gronke
Lyman- (Ly) emitting galaxies are powerful tools to probe the late stages of cosmic reionization. The observed sudden drop in Ly fraction at is often interpreted as…
Constraining the reionization history using deep learning from 21cm tomography with the Square Kilometre Array
Tumelo Mangena, Sultan Hassan, Mario G. Santos
Upcoming 21cm surveys with the SKA1-LOW telescope will enable imaging of the neutral hydrogen distribution on cosmological scales in the early Universe. These surveys are expected…
Testing Galaxy Formation Simulations with Damped Lyman- Abundance and Metallicity Evolution
Sultan Hassan, Kristian Finlator, Romeel Davé +2
We examine the properties of damped Lyman- absorbers (DLAs) emerging from a single set of cosmological initial conditions in two state-of-the-art cosmological hydrodynamic simul…
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…
ARTIST: Fast radiative transfer for large-scale simulations of the epoch of reionisation
Margherita Molaro, Romeel Davé, Sultan Hassan +2
We introduce the "Asymmetric Radiative Transfer In Shells Technique" (ARTIST), a new method for photon propagation on large scales that explicitly conserves photons, propagates pho…