research
∙
04/04/2023
The CAMELS project: Expanding the galaxy formation model space with new ASTRID and 28-parameter TNG and SIMBA suites
We present CAMELS-ASTRID, the third suite of hydrodynamical simulations ...
share
research
∙
02/27/2023
Robust field-level likelihood-free inference with galaxies
We train graph neural networks to perform field-level likelihood-free in...
research
∙
09/05/2022
The SZ flux-mass (Y-M) relation at low halo masses: improvements with symbolic regression and strong constraints on baryonic feedback
Ionized gas in the halo circumgalactic medium leaves an imprint on the c...
research
∙
06/09/2022
Field Level Neural Network Emulator for Cosmological N-body Simulations
We build a field level emulator for cosmic structure formation that is a...
research
∙
06/09/2022
Simple lessons from complex learning: what a neural network model learns about cosmic structure formation
We train a neural network model to predict the full phase space evolutio...
research
∙
05/16/2022
Fast and realistic large-scale structure from machine-learning-augmented random field simulations
Producing thousands of simulations of the dark matter distribution in th...
research
∙
04/28/2022
Learning cosmology and clustering with cosmic graphs
We train deep learning models on thousands of galaxy catalogues from the...
research
∙
04/15/2022
Wavelet Moments for Cosmological Parameter Estimation
Extracting non-Gaussian information from the non-linear regime of struct...
research
∙
03/15/2022
Machine Learning and Cosmology
Methods based on machine learning have recently made substantial inroads...
research
∙
01/04/2022
Augmenting astrophysical scaling relations with machine learning : application to reducing the SZ flux-mass scatter
Complex systems (stars, supernovae, galaxies, and clusters) often exhibi...
research
∙
01/04/2022
The CAMELS project: public data release
The Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS...
research
∙
11/29/2021
Weighing the Milky Way and Andromeda with Artificial Intelligence
We present new constraints on the masses of the halos hosting the Milky ...
research
∙
11/16/2021
Inferring halo masses with Graph Neural Networks
Understanding the halo-galaxy connection is fundamental in order to impr...
research
∙
09/22/2021
The CAMELS Multifield Dataset: Learning the Universe's Fundamental Parameters with Artificial Intelligence
We present the Cosmology and Astrophysics with MachinE Learning Simulati...
research
∙
09/21/2021
Robust marginalization of baryonic effects for cosmological inference at the field level
We train neural networks to perform likelihood-free inference from (25 h...
research
∙
09/20/2021
Multifield Cosmology with Artificial Intelligence
Astrophysical processes such as feedback from supernovae and active gala...
research
∙
10/29/2020
deep21: a Deep Learning Method for 21cm Foreground Removal
We seek to remove foreground contaminants from 21cm intensity mapping ob...
research
∙
10/17/2019
From Dark Matter to Galaxies with Convolutional Neural Networks
Cosmological simulations play an important role in the interpretation of...
research
∙
10/09/2019
Learning neutrino effects in Cosmology with Convolutional Neural Networks
Measuring the sum of the three active neutrino masses, M_ν, is one of th...
research
∙
04/29/2019
HIGAN: Cosmic Neutral Hydrogen with Generative Adversarial Networks
One of the most promising ways to observe the Universe is by detecting t...
research
∙
02/15/2019