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Found 10 papers, 2 papers with code
Towards out-of-distribution generalization in large-scale astronomical surveys: robust networks learn similar representations
The generalization of machine learning (ML) models to out-of-distribution (OOD) examples remains a key challenge in extracting information from upcoming astronomical surveys.
The CAMELS project: public data release
The Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project was developed to combine cosmology with astrophysics through thousands of cosmological hydrodynamic simulations and machine learning.
The CAMELS Multifield Dataset: Learning the Universe's Fundamental Parameters with Artificial Intelligence
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 containing many different properties of cosmic gas, dark matter, and stars from 2, 000 distinct simulated universes at several cosmic times.
Robust marginalization of baryonic effects for cosmological inference at the field level
We train neural networks to perform likelihood-free inference from $(25\, h^{-1}{\rm Mpc})^2$ 2D maps containing the total mass surface density from thousands of hydrodynamic simulations of the CAMELS project.
Multifield Cosmology with Artificial Intelligence
Although our maps only cover a small area of $(25~h^{-1}{\rm Mpc})^2$, and the different fields are contaminated by astrophysical effects in very different ways, our networks can infer the values of $\Omega_{\rm m}$ and $\sigma_8$ with a few percent level precision for most of the fields.
Hybrid analytic and machine-learned baryonic property insertion into galactic dark matter haloes
In doing so, we are able to recover more properties than the analytic formalism alone can provide, creating a high-speed hydrodynamic simulation emulator that populates galactic dark matter haloes in N-body simulations with baryonic properties.
The CAMELS project: Cosmology and Astrophysics with MachinE Learning Simulations
We present the Cosmology and Astrophysics with MachinE Learning Simulations --CAMELS-- project.
Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies Instrumentation and Methods for Astrophysics
Constraining the astrophysics and cosmology from 21cm tomography using deep learning with the SKA
Future Square Kilometre Array (SKA) surveys are expected to generate huge datasets of 21cm maps on cosmological scales from the Epoch of Reionization (EoR).
Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies Instrumentation and Methods for Astrophysics
Identifying Reionization Sources from 21cm Maps using Convolutional Neural Networks
We create a Convolutional Neural Network (CNN) that is efficiently able to distinguish between 21cm maps that are produced by AGN versus galaxies scenarios with an accuracy of 92-100%, depending on redshift and neutral fraction range.
Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies
Reionization Models Classifier using 21cm Map Deep Learning
Next-generation 21cm observations will enable imaging of reionization on very large scales.
Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies
