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Found 5 papers, 4 papers with code
Fast, high-fidelity Lyman $α$ forests with convolutional neural networks
Full-physics cosmological simulations are powerful tools for studying the formation and evolution of structure in the universe but require extreme computational resources.
Estimating Galactic Distances From Images Using Self-supervised Representation Learning
We use a contrastive self-supervised learning framework to estimate distances to galaxies from their photometric images.
Self-Supervised Representation Learning for Astronomical Images
We show that, without the need for labels, self-supervised learning recovers representations of sky survey images that are semantically useful for a variety of scientific tasks.
DESCQA: An Automated Validation Framework for Synthetic Sky Catalogs
The use of high-quality simulated sky catalogs is essential for the success of cosmological surveys.
Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics
CosmoGAN: creating high-fidelity weak lensing convergence maps using Generative Adversarial Networks
Inferring model parameters from experimental data is a grand challenge in many sciences, including cosmology.
