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Found 7 papers, 5 papers with code
Domain Adaptive Graph Neural Networks for Constraining Cosmological Parameters Across Multiple Data Sets
Deep learning models have been shown to outperform methods that rely on summary statistics, like the power spectrum, in extracting information from complex cosmological data sets.
Neural Inference of Gaussian Processes for Time Series Data of Quasars
A baseline approach to these tasks is to interpolate a time series with a Damped Random Walk (DRW) model, in which the spectrum is inferred using Maximum Likelihood Estimation (MLE).
Semi-Supervised Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection
For the first time, we demonstrate the successful use of domain adaptation on two very different observational datasets (from SDSS and DECaLS).
Inferring Structural Parameters of Low-Surface-Brightness-Galaxies with Uncertainty Quantification using Bayesian Neural Networks
Measuring the structural parameters (size, total brightness, light concentration, etc.)
Machine Learning and Cosmology
Methods based on machine learning have recently made substantial inroads in many corners of cosmology.
DeepAdversaries: Examining the Robustness of Deep Learning Models for Galaxy Morphology Classification
On the other hand, we show that training with domain adaptation improves model robustness and mitigates the effects of these perturbations, improving the classification accuracy by 23% on data with higher observational noise.
DeepShadows: Separating Low Surface Brightness Galaxies from Artifacts using Deep Learning
We take advantage of the fact that, for the first time, we have available a large number of labeled LSBGs and artifacts from the Dark Energy Survey, that we use to train, validate, and test a CNN model.
