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Found 6 papers, 5 papers with code
Category-based Galaxy Image Generation via Diffusion Models
In this work, we present GalCatDiff, the first framework in astronomy to leverage both galaxy image features and astrophysical properties in the network design of diffusion models.
A New Task: Deriving Semantic Class Targets for the Physical Sciences
We define deriving semantic class targets as a novel multi-modal task.
Radio Galaxy Zoo: Using semi-supervised learning to leverage large unlabelled data-sets for radio galaxy classification under data-set shift
In this work we examine the classification accuracy and robustness of a state-of-the-art semi-supervised learning (SSL) algorithm applied to the morphological classification of radio galaxies.
Structured Variational Inference for Simulating Populations of Radio Galaxies
We present a model for generating postage stamp images of synthetic Fanaroff-Riley Class I and Class II radio galaxies suitable for use in simulations of future radio surveys such as those being developed for the Square Kilometre Array.
Data Augmentation
Variational Inference
Instrumentation and Methods for Astrophysics
Astrophysics of Galaxies
Attention-gating for improved radio galaxy classification
In this work we introduce attention as a state of the art mechanism for classification of radio galaxies using convolutional neural networks.
Transfer learning for radio galaxy classification
In the context of radio galaxy classification, most state-of-the-art neural network algorithms have been focused on single survey data.
Instrumentation and Methods for Astrophysics
