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Found 9 papers, 8 papers with code
Scaling Laws for Galaxy Images
We then compare the downstream performance of finetuned models pretrained on either ImageNet-12k alone vs. additionally pretrained on our galaxy images.
Bayesian Imaging for Radio Interferometry with Score-Based Priors
The inverse imaging task in radio interferometry is a key limiting factor to retrieving Bayesian uncertainties in radio astronomy in a computationally effective manner.
A New Task: Deriving Semantic Class Targets for the Physical Sciences
We define deriving semantic class targets as a novel multi-modal task.
Towards Galaxy Foundation Models with Hybrid Contrastive Learning
New astronomical tasks are often related to earlier tasks for which labels have already been collected.
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.
Quantifying Uncertainty in Deep Learning Approaches to Radio Galaxy Classification
In this work we use variational inference to quantify the degree of uncertainty in deep learning model predictions of radio galaxy classification.
E(2) Equivariant Self-Attention for Radio Astronomy
In this work we introduce group-equivariant self-attention models to address the problem of explainable radio galaxy classification in astronomy.
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.
