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Found 14 papers, 12 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.
Rare Galaxy Classes Identified In Foundation Model Representations
We identify rare and visually distinctive galaxy populations by searching for structure within the learned representations of pretrained models.
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.
MiraBest: A Dataset of Morphologically Classified Radio Galaxies for Machine Learning
Existing applications that utilise the MiraBest dataset are reviewed, and an extended dataset of 2100 sources is created by cross-matching MiraBest with other catalogues of radio-loud AGN that have been used more widely in the literature for machine learning applications.
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.
Can semi-supervised learning reduce the amount of manual labelling required for effective radio galaxy morphology classification?
In this work, we examine the robustness of state-of-the-art semi-supervised learning (SSL) algorithms when applied to morphological classification in modern radio astronomy.
Practical Galaxy Morphology Tools from Deep Supervised Representation Learning
Models fine-tuned from our representation are better able to identify ring galaxies than models fine-tuned from terrestrial images (ImageNet) or trained from scratch.
Fanaroff-Riley classification of radio galaxies using group-equivariant convolutional neural networks
Weight sharing in convolutional neural networks (CNNs) ensures that their feature maps will be translation-equivariant.
Instrumentation and Methods for Astrophysics Astrophysics of 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
