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Found 7 papers, 4 papers with code
A Deep Neural Network Based Reverse Radio Spectrogram Search Algorithm
This algorithm can be used to improve the efficiency of vetting signals of interest in technosignature searches, but could also be applied to a wider variety of searches for "lookalike" signals in large astronomical datasets.
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.
Galaxy Zoo DECaLS: Detailed Visual Morphology Measurements from Volunteers and Deep Learning for 314,000 Galaxies
All classifications are used to train an ensemble of Bayesian convolutional neural networks (a state-of-the-art deep learning method) to predict posteriors for the detailed morphology of all 314, 000 galaxies.
An old stellar population or diffuse nebular continuum emission discovered in green pea galaxies
We use new HST images of nine Green Pea Galaxies (GPGs) to study their resolved structure and color.
Astrophysics of Galaxies
Radio Galaxy Zoo: Unsupervised Clustering of Convolutionally Auto-encoded Radio-astronomical Images
Our results demonstrate this method is capable of accurately separating outliers on a SOM with neighbourhood similarity and K-means clustering of radio-astronomical features complexity.
Galaxy Zoo: Probabilistic Morphology through Bayesian CNNs and Active Learning
We use Bayesian convolutional neural networks and a novel generative model of Galaxy Zoo volunteer responses to infer posteriors for the visual morphology of galaxies.
Machine Learning for the Zwicky Transient Facility
The Zwicky Transient Facility is a large optical survey in multiple filters producing hundreds of thousands of transient alerts per night.
Instrumentation and Methods for Astrophysics
