4 citations · 5 across the 9 of their papers we have counts for
3 papers · 1 filter
Radio Galaxy Zoo: Leveraging latent space representations from variational autoencoder
Sambatra Andrianomena, Hongming Tang
We propose to learn latent space representations of radio galaxies, and train a very deep variational autoencoder (\protect\Verb+VDVAE+) on RGZ DR1, an unlabeled dataset, to this e…
Classifying galaxies according to their HI content
Sambatra Andrianomena, Mika Rafieferantsoa, Romeel Davé
We use machine learning to classify galaxies according to their HI content, based on both their optical photometry and environmental properties. The data used for our analyses are…
Predicting the Neutral Hydrogen Content of Galaxies From Optical Data Using Machine Learning
Mika Rafieferantsoa, Sambatra Andrianomena, Romeel Davé
We develop a machine learning-based framework to predict the HI content of galaxies using more straightforwardly observable quantities such as optical photometry and environmental…