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Predicting the neutral hydrogen content of galaxies from optical data using machine learning
(Oxford University Press, 2018)
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
parameters. We train the algorithm on z ...
Classifying galaxies according to their H I content
(Oxford University Press, 2020)
We use machine learning to classify galaxies according to their H I content, based on both their optical photometry and environmental properties. The data used for our analyses are the outputs in the range z = 0–1 from ...
Constraining the astrophysics and cosmology from 21 cm tomography using deep learning with the SKA
(Oxford University Press, 2020)
Future Square Kilometre Array (SKA) surveys are expected to generate huge data sets of 21 cm maps on cosmological scales from the Epoch of Reionization. We assess the viability of exploiting machine learning techniques, ...
Probabilistic learning for pulsar classification
(IOP Publishing, 2022)
In this work, we explore the possibility of using probabilistic learning to identify
pulsar candidates. We make use of Deep Gaussian Process (DGP) and Deep Kernel Learning
(DKL). Trained on a balanced training set in ...