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Found 4 papers, 3 papers with code
Contribution of Radio Halos to the Foreground for SKA EoR Experiments
The overwhelming foreground contamination is one of the primary impediments to probing the EoR through measuring the redshifted 21 cm signal.
Cosmology and Nongalactic Astrophysics
Separating the EoR Signal with a Convolutional Denoising Autoencoder: A Deep-learning-based Method
When applying the foreground removal methods to uncover the faint cosmological signal from the epoch of reionization (EoR), the foreground spectra are assumed to be smooth.
Radio Galaxy Morphology Generation Using DNN Autoencoder and Gaussian Mixture Models
In this work, we propose a morphology generation framework for two typical radio galaxies namely Fanaroff-Riley type-I (FRI) and type-II (FRII) with deep neural network based autoencoder (DNNAE) and Gaussian mixture models (GMMs).
X-ray Astronomical Point Sources Recognition Using Granular Binary-tree SVM
An approach to recognize the point sources (PS) in the X-ray astronomical images using our newly designed granular binary-tree support vector machine (GBT-SVM) classifier is proposed.
