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GPU Framework for Radio Astronomical Image Synthesis
SAGECal is a fast, memory efficient and GPU accelerated radio interferometric calibration program. It supports all source models including points, Gaussians and Shapelets. Distributed calibration using MPI and consensus optimization is enabled. Both spectral and spatial priors can be used as constraints. Tools to build/restore sky models are included.
This tutorial is based on the SKA Data Challenge 1. The aim of the tutorial is to learn to identify and classify sources is radio images. The data provided is simulated, to represent what the SKA data will look like once the telescope is in operation.
[ApJ] Contribution of Radio Halos to the Foreground for SKA EoR Experiments
User documentation for the SKA Regional Centre prototype at IAA-CSIC
Data Mining and Virtual Observatory : Identifying galaxies, Quasars and stars in multi-Wavelength surveys.
This tutorial is based on the SKA Data Challenge 1. The aim of the tutorial is to learn to identify and classify sources is radio images. The data provided is simulated, to represent what the SKA data will look like once the telescope is in operation.
GitHub repository for the Python scripts used in the bachelor's degree thesis "Study of the kinematic and primordial dipoles/Estudio de los dioplos cinemático y primordial" by Ixaka Labadie.
LNA bias values currently need to be entered manually into the SPF SHIELD GUI. These values need to be looked up in as-built documentation, which is an extra step allowing human error. This code extracts the relevant data in a format that SHIELD can autopopulate the LNA bias fields.