Description
CHAIR: G. UMANA
The SKA precursor communities are currently developing new software to automate the processing of radio images for various tasks, including source extraction, object or morphology classification, and anomaly detection. These developments heavily rely on HPC processing paradigms and machine learning (ML) methodologies.
In this context, we are developing several tools to support the...
In recent years, the remarkable capabilities offered by precursors and pathfinders of the Square Kilometre Array (SKA) have started revolutionizing our view even of previously well-known objects such as jetted Active Galactic Nuclei (AGN). Particularly in the MHz-frequency regime, observations are now able to uncover the oldest plasma injected by AGN jets into their surrounding environment,...
The effective exploitation of modern architecture is a key factor to achieve best performances in terms of both energy efficiency and run-time reduction.
We bring a specific example of this, by discussing the W-stacking gridder, an algorithm that tackles Radio imaging in massively parallel systems; its performance is limited by an all-to-all data reduction needed to pass from time-domain...
SKA precursors are giving us a first glimpse of the future capabilities of SKA. Designed to be the most sensitive radio telescopes ever, the precursors are planned to release large area surveys with arcsec resolution. However, the final image product is heavily influenced by the data reduction. Not only the huge data quantity makes a careful visual inspection and manual reduction of the data...
Radio astronomy is evolving toward ever larger and more accurate datasets.
As soon as the SKA telescopes are fully operational, hundreds of petabytes of data will be produced each year with unprecedented resolution and detail.
This rapid evolution drives toward the development of Big Data analysis and visualization tools and services, which will necessarily need to be supported by suitable...
