Information collected by the world's largest radio telescope will be stored and processed by global data centers

Gaby Clark
scientific editor

Andrew Zinin
lead editor

When the Square Kilometer Array (SKA) Observatory goes , it will create one of science's biggest data challenges. The SKA Observatory is a global radio telescope project built in the Southern Hemisphere. There, views of our Milky Way are clearest and the SKA's remote sites .
The project spans two sites: approximately 131,000 Christmas-tree-shaped antennas in western Australia and 200 large dish antennas in the Karoo region of South Africa. As part of this international collaboration, Canada has established a .
The SKA Observatory will produce around of data each year. That amount would take 200 years to download using an at-home internet connection of 100 megabytes per second.
This data volume exceeds by a significant margin even what is produced by the , often considered to be the world's premier .
Research aims
Among its many , the SKA detects faint radio signals emitted during the , roughly 50 million to one billion years after the Big Bang, when the very first stars and galaxies lit up the universe.
The SKA will also test Albert Einstein's theory of by timing signals from pulsars (rapidly spinning neutron stars) with high accuracy.
Another goal is understanding —brief, intense radio pulses from distant sources. The SKA is expected to detect fast radio bursts far more frequently than current instruments, providing a large dataset to help determine their cause, building on work done by facilities like Canada's .
Initial data from the SKA is expected in 2027, with the start of major science operations in 2029 as the array is built and commissioned in phases.
Canada's role
Handling the large volume and complexity of SKA data requires a global network of specialized computing facilities, collectively known as SKA Regional Centers (SRCs).
Canada became in 2024. Shortly after joining, Canada committed to establishing one such center.
The Canadian SRC (CanSRC) will be the sole SRC in the Americas, serving as an important node for processing, storing and providing streamlined access to SKA data. It will allow researchers to focus on scientific analysis rather than data management hurdles.
Big Astronomy
The SKA is part of astronomy's ongoing evolution toward "," where international collaboration becomes essential for scientific breakthroughs. This large-scale approach not only changes how science is funded, but also how it is conducted.
While the SKA will still accommodate traditional investigator-led proposals—where individual scientists or small teams request specific telescope time and computational resources for more focused projects—most of its observing power will target ambitious, multi-year projects designed by large international teams.
Canadian researchers participate in all of the and have co-chaired four of them in recent years. Canada is recognized as a world leader in , as well as in low-frequency cosmology, areas where the SKA will make some of its most transformative discoveries.
Astronomical data management
Building, developing and managing CanSRC requires collaboration among , with four decades of experience in astronomical data management; the , offering high-performance computing resources; , operating the high-speed research network for data transfer; and the University of Victoria's , supplying the scalable infrastructure.
The project leverages expertise concentrated within the , which brings together researchers from the University of Victoria, the National Research Council Herzberg Astronomy and Astrophysics Research Centre and TRIUMF, Canada's national particle accelerator centre.
Importantly, CanSRC ensures that researchers have access to SKA data. The capabilities developed through CanSRC will strengthen Canada's digital ecosystem for the future.
Digital discovery
CanSRC will serve as a gateway for developing and expanding the use of advanced data methods and algorithms, helping scientists from research and industry sectors harness massive datasets.
Applications of these techniques extend far beyond astronomy, with potential uses in , remote sensing and artificial intelligence.
Provided by The Conversation
This article is republished from under a Creative Commons license. Read the .