Big data & Computer science
Astrophysics is rapidly entering the exabyte era, driven by a new generation of observatories producing unprecedented volumes of complex data. Facilities such as LOFAR, MeerKAT, and ASKAP already generate data streams of hundreds of Gbit/s, highlighting the challenges of real-time processing, scalable storage, and efficient data reuse. Upcoming instruments, including SKAO, CTA, Rubin Observatory, Euclid, and LISA, will increase these demands by orders of magnitude, making advanced data management and analysis strategies essential.
In this context, Big Data technologies, High Performance Computing (HPC), and Artificial Intelligence (AI) have become foundational to modern astrophysics. They enable the full scientific workflow: from data acquisition and reduction to simulation, modeling, and interpretation. HPC systems, especially those leveraging heterogeneous architectures with GPUs, high-speed interconnects, and distributed storage, are no longer just tools for accelerating computations, but the only viable approach to process data at this scale.
At the same time, computer science plays a critical role in redesigning algorithms, codes, and workflows to efficiently exploit exascale and post-exascale infrastructures. Machine learning and AI techniques are increasingly integrated into data pipelines, supporting automated analysis, anomaly detection, and advanced visualization of massive datasets.
Addressing these challenges requires a holistic ecosystem that combines scalable computing, intelligent data management, and interoperable software frameworks aligned with FAIR and Open Science principles. By integrating large-scale simulations with observational data and enabling real-time, high-throughput analysis, this ecosystem paves the way for new discoveries and a deeper understanding of the Universe.
[Credits: Claudio Gheller (INAF)]
The Institute of Radio Astronomy is involved in the following projects in the fields of big data and computer science:
