Speaker
Description
The Einstein Telescope (ET) is a next-generation, underground gravitational-wave observatory designed to explore the Universe across its cosmic history. Its ambitious scientific goals, ranging from probing black-hole physics and neutron-star matter to investigating dark energy and the early Universe, require a new generation of computational
and data-analysis infrastructures. Our contribution consists in the development of a Virtual Research Environment (VRE) for the ET Bologna site, aimed at enabling collaborative, high-performance, and reproducible research within the ET community.
The ET Bologna VRE builds upon the BETIF/DIFAET computing infrastructure, adopting a modular architecture based on widely used open-source technologies such as Docker, Kubernetes, Jupyter, and Rucio/Reana developed at CERN. This design allows users to perform both interactive analyses and large-scale computations within an orchestrated and
containerized environment. The system is fully customizable, supporting multiple software stacks through CERN Virtual Machine File System (CVMFS) and providing seamless integration with external Rucio Storage Elements for distributed data management.
Authentication and authorization are managed via Indigo-IAM, ensuring compliance with the ET federation’s identity and access policies.
The platform supports heterogeneous computing resources, including CPU and
GPU-accelerated environments, and offers a range of virtual configurations that can scale according to workload and user needs within the available hardware. Through its Python-friendly interface and integration with scientific frameworks, the VRE lowers the entry barrier for analysis development while ensuring portability and reproducibility of workflows
across the collaboration.
Beyond its immediate use for data analysis and algorithm prototyping, the ET Bologna VRE serves as a testbed for future computational strategies within the broader Einstein Telescope project. It demonstrates how local resources can be orchestrated into a flexible, cloud-native environment, paving the way for a distributed data analysis model that will be crucial during ET’s construction and operational phases. This work contributes to the establishment of a sustainable, collaborative computational ecosystem for the next era of gravitational-wave astronomy.
