Distributed Data Management with Rucio for the Einstein Telescope

¹ Istituto Nazionale di Fisica Nucleare, Sezione di Torino, via Pietro Giuria 1, I-10125 Torino, Italy
² Elemento Srl, via Virginio Allione 2, I-12100 Cuneo, Italy
³ Département d’Astronomie, Université de Genève, Chemin Pegasi 51, CH-1290 Versoix, Switzerland
⁴ Gravitational Wave Science Center (GWSC), Université de Genève, 24 quai Ernest-Ansermet, CH- 1211 Geneva, Switzerland
⁵ Université Catholique de Louvain, B-1348 Louvain-la-Neuve, Belgium

^* corresponding author, e-mail: lia.lavezzi@to.infn.it

Published online: 7 October 2025

Abstract

Modern physics experiments are often led by large collaborations including scientists and institutions from different parts of the world. To cope with the ever increasing computing and storage demands, computing resources are nowadays offered as part of a distributed infrastructure. A critical challenge for present and future experiments is an efficient and reliable data distribution and access system. Rucio is a framework for data management, access and distribution, originally developed by the ATLAS experiment and later adopted by several scientific collaborations. It is currently used by the second generation gravitational wave (GW) detectors LIGO and Virgo, and is being evaluated for the Einstein Telescope (ET), the future third generation interferometer in preparation in Europe. In this contribution, the on-going R&D for integration of Rucio within the ET computing infrastructure will be outlined. The activities include the setup of a Data Lake based on Rucio for future ET Mock Data Challenges and the customization of Rucio features for the GW community. The evaluation of RucioFS, a FUSE mount filesystem to provide the user with the well known POSIX-like view of the Rucio catalogue, and the possible implementation of additional features in it will be described. Moreover, the need for the integration of the ET Data Lake with mock Data Lakes belonging to other experiments within the astrophysics and GW communities will be addressed. This is a critical feature as data analysts in this field often require access to open data from other experiments for sky localisation and multi-messenger analysis.