PROCEEDINGS OF SPIE, THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING
Abstract
Euclid is an ESA mission aimed at understanding the nature of dark energy and dark matter by using simultaneously two probes (weak lensing and baryon acoustic oscillations). The mission will observe galaxies and clusters of galaxies out to z~2, in a wide extra-galactic survey covering 15000 deg2, plus a deep survey covering an area of 40 deg2. The payload is composed of two instruments, an imager in the visible domain (VIS) and an imager-spectrometer (NISP) covering the near-infrared. The launch is planned in Q4 of 2020. The elements of the Euclid Science Ground Segment (SGS) are the Science Operations Centre (SOC) operated by ESA and nine Science Data Centres (SDCs) in charge of data processing, provided by the Euclid Consortium (EC), formed by over 110 institutes spread in 15 countries. SOC and the EC started several years ago a tight collaboration in order to design and develop a single, cost-efficient and truly integrated SGS. The distributed nature, the size of the data set, and the needed accuracy of the results are the main challenges expected in the design and implementation of the SGS. In particular, the huge volume of data (not only Euclid data but also ground based data) to be processed in the SDCs will require distributed storage to avoid data migration across SDCs. This paper describes the management challenges that the Euclid SGS is facing while dealing with such complexity. The main aspect is related to the organisation of a geographically distributed software development team. In principle algorithms and code is developed in a large number of institutes, while data is actually processed at fewer centers (the national SDCs) where the operational computational infrastructures are maintained. The software produced for data handling, processing and analysis is built within a common development environment defined by the SGS System Team, common to SOC and ECSGS, which has already been active for several years. The code is built incrementally through different levels of maturity, going from prototypes (developed mainly by scientists) to production code (engineered and tested at the SDCs). A number of incremental challenges (infrastructure, data processing and integrated) have been included in the Euclid SGS test plan to verify the correctness and accuracy of the developed systems.
