The Latin American Giant Observatory: a successful collaboration in Latin America based on Cosmic Rays and computer science domains

TL;DR

The Latin American Giant Observatory (LAGO) fosters collaboration across Latin America by deploying cosmic ray detectors and developing computational tools, enabling data sharing, simulation, and analysis across diverse infrastructures for high-energy physics research.

Contribution

This work details the collaborative strategy, data management, and computational infrastructure of LAGO, integrating physics and computer science for large-scale cosmic ray research.

Findings

Successful deployment of detectors across Latin America

Development of a comprehensive data repository and metadata system

Implementation of a Science Gateway for data access and processing

Abstract

In this work the strategy of the Latin American Giant Observatory (LAGO) to build a Latin American collaboration is presented. Installing Cosmic Rays detectors settled all around the Continent, from Mexico to the Antarctica, this collaboration is forming a community that embraces both high energy physicist and computer scientists. This is so because the data that are measured must be analytical processed and due to the fact that \textit{a priori} and \textit{a posteriori} simulations representing the effects of the radiation must be performed. To perform the calculi, customized codes have been implemented by the collaboration. With regard to the huge amount of data emerging from this network of sensors and from the computational simulations performed in a diversity of computing architectures and e-infrastructures, an effort is being carried out to catalog and preserve a vast amount of data produced by the water-Cherenkov Detector network and the complete LAGO simulation workflow that characterize each site. Metadata, Permanent Identifiers and the facilities from the LAGO Data Repository are described in this work jointly with the simulation codes used. These initiatives allow researchers to produce and find data and to directly use them in a code running by means of a Science Gateway that provides access to different clusters, Grid and Cloud infrastructures worldwide.