Advanced computing for reproducibility of astronomy Big Data Science, with a showcase of AMIGA and the SKA Science prototype
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866911369268297728 |
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| author | Garrido, Julián Sánchez, Susana João, Edgar Ribeiro Ianjamasimanana, Roger Parra, Manuel Verdes-Montenegro, Lourdes |
| author_facet | Garrido, Julián Sánchez, Susana João, Edgar Ribeiro Ianjamasimanana, Roger Parra, Manuel Verdes-Montenegro, Lourdes |
| contents | The Square Kilometre Array Observatory (SKAO) faces unprecedented technological challenges due to the vast scale and complexity of its data. This paper provides an overview of research by the AMIGA group to address these computing and reproducibility challenges. We present advancements in semantic data models, analysis services integrated into federated infrastructures, and the application to astronomy studies of techniques that enhance research transparency. By showcasing these astronomy work, we demonstrate that achieving reproducible science in the Big Data era is feasible. However, we conclude that for the SKAO to succeed, the development of the SKA Regional Centre Network (SRCNet) must explicitly incorporate these reproducibility requirements into its fundamental architectural design. Embedding these standards is crucial to enable the global community to conduct verifiable and sustainable research within a federated environment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_07439 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Advanced computing for reproducibility of astronomy Big Data Science, with a showcase of AMIGA and the SKA Science prototype Garrido, Julián Sánchez, Susana João, Edgar Ribeiro Ianjamasimanana, Roger Parra, Manuel Verdes-Montenegro, Lourdes Instrumentation and Methods for Astrophysics Distributed, Parallel, and Cluster Computing The Square Kilometre Array Observatory (SKAO) faces unprecedented technological challenges due to the vast scale and complexity of its data. This paper provides an overview of research by the AMIGA group to address these computing and reproducibility challenges. We present advancements in semantic data models, analysis services integrated into federated infrastructures, and the application to astronomy studies of techniques that enhance research transparency. By showcasing these astronomy work, we demonstrate that achieving reproducible science in the Big Data era is feasible. However, we conclude that for the SKAO to succeed, the development of the SKA Regional Centre Network (SRCNet) must explicitly incorporate these reproducibility requirements into its fundamental architectural design. Embedding these standards is crucial to enable the global community to conduct verifiable and sustainable research within a federated environment. |
| title | Advanced computing for reproducibility of astronomy Big Data Science, with a showcase of AMIGA and the SKA Science prototype |
| topic | Instrumentation and Methods for Astrophysics Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2601.07439 |