Description du projet
Des techniques d’exploration des mégadonnées pour l’astronomie
Le mégadonnées sont déjà présentes dans de nombreux domaines, mais l’extraction efficace et automatisée des données demeure un défi. L’automatisation des techniques d’apprentissage automatique (AA) au sein de ces processus est essentielle. C’est précisément ce que font les astronomes. Cette collaboration apporterait des avantages dans divers domaines. Avec le soutien du programme Actions Marie Skłodowska-Curie, le projet SUNDIAL entend établir un réseau dédié au développement de nouveaux algorithmes. Ces algorithmes permettront d’étudier les vastes bases de données que génèrent les télescopes modernes, et de mieux comprendre la formation et l’évolution des galaxies. Le projet se propose également de former de jeunes scientifiques dans les domaines de l’informatique et de l’astronomie. Cette collaboration unique devrait déboucher sur de nombreuses applications sophistiquées au profit de la société.
Objectif
Though Big Data has become common in many domains nowadays, the challenges to develop efficient and automated mining of the ever increasing data sets by new generations of data scientists are eminent. These challenges span wide swathes of society, business and research. Astronomers with their high-tech observatories are historically at the forefront of this field, but obviously, the impact in e.g. commercial applications, security, environmental monitoring and experimental research is immense. We aim to contribute to this general discussion by training a number of young scientists in the fields of computer science and astronomy, focussing on techniques of automated learning from large quantities of data to answer fundamental questions on the evolution of properties of galaxies. While these techniques will lead to major advances in our understanding of the formation and evolution of galaxies, we will also promote, in collaboration with industry, much more general applications in society, e.g. in medical imaging or remote sensing. We have put together a team of astronomers and computer scientists, from academic and private sector partners, to develop techniques to detect and classify ultra-faint galaxies and galaxy remnants in a deep survey of the Fornax cluster, and use the results to study how galaxies evolve in the dense environment of galaxy clusters. With a team of young researchers we will develop novel computer science algorithms addressing fundamental topics in galaxy formation, such as the huge dark matter fractions inferred by theory, and the lack of detected angular momentum in galaxies. The collaboration is unique - it will develop a platform for deep symbiosis of two radically different strands of approaches: purely data-driven machine learning and specialist approaches based on techniques developed in astronomy. Young scientists trained with such skills are highly demanded both in research and business.The duration of the project was originally 48 months, till 31/3/2021. An extension of 6 months till 30/9/2021 was granted. The project therefore last 54 months which is indicated in all related project activities
Champ scientifique
- natural sciencescomputer and information sciencesdata sciencebig data
- natural sciencesphysical sciencesastronomyastrophysicsdark matter
- natural sciencesbiological sciencesbiological behavioural sciencesethologybiological interactions
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
- natural sciencesphysical sciencesastronomyphysical cosmologygalaxy evolution
Programme(s)
Régime de financement
MSCA-ITN-ETN - European Training NetworksCoordinateur
9712CP Groningen
Pays-Bas