Project description
Astronomy advancing techniques for Big Data mining
Big Data is already present in numerous fields and domains, but the efficient and automated mining of data still poses a challenge. The automation of Machine Learning (ML) techniques in these processes is necessary. This is precisely what astronomers are doing. Collaborating with them would bring benefits to various fields. With the support of the Marie Skłodowska-Curie Actions, the SUNDIAL project aims to establish a network dedicated to developing novel algorithms. These algorithms will be used to study the vast databases generated by modern telescopes, enabling a better understanding of galaxy formation and evolution. The project also focuses on training young scientists in both computer science and astronomy. This unique collaboration is expected to yield multiple sophisticated applications for society.
Objective
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
Fields of science
- 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)
Funding Scheme
MSCA-ITN-ETN - European Training NetworksCoordinator
9712CP Groningen
Netherlands