The Sundial international training network

Mission statement

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.

Sundial

SUNDIAL (SUrvey Network for Deep Imaging Analysis & Learning) is an ambitious interdisciplinary network of nine research groups in The Netherlands, Germany, Finland, France, the United Kingdom, Spain, Belgium and Italy. The aim of the network is to develop novel algorithms to study the very large databases coming from current-day telescopes to better understand galaxy formation and evolution, and to prepare for the huge missions of the next decade.

We train 14 ESRs (PhD students) using a combination of training in computer science and astrophysics, and a comprehensive package of complimentary skills training. 6 of these are ESRs in computer science, studying topics such as detecting ultrafaint galaxy signals, developing automated models for galaxy recognition and classification, and developing new methods to compare observations and galaxy simulations as well as visualization. 8 astronomy ESRs use these tools to study the evolution of galaxies in clusters and in the field, using the Fornax Deep Survey (FDS) and the Kilo Degree Survey (KIDS) as testbeds. They study dwarf galaxies, including the class of ultra-diffuse galaxies, as well as larger galaxies, using morphology, scaling relations and stellar populations, and make numerical simulations to compare these with.

New in this network is the combination of two fields, with their own history and traditions. Combining fields is the only way to continue to make progress in this era of Big Data. We expect that several of the methods that we develop will have useful applications in other fields in science and society.

Our network also contains 5 private companies, who collaborate with the university and observatory groups to bridge the gap between the academic world and society, with the aim to convert our results into commercial products. The ESRs have a chance to work with these companies closely through training and internships.

This project has received financial support from the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No. 721463 to the SUNDIAL ITN network.

Institutes

Business partners