SUNDIAL: Red de Investigación para el Aprendizaje y Análisis de Imágenes Profundas

Start year
2017
End year
2021
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Headquarter
Description

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. 

We acknowledge financial support from the European Union's Horizon 2020 research and innovation programme under Marie Sklodowska-Curie grant agreement No 721463 to the SUNDIAL ITN network.

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