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H2020Doctoral network2017–2021

SUNDIAL · SUrvey Network for Deep Imaging Analysis and Learning

Horizon 2020 — Marie Skłodowska-Curie Actions

Duration
2017-04-01 → 2021-09-30
EU contribution
€3,602,484
Participants
15
Scheme
MSCA-ITN-ETN

Lines connect the coordinator with its partners.

Results in brief

SUrvey Network for Deep Imaging Analysis and Learning

The objective of SUNDIAL is to train researchers to address the most prominent CI topics related to the analysis of Big Data and their application to galaxy evolution studies. These are: (1) Automatic detection of faint low surface brightness features (dwarf galaxies, merger remnants, intracluster light) in deep astronomical surveys, and interpreting them astrophysically in terms of galaxy formation and evolution. (2) Automated object recognition in Big Data sets: (a) the unsupervised identification of groups of objects with similar (clustering), properties and (b) the supervised assignment of objects into pre-defined target classes (classification). The addition of prior information from astrophysics will be crucial in both cases. (3) Simulations of galaxy interaction, their characterisation and visualisation. The simulations serve to identify the critical characterisation, necessary to optimally identify how observations can be described. Such comparisons will lead to a better parametrisation and understanding of galaxy cluster evolution. At the end of the network, we can see that our concept has worked. By putting together a team of astronomers and computer scientists, we have managed to create a set of tools that help to advance galaxy evolution science, published them in computer science journals, applied them to datasets in astronomy, and again published those results in astronomical journals. While being useful for our own science, the tools are only now being picked up by the community, implying that it is too early to see their full impact. The same holds for applications of these tools in other areas. Very important is that this collaboration has led to an efficient group of scientists, which is continuing to work together on making the tools better and applying them to more areas.

Data: CORDIS, © European Union

Project 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

Original text from CORDIS.

Participants

  • RIJKSUNIVERSITEIT GRONINGEN · GroningenCoordinatorNetherlands
  • ADCIS · Saint-ContestFrance
  • CHAMBRE DE COMMERCE ET D'INDUSTRIE DE REGION PARIS ILE-DE-FRANCE · PARISFrance
  • CHAMBRE DE COMMERCE ET DÍNDUSTRIDE REGION PARIS-ILE-DE-FRANCE · PARISFrance
  • CLEVERFRANKE B.V. · UtrechtNetherlands
  • FUNDACION CENTRO DE TECNOLOGIAS DE INTERACCION VISUAL Y COMUNICACIONES VICOMTECH · Donostia San SebastianSpain
  • IBM RESEARCH GMBH · RUESCHLIKONSwitzerland
  • INSTITUTO DE ASTROFISICA DE CANARIAS · SAN CRISTOBAL DE LA LAGUNASpain
  • ISTITUTO NAZIONALE DI ASTROFISICA · ROMAItaly
  • OULUN YLIOPISTO · OuluFinland
  • RUPRECHT-KARLS-UNIVERSITAET HEIDELBERG · HeidelbergGermany
  • THE UNIVERSITY OF BIRMINGHAM · BirminghamUnited Kingdom
  • Target Holding BV · GroningenNetherlands
  • UNIVERSITA DEGLI STUDI DI NAPOLI FEDERICO II · NapoliItaly
  • UNIVERSITEIT GENT · GentBelgium

Links

Data: CORDIS, © European Union