SUNDIAL - SUrvey Network for Deep Imaging Analysis and Learning

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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.

Big Data has become common nowadays but it is a challenge to develop efficient and automated use of the ever-increasing data sets by new generations of data scientists. The project aims 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.

It will also promote, in collaboration with industry, much more general application of big data in society, e.g. in medical imaging or remote sensing.

Basic information

Country: Belgium, Finland, France, Germany, Italy, Netherlands, Spain, United Kingdom

Coordinator: University of Groningen, https://www.rug.nl/

Programme: Horizon 2020

Project Acronym:

Target groups: primary school students, researchers

Topic: Astronomy, Computer science

Start year: 2016

End year: 2021

Url: https://www.astro.rug.nl/~sundial/

Contact person: Wiebe Zijlstra, wiebe.zijlstra (at) rug.nl

The project has 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 SUNDIAL 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 in both research and business.

The research in the project is divided into three work packages:

  1. Automated Detection Methods of Faint Structures
  2. Quantitative Galaxy Classification and Automated Exploratory Analysis
  3. Relating simulations with observations- characterisation and visualisation of simulations in the context of observations and vice versa

For the Sundial project Manou Ijtsma has developed a lesson series of five lessons about astronomical topics for students aged 8-12. The lessons can be given by any teacher, even if you have no knowledge about astronomy. The lessons can be downloaded ready to use with PowerPoint slides to illustrate several different topics, such as Mars, the Milky Way galaxy and exoplanets.

The information which can be taught during these lessons is available in the notes and there are even some extra materials in these lessons which could be added for advanced groups. These lessons are an ideal way for teachers to teach about astronomy without hours of preparing lessons. Astronomy is one of those topics which get the attention of students almost immediately and the easiness of giving these lessons makes you want to give them again every year.

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