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Understanding data: Visualisation, machine learning, and reproducibility

Data analysis and visualisation techniques, dealing with big data, machine learning techniques, spatial and nonspatial data visualisation, and best practices for reproducibility
When 25 June 2019
from 09:00 AM to 06:00 PM
Where Lyon, France
Contact Name
Attendees Konrad Hinsen, Centre National de la Recherche Scientifique, FR
Anna Scaife, head of Jodrell Bank Interferometry Centre of Excellence at the University of Manchester, UK
Simon Portegies Zwart, Professor of Computational Astrophysics, Sterrewacht Leiden, NL
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Modern astronomy is a data-driven endeavor, with use cases ranging from detailed computational simulations to massive telescopic surveys such as LSST, Gaia, and SKA. Within this dependence on data is an inherent need to better understand it. A number of techniques exist to visualise data, as well as to glean information from patterns that cannot be easily found algorithmically. Machine learning techniques complement visualisation, supporting knowledge discovery via statistical routines. 

This Special Session covers both traditional and cutting-edge data processing, visualisation, and machine learning techniques for astronomers. It introduces new ways for extracting useful information from a dataset, demonstrates existing software packages that support the workflows of astronomers and the reproducibility of astronomy research, and discusses the future of data processing for astronomy. This session provides information for newcomers to the field through introductory talks and deeper discussions and demonstrations to enhance and expand the knowledge of established researchers.

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