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.

Programme

Among the topics to be discussed are:

• Best practices in data analysis and visualisation techniques
• New and established software packages
• Creating and using reproducible workflows for reproducible research
• Dealing with big data
• Spatial and nonspatial data visualisation
• Best practices for reproducibility

Invited speakers

• Konrad Hinsen, Centre National de la Recherche Scientifique, FR
• Simon Portegies Zwart, Leiden University, NL
• Anna Scaife, University of Manchester, UK
• Johanna Schmidt, VRVis Research Center, AT

Scientific organisers

• Rachael Ainsworth, University of Manchester (UK)
• Mohammad Akhlaghi, Centre de Recherche Astrophysique de Lyon (FR)
• Alice Allen, Astrophysics Source Code Library/University of Maryland (US)
• Amruta Jaodand, ASTRON (NL)
• David Valls-Gabaud, Observatoire de Paris (FR)
• Rein Warmels, European Southern Observatory (DE)
• John Wenskovitch, Virginia Tech (US)

Contact

John Wenskovitch, johnwenskovitch @ gmail.com / Amruta Jaodand, a.d.jaodand @ uva.nl / Alice Allen, aallen @ ascl.net