Oral Contribution (O4.3) Sweta Singh (Kapteyn Astronomical Institute (University of Groningen)/Anchormen Data Activators (Amsterdam))
Theme: Data science challenges: tools from statistics to machine learning
Scientific Visualisation of Extremely Large Distributed Astronomical Surveys
Visualisation of large data sets is one of the core problems in carrying out scientific research. This is even more relevant for Astronomy where new cutting edge and large telescopes are going to generate multi Peta Byte (PB) sky surveys. We describe and demonstrate our solution, developed in context of the Euclid satellite whose astronomical imaging data would be so large during its several years of operation that it will have to be distributed over various data centers spread across Europe. In our architecture, several millions of HIPS survey images distributed over different data centers are efficiently stitched/combined to deliver image/s of interest at the desired resolution to the astronomers on their remote desktops. This is achieved in real time by optimally combining several modern tools consisting of http servers, load balancers, reverse proxies, SQL databases including on the fly image generation which all feed (only) the required information to the Aladin interactive sky atlas visualisation cum analysis tool on the remote users end. This approach has potential applications for other large astronomical projects especially those requiring their data to be distributed across different geographical locations due to its big size or/and due to collaborative nature of the project.
