ADASS XXXI

Peter K. G. Williams

The speaker's profile picture

Biography

Peter K. G. Williams is the Innovation Scientist of the Center for Astrophysics | Harvard & Smithsonian and the American Astronomical Society, as well as the Director of the AAS WorldWide Telescope project. His research background has focused on time-domain radio astronomy, stellar and substellar magnetism, data visualization, and the art of scientific software engineering.

Profile Picture adass-xxxi-2021/question_uploads/square-sm_1hCyHNd.png Affiliation

Center for Astrophysics | Harvard & Smithsonian and American Astronomical Society

Position

Innovation Scientist

Twitter handle

pkgw

Homepage

https://newton.cx/~peter/

GitHub ID

pkgw


Sessions

10-25
21:00
30min
Interactively Visualizing Massive Images and Catalogs in Jupyter with AAS WorldWide Telescope
Peter K. G. Williams

Modern astronomical datasets are, of course, bigger than ever. Not only are they often too big to fit in most computers’ memories, more and more frequently they’re too big to even download at all. While the astronomical community has converged on a big-picture approach to this challenge — the web-based “science platform” concept — numerous specific engineering problems still need to be solved before astronomers will be fully equipped to handle the upcoming data deluge. In particular, the switch to browser-based UIs creates a need — and an opportunity — for web-native, interactive data visualization tools. This Focus Demo will showcase new capabilities that have recently been added to AAS WorldWide Telescope that empower users to interactively explore catalogs with billions of rows (using the HiPS progressive standard) and imagery with billions of pixels (using HiPS and tiled FITS formats). These features are tightly integrated with JupyterLab to provide one-click startup and dead-simple linkage with Python notebooks for data analysis. WWT’s efficient WebGL-based rendering is paired with a suite of user-friendly data-processing tools that make it easy to prepare data for visualization.

Grand Ballroom