Interactively exploring and visualizing data on the sky with Jupyter and pywwt
Sometimes, astronomers view image data with very specific goals in mind, but often, they are interested in open-ended exploration and discovery, hoping to gain new insight by comparing against multiwavelength survey data or preexisting source catalogs. Participants in this tutorial will learn how to use a powerful tool that enables this discovery from the comfort of a Jupyter notebook: pywwt, a module that allows researchers to embed the sophisticated AAS WorldWide Telescope (WWT) visualization engine in Python applications and Jupyter notebooks. The WWT engine allows scientific data to be intermingled with an interactive, 4D model of the known universe seeded with survey data from lunar surface maps to all-sky surveys across the EM spectrum. The “ds9-like” experience provided by pywwt can be controlled through code as well as manually, and the engine that it controls can be embedded anywhere that a Web browser can run since it is built on HTML, JavaScript, and WebGL. Hands-on activities will stitch the pywwt module together with other elements of the modern Python ecosystem for working with astronomical data such as astroquery. Bring your sky images or source catalogs!
