Rhys Taylor's home page
Science, Art and Data Visualisation

Data Visualisation

There are two reasons I became an astronomer : 1) because Star Trek was cool; 2) pretty pictures of nebulae. While Star Trek may indeed be inspirational it doesn't really have any direct lessons for astronomy, but the importance of pretty pictures cannot be overstated. Or more accurately, how we go about turning raw data from telescopes into something we can understand really matters. Especially so for radio astronomy. With telescopes receiving light well beyond the visual wavelengths, we have to apply some quite creative methods to transform the data into something visibly meaningful.

The advantage of this is we have a great deal of freedom in how we choose to process the data. While radio astronomers almost never "listen" to the data (that's usually a bad idea), in some ways it does have more in common with sonar than optical astronomy. We generally receive not just how bright the gas is, but how fast it's moving – the Doppler shift we're more familiar with from sound waves. So we have 3D data sets but where one dimension is velocity, not space, and we have the particular concern that most of the data is generally noise rather than signal.

On these pages I describe the techniques I use for processing the data in different ways, from the mundane methods of "cleaning" the data to improve the signal-to-noise, to displaying data cubes as 3D volumes in Blender's real-time display. Specific projects will be given in the still-to-be-written Art section. All of the accompanying code will be given in the (also still-to-be-written !) Code section. For more on the data structure itself, see this page.

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Dark Galaxies
Eight Clouds In Virgo
Keenan's Ring
The Leo Ring And Its Clouds
The Tully-Fisher Relation
Ram Pressure Stripping
Tidal Encounters
Source Extraction
Understanding Catalogues
Data Structure
Stacking Galaxies
Diffuse Gas
Research Experience
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My Publications
Technical Skills
Data Visualisation
Data Cleaning
Basic FITS in Blender
Advanced FITS in Blender
Non-Cartesian Data
N-body Simulations
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