| |
 |
| Paper: |
The Challenge of Astronomical Visualisation |
| Volume: |
61, Astronomical Data Analysis Software and Systems III |
| Page: |
51 |
| Authors: |
Norris, Ray P. |
| Abstract: |
Current astronomical instruments now routinely produce such large volumes of data that it becomes difficult for an astronomer to obtain the information he wants from the data. While the machines get faster and cheaper, and are certainly able to handle the load, and the astronomer's brain is certainly capable of interpreting the data, the interface between machine and brain prevents the astronomer from assembling the data in a coherent fashion in his mind, and so prevents him from being able to extract all the useful information which resides in his data. This is particularly true of large spectral-line data cubes. There are three aspects to overcoming this bottleneck between man and machine: <nl> Make the data analysis tools more user friendly, so that they cease to be a hindrance to most users. In particular, users need to be able to easily extract quantitative information from the images presented to them. Devise better ways of handling large multi-dimensional images so that the astronomer can hold in his mind some representation of the entire data set. For example, spectral-line data cubes can be viewed interactively as three-dimensional cubes, or even walked through in a virtual reality system. Such techniques enable the user to make use of the powerful image recognition capabilities of his brain. For example, a weak spiral arm may be only just above the noise in each plane of an HI data cube, and yet is obvious to the human brain (and is indeed physically significant) when the whole cube is viewed at once. When each of these two goals has been achieved, we need to combine them to devise better ways of obtaining quantitative data from large data sets. For example, a user needs to be able to obtain the mean and rms amplitude over a region immersed in the depths of a large data cube. </nl> In each of these three aspects, the challenge is to accomplish this while keeping the user in touch with the data, so that he will remain alert to problems or unexpected features in the data. In this talk I will demonstrate some of the efforts being made to overcome this man-machine bottleneck at the Australia Telescope. |
|
|
|
|
|
