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| Paper: |
Edge Detection Techniques for Automatic Location of Spectra |
| Volume: |
461, Astronomical Data Analysis Software and Systems XXI |
| Page: |
589 |
| Authors: |
Zarate, N.; Labrie, K. |
| Abstract: |
To improve the processing of multi-object or cross-dispersed spectroscopic data,
especially for systems resulting in curved 2-D spectra, we have implemented in
Python edge detection techniques widely used in the photo processing and remote
sensing world. The software uses the discontinuity found in a spectral image to
precisely locate each dispersed 2-D spectrum on the pixel array. A valid
spectrum image edge is defined as continuous and sharp. To this end the best
input data is a well illuminated flat field. The algorithm applies a
discontinuity detection filter to the image. We find that a 3 × 3 Sobel kernel
reliably produces easily traceable edges on our data. Some instruments produce
data with large background noise. In those cases, a mild smoothing filter is
first applied to reduce noise spikes that would otherwise confuse the edge
tracing algorithm. The edges highlighted by the filtering are traced using the
SciPy function label. Each edge is represented by a second degree
polynomial that follows each slit edge. Currently the software assumes that the
spectra are nearly horizontal or nearly vertical. This constraint can easily be
lifted with the choice of a different convolution kernel. |
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