Astronomy and Astrophysics – Astronomy
Scientific paper
May 1998
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1998apjs..116...47b&link_type=abstract
Astrophysical Journal Supplement v.116, p.47
Astronomy and Astrophysics
Astronomy
15
Methods: Data Analysis, Techniques: Image Processing, Stars: General, Galaxies: General
Scientific paper
We are interested in examining different artificial intelligence techniques for classifying astronomical objects. In this study we use two different neural networks that utilize supervised learning: learning vector quantization and back-propagation. The networks are trained to distinguish stars and galaxies using an example base of 17 x 17 pixel images consisting of 60 galaxies and 27 stars extracted from the first-generation Digitized Sky Survey. For each neural network we use four different preprocessing methods to create input vectors from the pixelized images. We also use as input the "raw" image data consisting of a 289 (17 x 17) point vector. Our results show that both networks are capable of distinguishing stars and galaxies, with back-propagation working somewhat better in most cases. We discuss the details of the preprocessing methods and which methods work better in which cases.
Bazell David
Peng Yuan
No associations
LandOfFree
A Comparison of Neural Network Algorithms and Preprocessing Methods for Star-Galaxy Discrimination does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with A Comparison of Neural Network Algorithms and Preprocessing Methods for Star-Galaxy Discrimination, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and A Comparison of Neural Network Algorithms and Preprocessing Methods for Star-Galaxy Discrimination will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-1367986