Automated Classification of 2000 Bright IRAS Sources
| dc.contributor.author | Gupta, Ranjan | |
| dc.contributor.author | Singh, Harinder P. | |
| dc.contributor.author | Volk, K. | |
| dc.contributor.author | et al. | |
| dc.date.accessioned | 2012-03-07T05:25:42Z | |
| dc.date.available | 2012-03-07T05:25:42Z | |
| dc.date.issued | 2011-07-06 | |
| dc.description.abstract | An Artificial Neural Network (ANN) scheme has been employed that uses a supervised back-propagation algorithm to classify 2000 bright sources from the Calgary database of IRAS (Infrared Astronomical Satellite) spectra in the region 8µm to 23µm. The database has been classified into 17 predefined classes based on the spectral morphology. We have been able to classify over 80 percent of the sources correctly in the first instance. The speed and robustness of the scheme will allow us to classify the whole of the LRS database, containing more that 50,000 sources, in the near future. | en_US |
| dc.identifier.uri | http://hdl.handle.net/11007/796 | |
| dc.language.iso | en | en_US |
| dc.relation.ispartofseries | IUCAA Preprints;08/04 | |
| dc.subject | Galaxies | en_US |
| dc.subject | Methods: data analysis | en_US |
| dc.title | Automated Classification of 2000 Bright IRAS Sources | en_US |
| dc.type | Article | en_US |