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 | 2012-07-31T13:39:24Z | |
| dc.date.available | 2012-07-31T13:39:24Z | |
| dc.date.issued | 2004-06-25 | |
| 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 Infrared Astronomical Satellite (IRAS) spectra in the region 8–23 m. The database has been classified into 17 predefined classes based on the spectral morphology. We have been able to classify over 80% of the sources correctly in the first instance. The speed and robustness of the scheme will allow us to classify the whole of the Low Resolution Spectrometer database, containing more than 50,000 sources, in the near future. Subject headings: infrared: galaxies — methods: data analysis | en_US |
| dc.identifier.uri | http://hdl.handle.net/11007/1896 | |
| dc.language.iso | en | en_US |
| dc.publisher | The Astrophysical Journal Supplement Series | en_US |
| dc.subject | Infrared | en_US |
| dc.subject | Galaxies — methods | en_US |
| dc.subject | Data analysis On-line material | en_US |
| dc.subject | Machine-readable table | en_US |
| dc.title | AUTOMATED CLASSIFICATION OF 2000 BRIGHT IRAS SOURCES | en_US |
| dc.type | Article | en_US |