Unresolved Galaxy Classifier for ESA/Gaia mission: Support Vector Machines approach
Abstract
A software package Unresolved Galaxy Classifier (UGC) is being developed for the ground-based pipeline of ESA's Gaia mission. It aims to provide an automated taxonomic classification and specific parameters estimation analyzing Gaia BP/RP instrument low-dispersion spectra of unresolved galaxies. The UGC algorithm is based on a supervised learning technique, the Support Vector Machines (SVM). The software is implemented in Java as two separate modules. An offline learning module provides functions for SVM-models training. Once trained, the set of models can be repeatedly applied to unknown galaxy spectra by the pipeline's application module. A library of galaxy models synthetic spectra, simulated for the BP/RP instrument, is used to train and test the modules. Science tests show a very good classification performance of UGC and relatively good regression performance, except for some of the parameters. Possible approaches to improve the performance are discussed.
- Publication:
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Bulgarian Astronomical Journal
- Pub Date:
- June 2012
- Bibcode:
- 2012BlgAJ..18b...3B
- Keywords:
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- Methods: data analysis;
- Techniques: miscellaneous;
- Galaxies: general