http://arxiv.org/abs/1802.01233
We propose a new soft clustering scheme for classifying galaxies in different activity classes using simultaneously 4 emission-line ratios; log([NIII]/H{\alpha}), log([SII]/H{\alpha}), log([OI]/H{\alpha}) and log([OIII]/H{\beta}). We fit 20 multivariate Gaussian distributions to spectra obtained from the Sloan Digital Sky Survey (SDSS) in order to capture local structures and subsequently group the multivariate Gaussian distributions to represent the complex multi-dimensional structure of the joint distribution of galaxy spectra in the 4 dimensional line ratio space. The main advan- tages of this method are the use of all four optical-line ratios simultaneously and the adoption of a clustering scheme. This maximises the available information, avoids contradicting clas- sifications, and treats each class as a distribution resulting in soft classification boundaries. We also introduce linear multi-dimensional decision surfaces using support vector machines based on the classification of our soft clustering scheme. This linear multi-dimensional hard clustering technique shows high classification accuracy with respect to our soft-clustering scheme.
V. Stampoulis, D. Dyk, V. Kashyap, et. al.
Tue, 6 Feb 18
24/62
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