Automatic Spectral Classification of Galaxies in the Infrared

Multi-object spectroscopy (MOS) provides us with numerous spectral data, and the projected new facilities and survey missions will increment the available spectra from stars and galaxies. In order to better understand this huge amount of data we need to develop new techniques of analysis and classification. Over the past decades it has been demonstrated that artificial neural networks are excellent tools for automatic spectral classification and identification, being robust tools and highly resistant to the presence of noise. We present here the result of the application of unsupervised neural networks: competitive neural networks (CNN) and self organized maps (SOM), to a sample of 747 galaxy spectra from the Infrared Spectrograph (IRS) of Spitzer. We obtained an automatic classification on 17 groups with the CNN,-and we compare the results with those obtained with SOMs.The final goal of the project is to develop an automatic spectral classification tool for galaxies in the infrared, making use of artificial neural networks with unsupervised training and analyze the spectral characteristics of the galaxies that can give us clues to the physical processes taking place inside them.