Abstract:
In order to develop a pipeline for automated classification of stars to be observed by the Tel-Aviv University Ultra-Violet Experiment (TAUVEX) ultraviolet space telescope, we employ an artificial neural network (ANN) technique for classifying stars by using synthetic spectra in the ultraviolet (UV) region from 1250 to 3220 Å as the training set and International Ultraviolet Explorer (IUE) low-resolution spectra as the test set. Both the data sets have been pre-processed to mimic the observations of the TAUVEX UV imager. We have successfully classified 229 stars from the IUE low-resolution catalogue to within three to four spectral subclass using two different simulated training spectra, the TAUVEX spectra of 286 spectral types and UVBLUE (http://www.inaoep.mx/~modelos/uvblue/uvblue.html) spectra of 277 spectral types. Further, we have also been able to obtain the colour excess [i.e. E(B − V) in magnitude units] or the interstellar reddening for those IUE spectra which have known reddening to an accuracy of better than 0.1 mag. It has been shown that even with the limitation of data from just photometric bands, ANNs have not only classified the stars, but also provided satisfactory estimates for interstellar extinction. The ANN based classification scheme has been successfully tested on the simulated TAUVEX data pipeline. It is expected that the same technique can be employed for data validation in the UV from the virtual observatories. Finally, the interstellar extinction estimated by applying the ANNs on the TAUVEX data base would provide an extensive extinction map for our Galaxy and which could in turn be modelled for the dust distribution in the Galaxy.
