Automated star-galaxy segregation using spectral and integrated band
  data for TAUVEX/ASTROSAT satellite data pipeline
  We employ an Artificial Neural Network (ANN) based technique to develop a
pipeline for automated segregation of stars from the galaxies to be observed by
Tel-Aviv University Ultra-Violet Experiment (TAUVEX). We use synthetic spectra
of stars from UVBLUE library and selected International Ultraviolet Explorer
(IUE) low resolution spectra for galaxies in the ultraviolet (UV) region from
1250 to 3220\AA as the training set and IUE low-resolution spectra for both the
stars and the galaxies as the test set. All the data sets have been
pre-processed to get band integrated fluxes so as to mimic the observations of
the TAUVEX UV imager. We also perform the ANN based segregation scheme using
the full length spectral features (which will also be useful for the ASTROSAT
mission). Our results suggest that, in the case of the non-availability of full
spectral features, the limited band integrated features can be used to
segregate the two classes of objects; although the band data classification is
less accurate than the full spectral data classification.
