Automated star-galaxy segregation using spectral and integrated band data for TAUVEX/ASTROSAT satellite data pipeline

TL;DR

This paper presents an ANN-based pipeline for automatically distinguishing stars from galaxies in UV data from TAUVEX/ASTROSAT, using synthetic and real spectra to train and test the model.

Contribution

It introduces a novel ANN approach utilizing both spectral and band-integrated data for star-galaxy classification in UV observations.

Findings

Full spectral features improve classification accuracy.

Band-integrated features are effective but less accurate.

The method is applicable to TAUVEX and ASTROSAT UV data.

Abstract

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.