Research Publications

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    A 3D Automated Classification Scheme for the TAUVEX data pipeline
    (Mon. Not. R. Astron. Soc., 2007-02-02) Bora, Archana; Gupta, Ranjan; Singh, Harinder P; et.al
    In order to develop a pipeline for automated classification of stars to be observed by the TAUVEX ultraviolet space Telescope, we employ an artificial neural network (ANN) technique for classifying stars by using synthetic spectra in the UV region from 1250°A to 3220°A 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 ultraviolet imager. We have successfully classified 229 stars from the IUE low resolution catalog to within 3-4 spectral sub-class using two different simulated training spectra, the TAUVEX spectra of 286 spectral types and UVBLUE 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 magnitudes. 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 ultraviolet 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 modeled for the dust distribution in the galaxy.
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    A 3D Automated Classification Scheme for the TAUVEX data pipeline
    (Mon. Not. R. Astron. Soc, 2007-01-28) Bora, Archana; Gupta, Ranjan; Singh, Harinder P; et
    In order to develop a pipeline for automated classification of stars to be observed by the TAUVEX ultraviolet space Telescope, we employ an artificial neural network (ANN) technique for classifying stars by using synthetic spectra in the UV region from 1250°A to 3220°A 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 ultraviolet imager. We have successfully classified 229 stars from the IUE low resolution catalog to within 3-4 spectral sub-class using two different simulated training spectra, the TAUVEX spectra of 286 spectral types and UVBLUE 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 magnitudes. 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 ultraviolet 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 modeled for the dust distribution in the galaxy.
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    Possible interpretations of the magnitude-redshift relation for supernovae of type IA
    (American Astronomical Society, 2000-06-23) Banerjee, Shyamal K.; Narlikar, J. V.; Wickramasinghe, N. C.; et al.
    It has been shown by Riess et al. and Perlmutter et al. that the observed redshift-magnitude relation for supernovae of type Ia, which suggests that the deceleration parameter is negative, can be explained in a Friedmann model with a positive cosmological constant. We show that a quasi-steady state cosmology (QSSC) model can also fit the supernova data. Since most of the emphasis and publicity have been concentrated on explanations involving the Friedmann model, we show how a good Ðt can be obtained to the observations in the framework of the QSSC. Using this model, we show that absorption due to intergalactic dust may play an important role. This may explain why a few of the supernovae observed show large deviations from the curve determined by the majority of the data.
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    Log N-log S curve for 3CR radio galaxies and the problem of identifying faint radio galaxies
    (American Astronomical Society, 1976-04-15) Burbidge, G.; Narlikar, J. V.
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    Theoretician’s analysis of the supernova data and the limitations in determining the nature of dark energy
    (Wiley-Blackwell, 2003-06-02) Padmanabhan, T.; Choudhury, T. Roy