Browsing by Author "Bora, Archana"

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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.
3D Automated Classification Scheme for the TAUVEX data pipeline
(2007-11-28) Bora, Archana; Gupta, Ranjan; Singh, Harinder P.; et al.
In order to develop a pipeline for automated classi cation of stars to be observed by the TAUVEX ultraviolet space Telescope, we employ an arti cial 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 classi ed 229 stars from the IUE low resolution catalog to within 3-4 spectral sub-class using two di erent 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 classi ed the stars, but also provided satisfactory estimates for interstellar extinction. The ANN based classi cation 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.
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.
Automated star-galaxy segregation using spectral and integrated band data for TAUVEX/ASTROSAT satellite data pipeline
(2009-04-01) Bora, Archana; Harinder, P.; Gupta, Ranjan; et al.
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 3220Å 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.
Automated star–galaxy segregation using spectral and integrated band data for TAUVEX/ASTROSAT satellite data pipeline
(New Astronomy, 2009-10-13) Bora, Archana; Gupta, Ranjan; Singh, Harinder P; et.al
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 Å 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.