Professor Ranjan Gupta

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    A near-infrared stellar spectral library: III. J-band spectra
    (Bull. Astr. Soc. India, 2007-09-03) Ranade, Arvind C; Ashok, N.M; Singh, Harinder P
    This paper is the third in the series of papers published on near- infrared (NIR) stellar spectral library by Ranade et al. (2004 & 2007). The observations were carried out with 1.2 meter Gurushikhar Infrared Telescope (GIRT), at Mt. Abu, India using a NICMOS3 HgCdTe 256 £ 256 NIR array based spectrometer. In paper I (Ranade et al. 2004), H-band spectra of 135 stars at a resolution of » 16 ºA & paper II (Ranade et al. 2007), K band spectra of 114 stars at a resolution of » 22 ºA were presented. The J-band library being released now consists of 126 stars covering spectral types O5{ M8 and luminosity classes I{V. The spectra have a moderate resolution of » 12:5 ºA in the J band and have been continuum shape corrected to their respective e®ective temperatures. The complete set of library in near-infrared (NIR) will serve as a good database for researchers working in the ¯eld of stellar population synthesis. The complete library in J, H & K is available online at: http://vo.iucaa.ernet.in/»voi/NIR Header.html
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    Cross-checking reliability of some available stellar spectral libraries using artificial neural networks
    (Stellar Populations as Building Blocks of Galaxies Proceedings IAU Symposium, 2006-06-25) Gupta, Ranjan; Singh, S. Jotin; Singh, Harinder P
    Cross-checking the reliability of various stellar spectral databases is an important and desirable exercise. Since number of stars in various databases have no known spectral types and some of the libraries do not have complete coverage resulting in gaps. We use an automated classification scheme based on Artificial Neural Networks (ANN) to cross-classify stars in the Indo-US stellar spectral library (Valdes et al. 2004), JHC (Jacoby, Hunter & Christian 1984), ELODIE spectra (Moultaka et al. 2004) and STELIB (Le Borgne et al. 2003). We have also examined the effects of over-training and over-fitting on the classification efficiency of a Neural Network. It is hoped that such a automated data analysis and validation technique will be useful in the future.
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    An analysis of the distribution of background star
    (Mon. Not. R. Astron. Soc, 2002-02-02) Sen, A.K.; Mukai, T; Gupta, Ranjan; et al.
    The polarization observed for stars background to dark clouds (Bok Globules) is often used as diagnostic to study the ongoing star formation processes in these clouds. Such polarization maps in the optical have been reported for eight nearby clouds CB3, CB25, CB39, CB52, CB54, CB58, CB62 and CB246 in one of our previous work (Sen et al 2000). With a view to understand the origin of this polarization, in the present work attempts are made to look for any possible relation between this observed polarization and other physical parameters in the cloud (like temperature, turbulence etc.). The observed polarization does not seem to be clearly related to the dust and gas temperatures (Td and Tg) in the cloud as expected from Davis-Greenstein grain alignment mechanism (Davis & Greenstein 1952). However, the average observed polarization (pav) appears to be related to the turbulence V (measured by 12CO line width) by the mathematical relation pav = 2.95 exp(−0.24 V ). The possible relation between the direction of polarization vector and other physical parameters are also discussed. For this analysis in addition to the data on above eight dark clouds, the data on CB4 (Kane et al. 1995) are also included for comparison. In order to study the spatial distribution of the degree of polarization and position angles across the different parts of the cloud a simple model is proposed, where the cloud has been assumed to be a simple dichroic polarizing sphere and the light from the background star first passes through the IS medium and then through the cloud, before reaching the observer. One finds this simple model can explain to a reasonable extent the observed spatial (radial) dependence of the value of p for two of the clouds (CB25 , CB39), but for rest of the clouds the model fails. However, through this model one can explain why the polarization (p) need not always increase with total extinction Av as one moves in the deeper interior part of the cloud.