Research Papers (RG)

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Author: search.filters.author.Singh, Harinder PSubject: search.filters.subject.Stars

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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.