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Author: search.filters.author.Wadadekar, YogeshSubject: search.filters.subject.Galaxies: fundamental parameters

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    Near infrared photometric plane for ellipticals and bulges of spirals
    (2000-09-20) Khosroshahi, Habib G.; Wadadekar, Yogesh; Kembhavi, A.K.; et al.
    We report the existence of a single plane in the space of global photometric pa- rameters describing elliptical galaxies and the bulges of early type spiral galaxies. The three parameters which define the plane are obtained by fitting the Sersic form to the brightness distribution obtained from near-infrared K band images. We find, from the range covered by their shape parameters, that the elliptical galaxies form a more ho- mogeneous population than the bulges. Known correlations like the Kormendy relation are projections of the photometric plane. The existence of the plane has interesting implications for bulge formation models.
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    Difference boosting neural network for automated star-galaxy classification
    (2002-03-01) Philip, Ninan S.; Wadadekar, Yogesh; Kembhavi, A.K.; et al.
    In this paper we describe the use of a new artificial neural network, called the difference boosting neural network (DBNN), for automated classification problems in astronomical data analysis. We illustrate the capabilities of the network by applying it to star galaxy classification using recently released, deep imaging data. We have compared our results with classification made by the widely used Source Extractor (SExtractor) package. We show that while the performance of the DBNN in star-galaxy classification is comparable to that of SExtractor, it has the advantage of significantly higher speed and flexibility during training as well as classification.