CCD photometry inVRI bands of the galactic cluster NGC 2818

bdAbstract The open cluster NGC 2818 containing a planetary nebula has been observed inVRI bands using the CCD system at prime focus of the 2.3-metre Vainu Bappu Telescope. The study extending to starsV ∼ 21 magnitude establishes the distance modulus as(m-M) ₀ = 12.9 ±0.1 for the cluster. Based on the fitting of theoretical isochrones computed for solar metallicity, an age of 5(±1) × 10₈ years has been assigned to the cluster. Association of the planetary nebula with the cluster indicates that the progenitor mass of the planetary nebula on the main sequence is ≥2.5M_⊙ Based on observations obtained with the Vainu Bappu Telescope.     

Is dark matter created in the gravitation field of galaxies?

Using the heuristic arguments of quantum physics we describe a new mechanism of the creation of short-living particles from the virtual ones in a stationary gravitation field. The mass of these particles is a function of the intensity of gravitation field. We suppose that the particles created in the gravitation field form a part of the non-baryonic dark matter. Having the intensity of gravitation field in a galaxy we can calculate the density of dark matter created in it by the vacuum quantum fluctuation. We calculate the distribution of this dark matter in a model galaxy and show that its total mass is comparable with the visible mass of the galaxy.     

Interactions and the Evolution of Cluster Galaxies

We have developed model predictions for the morphological distribution of cluster galaxies as a function of the cluster-centric distance and the local galaxy density, using a semi-analytical code. This code allows us to obtain magnitudes and colours for cluster galaxies at different redshifts, and thus to study in detail the evolution of the colour–magnitude relation of specific distant clusters. This revised version was published online in July 2006 with corrections to the Cover Date.     

Prediction of Stellar Atmospheric Parameters using Instance-Based Machine Learning and Genetic Algorithms

In this article we present a method for the automated prediction of stellar atmospheric parameters from spectral indices. This method uses a genetic algorithm (GA) for the selection of relevant spectral indices and prototypical stars and predicts their properties, using the k-nearest neighbors method (KNN). We have applied the method to predict the effective temperature, surface gravity, metallicity, luminosity class and spectral class of stars from spectral indices. Our experimental results show that the feature selection performed by the genetic algorithm reduces the running time of KNN up to 92%, and the predictive accuracy error up to 35%. This revised version was published online in July 2006 with corrections to the Cover Date.     

The expansive nondecelerative universe

(a) Hubble's discovery of the expansion of the Universe makes it possible to choose unambiguously from the models described by Friedmann's equations of universe dynamics. (b) From the present temperature of the cosmic microwave background radiation, the specific entropy in the matter era and the model properties of the expansive nondecelerative universe, we can determine the present parameters of our Universe with deviations smaller than 2.2%.     

Testing the consistency of the Starburst scenario for active galactic nuclei

In the frame of the Starburst Model, we show that the evolution of a massive stellar cluster in a high metallicity environment can reproduce the observed emission-line spectrum and the UV-optical Spectral Energy Distribution of the Seyfert 2 galaxies and LINERs. We apply the results of our models to three objects: NGC 5506, NGC 5643 and Mk 348.