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Item Infrared Emission from the Composite Grains: Effects of Inclusions and Porosities on the 10 and 18 μm Features(Astronomy & Astrophysics manuscript, 2011-01-11) Vaidya, D.B.; Gupta, RanjanAims. In this paper we study the effects of inclusions and porosities on the emission properties of silicate grains and compare the model curves with the observed infrared emission from circumstellar dust. Methods. We calculate the absorption efficiency of the composite grain, made up of a host silicate oblate spheroid and inclusions of ice/graphite/or voids, in the spectral region 5.0-25.0μm. The absorption efficiencies of the composite spheroidal oblate grains for three axial ratios are computed using the discrete dipole approximation (DDA). We study the absorption as a function of the volume fraction of the inclusions and porosity. In particular, we study the variation in the 10μm and 18μm emission features with the volume fraction of the inclusions and porosities. We then calculate the infrared fluxes for these composite grains at several dust temperatures (T=200-350K) and compare the model curves with the average observed IRAS-LRS curve, obtained for circumstellar dust shells around oxygen rich M-type stars. The model curves are also compared with two other individual stars. Results. The results on the composite grains show variation in the absorption efficiencies with the variation in the inclusions and porosities. In particular, it is found that the wavelength of peak absorption at 10μm, shifts towards longer wavelengths with variation in the volume fraction of the inclusions of graphite. The spheroidal composite grains with axial ratio ∼ 1.33; volume fraction of f=0.1 and dust temperature between 210-340K, fit the observed infra-red emission from circumstellar dust reasonably well in the wavelength range 5-25μm. The model flux ratio, R=Flux(18μ)/Flux(10μ), compares well with the observed ratio for the circumstellar dust. Conclusions. The results on the composite grains clearly indicate that the silicate feature at 10μm shifts with the volume fraction of graphite inclusions. The feature does not shift with the porosity. Both the features do not show any broadening with the inclusions or porosity. The absorption efficiencies of the composite grains calculated using DDA and Effective Medium Approximation (EMA) do not agree. The composite grain models presented in this study need to be compared with the observed IR emission from the circumstellar dust around a few more stars.Item Optical Spectroscopy of Candidates of Young Stellar Objects in NGC 1333(2001-01-01) Itoh, Yoichi; Gupta, Ranjan; Oasa, Yumiko; et.alWe carried out low-resolution optical spectroscopy of 14 low-luminosity young stellar object (YSO) candidates in the NGC 1333 cluster. These objects were previously identified by the near-infrared imaging survey. Eleven objects were confirmed as YSOs by the H line emission. Strengths of the H emission are correlated with the near-infrared excesses of the objects. Spectral types of all YSOs are estimated to be M-type, indicative of low-mass. Comparisons of the results of our spectroscopic observations and the previous photometric observations with evolutionary tracks on the HR diagram suggest two objects to be young brown dwarfs.Item 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.alWe 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.Item Frequency andsizedistributiondependenceofvisibleandinfrared extinctionforastronomicalsilicateandgraphitegrains(JournalofQuantitativeSpectroscopy& RadiativeTransfer, 2011-10-09) Roy, Ashim K; Sharma, Subodh K; Gupta, RanjanIn arecentpaper,thefrequencyandsizedistributiondependenceofextinctionspectra for astronomicalsilicateandgraphitegrainswasanalyzedinthecontextofMRNtype interstellardustmodelsinthefarultravioletandultravioletregions.Thesegrains were takentobehomogeneousspheresfollowingapowerlawsizedistribution.Inthe present workweextendtheanalysisfurthertocoverthevisibleaswellastheinfrared part oftheelectromagneticspectrum.Theanalyticformulaspresentedherealongwith thosegivenintheearlierpaperwouldenableonetoevaluateextinctionforthesegrains withinawiderwavelengthrange1000–22,500 ˚A andanalyzetheobservational interstellarextinctiondatainfargreaterdetailsItem Composite interstellar grains and the 2175˚A feature(Organic Matter in Space Proceedings IAU Symposium, 2008-08-14) Vaidya, D.B.; Gupta, RanjanWe use discrete dipole approximation (DDA) to study the scattering properties of composite grains made up of host silicate spheroids and graphite inclusions. We calculate the extinction cross sections of the composite grains in the wavelength region 0.20–0.55 μm and study the extinction of the composite grains as a function of graphite inclusions. We present the composite grain model and discuss the results.Item Automated classification of sloan digital sky survey (SDSS) stellar spectra using artificial neural networks(Astrophys Space Sci, 2008-04-21) Bazarghan, Mahdi; Gupta, RanjanAutomated techniques have been developed to automate the process of classification of objects or their analysis. The large datasets provided by upcoming spectroscopic surveys with dedicated telescopes urges scientists to use these automated techniques for analysis of such large datasets which are now available to the community. Sloan Digital Sky Survey (SDSS) is one of such surveys releasing massive datasets. We use Probabilistic Neural Network (PNN) for automatic classification of about 5000 SDSS spectra into 158 spectral type of a reference library ranging from O type to M type stars.Item 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.alIn 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.Item Scattering Properties and Composition of Cometary Dust(Astrophysics and Space Science, 2005-04-11) Gupta, Ranjan; Vaidya, D.B.; Bobbie, J.S; et.alComposition of the Comet dust obtained by the dust impact analyzer on the Halley probes indicated that the comet dust is a mixture of silicate and carbonaceous material. The collected interplanetary dust particles (IDP’s) are fluffy and composite, having grains of several different types stuck together. Using discrete dipole approximation (DDA) we study the scattering properties of composite grains. In particular, we study the angular distribution of the scattered intensity and linear polarization of composite grains.We assume that the composite grains are made up of a host silicate sphere/spheroid with the inclusions of graphite. Results of our calculations on the composite grains show that the angle of maximum polarization shifts, and the degree of polarization varies with the volume fraction of the inclusions.We use these results on the composite grains to interpret the observed scattering in cometary dust.Item Automated classification of sloan digital sky survey (SDSS) stellar spectra using artificial neural networks(Astrophys Space Sci, 2008-04-21) Bazarghan, Mahdi; Gupta, RanjanAutomated techniques have been developed to automate the process of classification of objects or their analysis. The large datasets provided by upcoming spectroscopic surveys with dedicated telescopes urges scientists to use these automated techniques for analysis of such large datasets which are now available to the community. Sloan Digital Sky Survey (SDSS) is one of such surveys releasing massive datasets. We use Probabilistic Neural Network (PNN) for automatic classification of about 5000 SDSS spectra into 158 spectral type of aItem 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; etIn 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.
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