132 research outputs found
A Study of Quasar Radio Emission from the VLA FIRST Survey
Using the most recent (1998) version of the VLA FIRST survey radio catalog,
we have searched for radio emission from 1704 quasars taken from the most
recent (1993) version of the Hewitt and Burbidge quasar catalog. These quasars
lie in the ~5000 square degrees of sky already covered by the VLA FIRST survey.
Our work has resulted in positive detection of radio emission from 389 quasars
of which 69 quasars have been detected for the first time at radio wavelengths.
We find no evidence of correlation between optical and radio luminosities for
optically selected quasars. We find indications of a bimodal distribution of
radio luminosity, even at a low flux limit of 1 mJy. We show that radio
luminosity is a good discriminant between radio loud and radio quiet quasar
populations, and that it may be inappropriate to make such a division on the
basis of the radio to optical luminosity ratio. We discuss the dependence of
the radio loud fraction on optical luminosity and redshift.Comment: 33 pages LaTeX, 10 figures, 2 tables. Accepted in the Astronomical
Journa
Seyfert 1 composite spectrum using SDSS Legacy survey data
We present a rest-frame composite spectrum for Seyfert 1 galaxies using spectra obtained from the 12th Data Release of the Sloan Digital Sky Survey. The spectrum is constructed by combining data from a total of 10112 galaxies, spanning a redshift range of 0–0.793. We produce an electronic table of the median and geometric mean composite Seyfert 1 spectrum. We measure the spectral index of the composite spectrum, and compare it with that of the composite quasar spectrum. We also measure the flux and width of the strong emission lines present in the composite spectrum. We compare the entire spectrum with the quasar spectrum in the context of the unification model for active galactic nuclei. The two composite spectra match extremely well in the blue part of the spectrum, while there is an offset in flux in the red portion of the spectrum
Estimating Photometric Redshifts Using Support Vector Machines
We present a new approach to obtaining photometric redshifts using a kernel
learning technique called Support Vector Machines (SVMs). Unlike traditional
spectral energy distribution fitting, this technique requires a large and
representative training set. When one is available, however, it is likely to
produce results that are comparable to the best obtained using template fitting
and artificial neural networks. Additional photometric parameters such as
morphology, size and surface brightness can be easily incorporated. The
technique is demonstrated using samples of galaxies from the Sloan Digital Sky
Survey Data Release 2 and the hybrid galaxy formation code GalICS. The RMS
error in redshift estimation is for both samples. The strengths and
limitations of the technique are assessed.Comment: 10 pages, 3 figures, to appear in the PASP, minor typos fixed to make
consistent with published versio
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