UVIT FUV catalogues of ELAIS N1 plus CIGALE SED fits show SFR increasing with redshift and a flat young-to-total stellar mass ratio to z≲0.76, consistent with secular SFMS evolution.
The SDSS Quasar Survey: Quasar Luminosity Function from Data Release Three
2 Pith papers cite this work. Polarity classification is still indexing.
abstract
We determine the number counts and z=0-5 luminosity function for a well-defined, homogeneous sample of quasars from the Sloan Digital Sky Survey (SDSS). We conservatively define the most uniform statistical sample possible, consisting of 15,343 quasars within an effective area of 1622 deg^2 that was derived from a parent sample of 46,420 spectroscopically confirmed broad-line quasars in the 5282 deg^2 of imaging data from SDSS Data Release Three. The sample extends from i=15 to i=19.1 at z<3 and to i=20.2 for z>3. The number counts and luminosity function agree well with the results of the 2dF QSO Survey, but the SDSS data probe to much higher redshifts than does the 2dF sample. The number density of luminous quasars peaks between redshifts 2 and 3, although uncertainties in the selection function in this range do not allow us to determine the peak redshift more precisely. Our best fit model has a flatter bright end slope at high redshift than at low redshift. For z<2.4 the data are best fit by a redshift-independent slope of beta = -3.1 (Phi(L) propto L^beta). Above z=2.4 the slope flattens with redshift to beta=-2.37 at z=5. This slope change, which is significant at a >5-sigma level, must be accounted for in models of the evolution of accretion onto supermassive black holes.
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Review of machine learning applications for analyzing Lyman-alpha forest observations to probe cosmology, reionization, and dark matter.
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Deep far-UV observations of the ELAIS N1 field using AstroSat: Source catalogue, spectral energy distribution modelling and star formation
UVIT FUV catalogues of ELAIS N1 plus CIGALE SED fits show SFR increasing with redshift and a flat young-to-total stellar mass ratio to z≲0.76, consistent with secular SFMS evolution.
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Machine Learning Techniques for Astrophysics and Cosmology: Lyman-$\alpha$ forest
Review of machine learning applications for analyzing Lyman-alpha forest observations to probe cosmology, reionization, and dark matter.