Shape-preserving LADE models with fixed local LF shape provide the statistically preferred description of UV QLF evolution to z~7.5 over flexible alternatives based on AIC/BIC.
The SDSS-III Baryon Oscillation Spectroscopic Survey: The Quasar Luminosity Function from Data Release Nine
2 Pith papers cite this work. Polarity classification is still indexing.
abstract
We present a new measurement of the optical Quasar Luminosity Function (QLF), using data from the Sloan Digital Sky Survey-III: Baryon Oscillation Spectroscopic Survey (SDSS-III: BOSS). From the SDSS-III Data Release Nine (DR9), we select a uniform sample of 22,301 i<=21.8 quasars over an area of 2236 sq. deg with confirmed spectroscopic redshifts between 2.2<z<3.5, filling in a key part of the luminosity-redshift plane for optical quasar studies. We derive the completeness of the survey through simulated quasar photometry, and check this completeness estimate using a sample of quasars selected by their photometric variability within the BOSS footprint. We investigate the level of systematics associated with our quasar sample using the simulations, in the process generating color-redshift relations and a new quasar k-correction. We probe the faint end of the QLF to M_i(z=2.2) = -24.5 and see a clear break in the QLF at all redshifts up to z=3.5. We find that a log-linear relation (in log[Phi*] - M*) for a luminosity and density evolution (LEDE) model adequately describes our data within the range 2.2<z<3.5; across this interval the break luminosity increases by a factor of ~2.3 while Phi* declines by a factor of ~6. At z<2.2 our data is reasonably well fit by a pure luminosity evolution (PLE) model. We see only a weak signature of "AGN downsizing", in line with recent studies of the hard X-ray luminosity function. We compare our measured QLF to a number of theoretical models and find that models making a variety of assumptions about quasar triggering and halo occupation can fit our data over a wide range of redshifts and luminosities.
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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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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.