The Extremely Luminous Quasar Survey in the Pan-STARRS 1 Footprint (PS-ELQS)

Abstract

We present the results of the Extremely Luminous Quasar Survey in the 3π survey of the Panoramic Survey Telescope and Rapid Response System (Pan-STARRS; PS1). This effort applies the successful quasar selection strategy of the Extremely Luminous Survey in the Sloan Digital Sky Survey footprint (∼ 12, 000 deg 2 ) to a much larger area (∼ 21486 deg 2 ). This spectroscopic survey targets the most luminous quasars (M 1450 ≤ −26.5; m i ≤ 18.5) at intermediate redshifts (z ≥ 2.8). Candidates are selected based on a near-infrared JKW2 color cut using WISE AllWISE and 2MASS photometry to mainly reject stellar contaminants. Photometric redshifts (z reg ) and star-quasar classifications for each candidate are calculated from near-infrared and optical photometry using the supervised machine learning technique random forests. We select 806 quasar candidates at z reg ≥ 2.8 from a parent sample of 74318 sources. After exclusion of known sources and rejection of candidates with unreliable photometry, we have taken optical identification spectra for 290 of our 334 good PS-ELQS candidates. We report the discovery of 190 new z ≥ 2.8 quasars and an additional 28 quasars at lower redshifts. A total of 44 good PS-ELQS candidates remain unobserved. Including all known quasars at z ≥ 2.8, our quasar selection method has a selection efficiency of at least 77%. At lower declinations −30 ≤ Decl. ≤ 0 we approximately triple the known population of extremely luminous quasars. We provide the PS-ELQS quasar catalog with a total of 592 luminous quasars (m i ≤ 18.5, z ≥ 2.8). This unique sample will not only be able to provide constraints on the volume density and quasar clustering of extremely luminous quasars, but also offers valuable targets for studies of the intergalactic medium.

The Luminosity Function of bright QSOs at z~4 and implications for the cosmic ionizing background

“…The error bars of our data points are significantly smaller than the SDSS and the ones presented by Schindler et al (2019a,b). Results from Schindler et al (2019b) indicate that SDSS can be incomplete at ∼ 40% level, confirming previous values by Fontanot et al (2007). The results of the QUBRICS survey, shown in Fig.…”

The space density of ultra-luminous QSOs at the end of reionization epoch by the QUBRICS Survey and the AGN contribution to the hydrogen ionizing background

“…A similar result has been obtained also by Onken et al (2021) at z > 4.4 in a sky area partially overlapping with the QUBRICS survey. The reason for the huge discrepancies with respect to previous surveys is probably due to more efficient and complete selection criteria on the recent surveys, as shown by Schindler et al (2019b) at z > 3 or by Boutsia et al (2021) at z ∼ 4.…”

“…2 (bottom part) shows the space density of bright QSOs at M 1450 = −28.50 as a function of the mean redshifts of the surveys at z > 2.5 collected from the literature. Fitting only the z <= 5.0 data, obtained by Schindler et al (2019b) at z = 2.90 and 3.25, by the QUBRICS survey at z = 3.9 by Boutsia et al (2021), at z = 4.75 (this work) and at z = 5.0 by Yang et al (2016), we obtain a best-fit parameter of γ = −0.25, by adopting an evolution of the space density as Φ(z) = Φ(z = 4.0) • 10 γ•(z−4.0) . The continuous line in Fig.…”

“…2 is due to an incompleteness of the surveys at z 5 or due to an underestimation of the incompleteness factors. Recent surveys (Schindler et al 2019b;Boutsia et al 2021), indeed, have shown that the first attempts to measure the space densities of bright QSOs at high-z by the SDSS could possibly suffer from incompleteness by a large amount of ∼ 30 − 40% at z ∼ 4, reaching a factor of 3 incompleteness at z ∼ 5 (Wolf et al 2020;Onken et al 2021). If such an incompleteness is affecting also the data points at z > 5, and plausibly the incompleteness is stronger at z ∼ 6 than at z ∼ 5, then the observed drop could only be due to a spurious trend.…”

Identifying galaxies, quasars, and stars with machine learning: A new catalogue of classifications for 111 million SDSS sources without spectra

“…Consequently, photometry data have been demonstrated to be useful as machine learning features in source-type classification by a number of studies (e.g. Carrasco et al 2015;Schindler et al 2019;Kang et al 2019;Nakoneczny et al 2019;Bai et al 2019). Furthermore, testing whether a source is resolved or unresolved can help distinguish the extended profiles of galaxies from stars and quasars (Aguado et al 2019;Baldry et al 2010;Morice-Atkinson et al 2018) and serve as a useful machine learning feature.…”