Ultraluminous high-redshift quasars from SkyMapper – II. New quasars and the bright end of the luminosity function

Onken, Christopher A.; Wolf, Christian; Bian, Fuyan; Fan, Xiaohui; Hon, Wei Jeat; Raithel, David; Tisserand, Patrick; Lai, Samuel

doi:10.1093/mnras/stac051

Cited by 16 publications

(24 citation statements)

References 68 publications

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“…Based on our results, we argue that this evolution continues to z ≈ 7, when excluding the discrepant data from Boutsia et al (2021) and Giallongo et al (2019). There is evidence that at z < 4 the density evolution flattens (k > −0.7), before the turnover point at z ≈ 2-2.5 (Richards et al 2006;Kulkarni et al 2019), as also discussed in Onken et al (2022). In light of the recent literature, and given the systematic uncertainties inherent in QLF estimates, due to differing models for completeness correction, we conclude that the bright-end density evolution at z > 4 can be well described by an exponential decline with k = −0.7.…”

Section: Evolution Of the M 1450 < −26 Quasar Densitysupporting

confidence: 78%

“…We show our result in comparison to values from the literature in Figure 14. We include a range of studies that provide a significant reevaluation of the QLF at z = 3-5 (Akiyama et al 2018;Schindler et al 2018Schindler et al , 2019Giallongo et al 2019;Boutsia et al 2021;Onken et al 2022), compared to the first results from SDSS (Richards et al 2006;Shen & Kelly 2012;Ross et al 2013). We also show the redshift evolution from integrating the quasar density from the QLFs of Richards et al (2006;z = 0.3-5) and Kulkarni et al (2019;z = 0.3-6), as the gray dotted and dotted-dashed lines.…”

Section: Evolution Of the M 1450 < −26 Quasar Densitymentioning

confidence: 99%

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The Pan-STARRS1 z > 5.6 Quasar Survey. III. The z ≈ 6 Quasar Luminosity Function

Schindler

Bañados

Connor

et al. 2023

ApJ

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We present the z ≈ 6 type-1 quasar luminosity function (QLF), based on the Pan-STARRS1 (PS1) quasar survey. The PS1 sample includes 125 quasars at z ≈ 5.7–6.2, with −28 ≲ M 1450 ≲ −25. With the addition of 48 fainter quasars from the SHELLQs survey, we evaluate the z ≈ 6 QLF over −28 ≲ M 1450 ≲ −22. Adopting a double power law with an exponential evolution of the quasar density (Φ(z) ∝ 10 k(z−6); k = −0.7), we use a maximum likelihood method to model our data. We find a break magnitude of M * = − 26.38 − 0.60 + 0.79 mag , a faint-end slope of α = − 1.70 − 0.19 + 0.29 , and a steep bright-end slope of β = − 3.84 − 1.21 + 0.63 . Based on our new QLF model, we determine the quasar comoving spatial density at z ≈ 6 to be n ( M 1450 < − 26 ) = 1.16 − 0.12 + 0.13 cGpc − 3 . In comparison with the literature, we find the quasar density to evolve with a constant value of k ≈ −0.7, from z ≈ 7 to z ≈ 4. Additionally, we derive an ionizing emissivity of ϵ 912 ( z = 6 ) = 7.23 − 1.02 + 1.65 × 10 22 erg s − 1 Hz − 1 cMpc − 3 , based on the QLF measurement. Given standard assumptions, and the recent measurement of the mean free path by Becker et al. at z ≈ 6, we calculate an H i photoionizing rate of ΓH I(z = 6) ≈ 6 × 10−16 s−1, strongly disfavoring a dominant role of quasars in hydrogen reionization.

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Section: Evolution Of the M 1450 < −26 Quasar Densitysupporting

confidence: 78%

Section: Evolution Of the M 1450 < −26 Quasar Densitymentioning

confidence: 99%

The Pan-STARRS1 z > 5.6 Quasar Survey. III. The z ≈ 6 Quasar Luminosity Function

Schindler

Bañados

Connor

et al. 2023

ApJ

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“…The remaining 4% of sources were cross-matched against recent spectroscopic catalogues, e.g., the SDSSDR16Q (Lyke et al 2020), the Veron-Cétty catalogue (Véron-Cetty & Véron 2010), the 2dF (Colless et al 2001), the 6dF (Jones et al 2009), in order to identify previously known QSOs and nonactive galaxies. Additional spectroscopic identifications, when avail-able, were drawn from the literature (e.g., Schindler et al 2019a,b;Wolf et al 2020;Onken et al 2021) leaving 31,550 unclassified sources: these will automatically be classified by the PRF trained on the spectroscopically confirmed QSOs and galaxies, together with 𝑏𝑜𝑛𝑎 𝑓 𝑖𝑑𝑒 stars.…”

Section: The New Main Samplementioning

confidence: 99%

“…The selection of high-redshift quasar is often performed by employing colour cuts: optical and infrared colour criteria have been successfully employed in the past for the selection of high-redshfit quasars (e.g., Onken et al 2021;Wolf et al 2020), and machine learning methods have been successfully employing colours as features (e.g., Nakoneczny et al 2021;Wenzl et al 2021). Low-and highredshift QSOs, non active galaxies and stars occupy (mostly) distinct regions in a multidimensional colour space 2 , allowing an algorithm which employs colours to successfully differentiate between classes.…”

Section: Colour Selectionmentioning

confidence: 99%

“…Luminous, high-redshift quasars are of paramount importance for a wide range of extragalactic studies. These include, e.g., the number density of bright quasars at high-𝑧 (Schindler et al 2019a;Boutsia et al 2021;Onken et al 2021;Grazian et al 2022), their role in the re-ionisation process (e.g., Fontanot et al 2012, Fontanot et al, in preparation), the early phases of galaxy formation and co-evolution with their central SMBHs (e.g., Fontanot et al 2020), the inference of cosmological parameters from lensed QSOs or the characterisation of the accretion properties for SMBHs (e.g., Wu et al 2015).…”

Section: Introductionmentioning

confidence: 99%

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The probabilistic random forest applied to the QUBRICS survey: improving the selection of high-redshift quasars with synthetic data

Guarneri

Calderone

Cristiani

et al. 2022

Monthly Notices of the Royal Astronomical Society

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Several recent works have focused on the search for bright, high-z quasars (QSOs) in the South. Among them, the QUasars as BRIght beacons for Cosmology in the Southern hemisphere (QUBRICS) survey has now delivered hundreds of new spectroscopically confirmed QSOs selected by means of machine learning algorithms. Building upon the results obtained by introducing the probabilistic random forest (PRF) for the QUBRICS selection, we explore in this work the feasibility of training the algorithm on synthetic data to improve the completeness in the higher redshift bins. We also compare the performances of the algorithm if colours are used as primary features instead of magnitudes. We generate synthetic data based on a composite QSO spectral energy distribution. We first train the PRF to identify QSOs among stars and galaxies, then separate high-z quasar from low-z contaminants. We apply the algorithm on an updated dataset, based on SkyMapper DR3, combined with Gaia eDR3, 2MASS and WISE magnitudes. We find that employing colours as features slightly improves the results with respect to the algorithm trained on magnitude data. Adding synthetic data to the training set provides significantly better results with respect to the PRF trained only on spectroscopically confirmed QSOs. We estimate, on a testing dataset, a completeness of $\sim 86{{\ \rm per\ cent}}$ and a contamination of $\sim 36{{\ \rm per\ cent}}$. Finally, 207 PRF-selected candidates were observed: 149/207 turned out to be genuine QSOs with z>2.5, 41 with z<2.5, 3 galaxies and 14 stars. The result confirms the ability of the PRF to select high-z quasars in large datasets.

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AllBRICQS: The All-sky BRIght, Complete Quasar Survey

Onken

Wolf

Hon

et al. 2023

Publ. Astron. Soc. Aust.

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We describe the first results from the All-sky BRIght, Complete Quasar Survey (AllBRICQS), which aims to discover the last remaining optically bright quasars. We present 156 spectroscopically confirmed quasars (140 newly identified) having $|b|>10^{\circ}$ . 152 of the quasars have Gaia DR3 magnitudes brighter than $B_{P}=16.5$ or $R_{P}=16$ mag, while four are slightly fainter. The quasars span a redshift range of $z=0.07-3.93$ . In particular, we highlight the properties of J0529-4351 at $z=3.93$ , which, if unlensed, is one of the most intrinsically luminous quasars in the Universe. The AllBRICQS sources have been selected by combining data from the Gaia and WISE all-sky satellite missions, and we successfully identify quasars not flagged as candidates by Gaia Data Release 3. We expect the completeness to be $\approx$ 96% within our magnitude and latitude limits, while the preliminary results indicate a selection purity of $\approx$ 96%. The optical spectroscopy used for source classification will also enable detailed quasar characterisation, including black hole mass measurements and identification of foreground absorption systems. The AllBRICQS sources will greatly enhance the number of quasars available for high-signal-to-noise follow-up with present and future facilities.

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Ultraluminous high-redshift quasars from SkyMapper – II. New quasars and the bright end of the luminosity function

Cited by 16 publications

References 68 publications

The Pan-STARRS1 z > 5.6 Quasar Survey. III. The z ≈ 6 Quasar Luminosity Function

The Pan-STARRS1 z > 5.6 Quasar Survey. III. The z ≈ 6 Quasar Luminosity Function

The probabilistic random forest applied to the QUBRICS survey: improving the selection of high-redshift quasars with synthetic data

AllBRICQS: The All-sky BRIght, Complete Quasar Survey

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