The Spectroscopic Follow-up of the QUBRICS Bright Quasar Survey

Boutsia, K.; Grazian, A.; Calderone, Giorgio; Cristiani, S.; Cupani, G.; Guarneri, Francesco; Fontanot, Fabio; Amorín, R.; D’Odorico, V.; Giallongo, E.; Salvato, M.; Omizzolo, A.; Romano, Michael; Menci, Nicola

doi:10.3847/1538-4365/abafc1

Cited by 28 publications

(37 citation statements)

References 43 publications

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“…The space density shown in Fig. 1 is based on the CCA selection criterion (Calderone et al 2019;Boutsia et al 2020), which has not been tuned to be particular complete at z > 4.5, but has been design to have high efficiency in a broader redshift range, i.e. at z > 2.5.…”

Section: Discussionmentioning

confidence: 99%

“…The QUBRICS survey (Calderone et al 2019;Boutsia et al 2020Boutsia et al , 2021Guarneri et al 2021) turns out to be particularly efficient and complete in the selection of ultra-bright QSOs at high redshift (z > 2.5). Thanks to the extensive spectroscopic confirmations carried out progressing with this survey, and complementing our database with the results of other groups (Wolf et al 2020;Onken et al 2021), a sample of 14 ultra-bright QSOs with M 1450 ≤ −28.3 at 4.5 < z spec < 5.0 has been assembled in Table 1.…”

Section: Discussionmentioning

confidence: 99%

“…While at redshift z 6 we have only scanty data mainly confined to bright QSOs, an increasing dataset coming from spectroscopic and photometric surveys is progressively available at z ∼ 5, based on more sophisticated selection criteria. In this context, we present here an estimate of the space density of luminous QSOs selected in a homogeneous way within the QUBRICS (QUasars as BRIght beacons for Cosmology in the Southern hemisphere) survey (Calderone et al 2019;Boutsia et al 2020). This survey turned out to be particularly efficient (∼ 70%) and complete (∼ 80%) in finding very bright QSOs at high-z (Boutsia et al 2021).…”

Section: Introductionmentioning

confidence: 99%

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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

Grazian,

Giallongo,

Boutsia

et al. 2021

Preprint

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Motivated by evidences favoring a rapid and late hydrogen reionization process completing at z ∼ 5.2 − 5.5 and mainly driven by rare and luminous sources, we have reassessed the estimate of the space density of ultra-luminous QSOs at z ∼ 5 in the framework of the QUBRICS survey. A ∼ 90% complete sample of 14 spectroscopically confirmed QSOs at M 1450 ≤ −28.3 and 4.5 ≤ z ≤ 5.0 has been derived in an area of 12, 400 sq. deg., thanks to multi-wavelength selection and GAIA astrometry. The space density of z ∼ 5 QSOs within −29.3 ≤ M 1450 ≤ −28.3 is three times higher than previous determinations. Our results suggest a steep bright-end slope for the QSO luminosity function at z ∼ 5 and a mild redshift evolution of the space density of ultra-bright QSOs (M 1450 ∼ −28.5) at 3 < z < 5.5, in agreement with the redshift evolution of the much fainter AGN population at M 1450 ∼ −23. These findings are consistent with a pure density evolution for the AGN population at z > 3. Adopting our z ∼ 4 QSO luminosity function and applying a mild density evolution in redshift, a photo-ionization rate of Γ HI = 0.46 +0.17 −0.09 × 10 −12 s −1 has been obtained at z = 4.75, assuming an escape fraction of ∼ 70% and a steep faint-end slope of the AGN luminosity function. The derived photo-ionization rate is ∼ 50 − 100% of the ionizing background measured at the end of the reionization epoch, suggesting that AGNs could play an important role in the cosmological reionization process.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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

Grazian,

Giallongo,

Boutsia

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…The main bottleneck, in addition to the spectrograph stability which is thought not to be a limiting factor (Milaković et al 2020), is the availability of bright quasars providing the required signal to noise in reasonable amounts of telescope time. The discovery of additional bright quasars will improve the experiment feasibility (Boutsia et al 2020), so our ELT analysis is likely to be conservative. The Cosmic Accelerometer of Eikenberry et al (2019) (henceforth CAC) is a low-cost version of the experiment, relying on commercial off the shelf equipment.…”

Section: Measuring the Redshift Driftmentioning

confidence: 99%

Cosmological impact of redshift drift measurements

Esteves,

Martins,

Pereira

et al. 2021

Preprint

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The redshift drift is a model-independent probe of fundamental cosmology, but choosing a fiducial model one can also use it to constrain the model parameters. We compare the constraining power of redshift drift measurements by the Extremely Large Telescope (ELT), as studied by Liske et al. (2008), with that of two recently proposed alternatives: the cosmic accelerometer of Eikenberry et al. (2020), and the differential redshift drift of Cooke (2020). We find that the cosmic accelerometer with a 6-year baseline leads to weaker constraints than those of the ELT (by 60%), but with identical time baselines it outperforms the ELT by up to a factor of 6. The differential redshift drift always performs worse that the standard approach if the goal is to constrain the matter density, but it can perform significantly better than it if the goal is to constrain the dark energy equation of state. Our results show that accurately measuring the redshift drift and using these measurements to constrain cosmological parameters are different merit functions: an experiment optimized for one of them will not be optimal for the other. These non-trivial trade-offs must be kept in mind as next generation instruments enter their final design and construction phases.

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“…Another, more computationally efficient option is the probabilistic random forest (PRF) [ 30 ], which is a modification of the long-established Random Forest (RF) algorithm, which can take into account uncertainties in the measurements (i.e., features) as well as in the assigned classes (i.e., activity labels). It is an algorithm recently released for dealing with noisy astronomical data and the scope of this paper is to use this novel methodology for target prediction [ 30 , 31 ].…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Random Forest improves bioactivity predictions close to the classification threshold by taking into account experimental uncertainty

et al. 2021

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Measurements of protein–ligand interactions have reproducibility limits due to experimental errors. Any model based on such assays will consequentially have such unavoidable errors influencing their performance which should ideally be factored into modelling and output predictions, such as the actual standard deviation of experimental measurements (σ) or the associated comparability of activity values between the aggregated heterogenous activity units (i.e., Ki versus IC50 values) during dataset assimilation. However, experimental errors are usually a neglected aspect of model generation. In order to improve upon the current state-of-the-art, we herein present a novel approach toward predicting protein–ligand interactions using a Probabilistic Random Forest (PRF) classifier. The PRF algorithm was applied toward in silico protein target prediction across ~ 550 tasks from ChEMBL and PubChem. Predictions were evaluated by taking into account various scenarios of experimental standard deviations in both training and test sets and performance was assessed using fivefold stratified shuffled splits for validation. The largest benefit in incorporating the experimental deviation in PRF was observed for data points close to the binary threshold boundary, when such information was not considered in any way in the original RF algorithm. For example, in cases when σ ranged between 0.4–0.6 log units and when ideal probability estimates between 0.4–0.6, the PRF outperformed RF with a median absolute error margin of ~ 17%. In comparison, the baseline RF outperformed PRF for cases with high confidence to belong to the active class (far from the binary decision threshold), although the RF models gave errors smaller than the experimental uncertainty, which could indicate that they were overtrained and/or over-confident. Finally, the PRF models trained with putative inactives decreased the performance compared to PRF models without putative inactives and this could be because putative inactives were not assigned an experimental pXC50 value, and therefore they were considered inactives with a low uncertainty (which in practice might not be true). In conclusion, PRF can be useful for target prediction models in particular for data where class boundaries overlap with the measurement uncertainty, and where a substantial part of the training data is located close to the classification threshold.

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The Spectroscopic Follow-up of the QUBRICS Bright Quasar Survey

Cited by 28 publications

References 43 publications

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

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

Cosmological impact of redshift drift measurements

Probabilistic Random Forest improves bioactivity predictions close to the classification threshold by taking into account experimental uncertainty

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