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Record W4313268914 · doi:10.1093/mnras/stac3668

New radio-loud QSOs at the end of the Re-ionization epoch

2022· article· en· W4313268914 on OpenAlexfundno aff
L. Ighina, A. Caccianiga, A. Moretti, S. Belladitta, J. W. Broderick, G. Drouart, James K. Leung, N. Seymour

Bibliographic record

VenueMonthly Notices of the Royal Astronomical Society · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Cosmic Phenomena
Canadian institutionsnot available
FundersInstitut sur la Nutrition et les Aliments FonctionnelsNational Science Foundation of Sri LankaConsiliul National al Cercetarii StiintificeAgenzia Spaziale Italiana
KeywordsQSOSPhysicsAstrophysicsRedshiftBlazarAstronomyPopulationQuasarYoung stellar objectStarsGalaxyStar formation

Abstract

fetched live from OpenAlex

ABSTRACT We present the selection of high-redshift (z ≳ 5.7) radio-loud (RL) quasi-stellar object (QSO) candidates from the combination of the radio Rapid ASKAP Continuum Survey (RACS; at 888 MHz) and the optical/near-infrared Dark Energy Survey (DES). In particular, we selected six candidates brighter than $S_{\rm 888\, MHz}\gt 1$ mJy beam−1 and mag(zDES) < 21.3 using the dropout technique (in the i-band). From this sample, we were able to confirm the high-z nature (z ∼ 6.1) of two sources, which are now among the highest redshift RL QSOs currently known. Based on our Gemini-South/GMOS observations, neither object shows a prominent Ly α emission line. This suggests that both sources are likely to be weak emission-line QSOs hosting radio jets and would therefore further strengthen the potential increase of the fraction of weak emission-line QSOs recently found in the literature. However, further multiwavelength observations are needed to constrain the properties of these QSOs and of their relativistic jets. From the discovery of these two sources, we estimated the space density of RL QSOs in the redshift range 5.9 < z < 6.4 to be 0.13$^{+0.18}_{-0.09}$ and found it to be consistent with the expectations based on our current knowledge of the blazar population up to z ∼ 5.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.349
Threshold uncertainty score0.964

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.006
GPT teacher head0.186
Teacher spread0.181 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations21
Published2022
Admission routes1
Has abstractyes

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