New radio-loud QSOs at the end of the Re-ionization epoch
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".