The GLEAMing of the first supermassive black holes: II. A new sample of high-redshift radio galaxy candidates
Bibliographic record
Abstract
Abstract While unobscured and radio-quiet active galactic nuclei are regularly being found at redshifts $z > 6$ , their obscured and radio-loud counterparts remain elusive. We build upon our successful pilot study, presenting a new sample of low-frequency-selected candidate high-redshift radio galaxies (HzRGs) over a sky area 20 times larger. We have refined our selection technique, in which we select sources with curved radio spectra between 72–231 MHz from the GaLactic and Extragalactic All-sky Murchison Widefield Array (GLEAM) survey. In combination with the requirements that our GLEAM-selected HzRG candidates have compact radio morphologies and be undetected in near-infrared $K_{\rm s}$ -band imaging from the Visible and Infrared Survey Telescope for Astronomy Kilo-degree Infrared Galaxy (VIKING) survey, we find 51 new candidate HzRGs over a sky area of approximately $1200\ \mathrm{deg}^2$ . Our sample also includes two sources from the pilot study: the second-most distant radio galaxy currently known, at $z=5.55$ , with another source potentially at $z \sim 8$ . We present our refined selection technique and analyse the properties of the sample. We model the broadband radio spectra between 74 MHz and 9 GHz by supplementing the GLEAM data with both publicly available data and new observations from the Australia Telescope Compact Array at 5.5 and 9 GHz. In addition, deep $K_{\rm s}$ -band imaging from the High-Acuity Widefield K -band Imager (HAWK-I) on the Very Large Telescope and from the Southern Herschel Astrophysical Terahertz Large Area Survey Regions $K_{\rm s}$ -band Survey (SHARKS) is presented for five sources. We discuss the prospects of finding very distant radio galaxies in our sample, potentially within the epoch of reionisation at $z \gtrsim 6.5$ .
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".