Searching for High-z Radio Galaxies with the MGCLS
Bibliographic record
Abstract
We present the results from a search for high-redshift radio galaxy (HzRG) candidates using 1.28 GHz data in the Abell 2751 field drawn from the MeerKAT Galaxy Cluster Legacy Survey (MGCLS). We used the HzRG criteria that a radio source is undetected in all-sky optical and infrared catalogues and that it has a very steep radio spectrum. We used the likelihood ratio method for cross-matching the radio catalogue against multi-wavelength galaxy catalogues from the Dark Energy Camera Legacy Survey (DECaLS) and the All-sky Wide Infrared Survey Explorer (AllWISE). For those radio sources with no multi-wavelength counterpart, we further implemented a radio spectral index criterium of α<−1, using in-band spectral index measurements from the wide-band MeerKAT data. Using a 5σ signal-to-noise cut on the radio flux densities, we found a total of 274 HzRG candidates: 179 ultra-steep spectrum sources and 95 potential candidates, which could not be ruled out as they had no spectral information available. The spectral index assignments in this work were complete above a flux density of 0.3 mJy, which is at least an order of magnitude lower than existing studies in this frequency range or when extrapolating from lower frequency limits. Our faintest HzRG candidates with and without an in-band spectral index measurement had a 1.28 GHz flux density of 57 ± 8 μJy and 68 ± 13 μJy, respectively. Although our study is not complete down to these flux densities, our results indicate that the sensitivity and bandwidth of the MGCLS data make them a powerful radio resource to search for HzRG candidates in the Southern sky, with 20 of the MGCLS pointings having similar image quality as the Abell 2751 field and full coverage in both DECaLS and AllWISE. Data at additional radio frequencies will be needed for the faintest source populations, which could be provided in the near future by the MeerKAT UHF band (580–1015 MHz) at a similar resolution (∼8–10″).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".