SuperCLASS – II. Photometric redshifts and characteristics of spatially resolved <i>μ</i>Jy radio sources
Bibliographic record
Abstract
ABSTRACT We present optical and near-infrared imaging covering a ∼1.53 deg2 region in the Super-Cluster Assisted Shear Survey (SuperCLASS) field, which aims to make the first robust weak lensing measurement at radio wavelengths. We derive photometric redshifts for ≈176 000 sources down to $i^\prime _{\rm AB}\sim 24$ and present photometric redshifts for 1.4 GHz expanded Multi-Element Radio Linked Interferometer Network (e-MERLIN) and Karl G. Jansky Very Large Array (VLA) detected radio sources found in the central 0.26 deg2. We compile an initial catalogue of 149 radio sources brighter than S1.4 > 75 μJy and find their photometric redshifts span 0 < zphot < 4 with radio luminosities between 1021 and 1025 W Hz−1, with medians of $\langle z \rangle \, =0.55$ and $\langle L_{1.4}\rangle \, =1.9\times 10^{23}$ W Hz−1, respectively. We find 95 per cent of the μJy radio source sample (141/149) have spectral energy distributions (SEDs) best fit by star-forming templates while 5 per cent (8/149) are better fit by active galactic nuclei (AGN). Spectral indices are calculated for sources with radio observations from the VLA and Giant Metrewave Radio Telescope (GMRT) at 325 MHz, with an average spectral slope of α = 0.59 ± 0.04. Using the full photometric redshift catalogue, we construct a density map at the redshift of the known galaxy clusters, z = 0.20 ± 0.08. Four of the five clusters are prominently detected at $\gt 7\, \sigma$ in the density map and we confirm the photometric redshifts are consistent with previously measured spectra from a few galaxies at the cluster centres.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".