The SCUBA Half Degree Extragalactic Survey - IV. Radio-mm-FIR photometric redshifts
Bibliographic record
Abstract
We present the redshift distribution of the Submillimetre Common-User Bolometer Array (SCUBA) Half Degree Survey (SHADES) galaxy population based on the rest-frame radio–mm–far-infrared (FIR) colours of 120 robustly detected 850 μm sources in the Lockman Hole East (LH) and Subaru XMM–Newton Deep Field (SXDF). The redshift distribution derived from the full spectral energy distribution (SED) information is shown to be narrower than that determined from the radio–sub-mm spectral index, as more photometric bands contribute to a higher redshift accuracy. The redshift distribution of sources derived from at least two photometric bands peaks at z≈ 2.4 and has a near-Gaussian distribution, with 50 per cent (interquartile range) of sources at z= 1.8–3.1. We find a statistically significant difference between the measured redshift distributions in the two fields; the SXDF peaking at a slightly lower redshift (median z≈ 2.2) than the LH (median z≈ 2.7), which we attribute to the noise properties of the radio observations. We demonstrate, however, that there could also be field-to-field variations that are consistent with the measured differences in the redshift distributions and, hence, that the incomplete area observed by SHADES with SCUBA, despite being the largest sub-mm survey to date, may still be too small to fully characterize the bright sub-mm galaxy population. Finally, we present a brief comparison with the predicted, or assumed, redshift distributions of sub-mm galaxy formation and evolution models, and we derive the contribution of these SHADES sources and the general sub-mm galaxy population to the star formation rate density at different epochs.
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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 | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 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) | 0.004 | 0.002 |
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".