The <i>Herschel</i>-SPIRE Dark Field – II. A <i>P(D)</i> fluctuation analysis of the deepest <i>Herschel</i> image of the submillimetre universe
Bibliographic record
Abstract
ABSTRACT The Herschel-SPIRE Dark Field is the deepest field produced by the SPIRE instrument pushing down below the galaxy confusion limit in each of the 250, 350, 500 $\mu$m bands. Standard source extraction techniques inevitably fail because of this, and we must turn to statistical methods. Here, we present a P(D) – probability of deflection – analysis of a 12$^{\prime }$ diameter region of uniform coverage at the centre of the Herschel-SPIRE Dark Field. Comparing the distribution of pixel fluxes from our observations to the distributions predicted by current literature models, we find that none of the most recent models can accurately recreate our observations. Using a P(D) analysis, we produce a fitted differential source count spline with a bump in the source counts at faint flux densities, followed by a turnover at fainter fluxes, required to fit the observations. This indicates a possible missing component from the current literature models that could be interpreted perhaps as a new population of galaxies, or a missing aspect of galaxy evolution. Taking our best-fitting results, we also calculate the contribution to the cosmic infrared background (CIB) in each of the bands, which all agree with the Planck CIB measurements in this field.
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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 | 0.001 | 0.001 |
| 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) | 0.003 | 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".