HerMES: Deep galaxy number counts from a P(D) fluctuation analysis of SPIRE Science Demonstration Phase observations
Bibliographic record
Abstract
Dusty, star-forming galaxies contribute to a bright, currently unresolved cosmic far-infrared background. Deep Herschel-Spectral and Photometric Imaging Receiver (SPIRE) images designed to detect and characterize the galaxies that comprise this background are highly confused, such that the bulk lies below the classical confusion limit. We analyse three fields from the Herschel Multi-tiered Extragalactic Survey (HerMES) programme in all three SPIRE bands (250, 350 and 500 μm); parametrized galaxy number count models are derived to a depth of ~2 mJy beam, approximately four times the depth of previous analyses at these wavelengths, using a probability of deflection [P(D)] approach for comparison to theoretical number count models. Our fits account for 64, 60 and 43 per cent of the far-infrared background in the three bands. The number counts are consistent with those based on individually detected SPIRE sources, but generally inconsistent with most galaxy number count models, which generically overpredict the number of bright galaxies and are not as steep as the P(D)-derived number counts. Clear evidence is found for a break in the slope of the differential number counts at low flux densities. Systematic effects in the P(D) analysis are explored. We find that the effects of clustering have a small impact on the data, and the largest identified systematic error arises from uncertainties in the SPIRE beam. © 2010 The Authors. Journal compilation
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.003 | 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".