Detailed modelling of a large sample of Herschel sources in the Lockman Hole: identification of cold dust and of lensing candidates through their anomalous SEDs★
Bibliographic record
Abstract
We have studied in detail a sample of 967 SPIRE sources with 5σ detections at 350 and 500 μm and associations with Spitzer-SWIRE 24 μm galaxies in the HerMES-Lockman survey area, fitting their mid- and far-infrared, and submillimetre, spectral energy distributions (SEDs) in an automatic search with a set of six infrared templates. For almost 300 galaxies, we have modelled their SEDs individually to ensure the physicality of the fits. We confirm the need for the new cool and cold cirrus templates, and also of the young starburst template, introduced in earlier work. We also identify 109 lensing candidates via their anomalous SEDs and provide a set of colour–redshift constraints which allow lensing candidates to be identified from combined Herschel and Spitzer data. The picture that emerges of the submillimetre galaxy population is complex, comprising ultraluminous and hyperluminous starbursts, lower luminosity galaxies dominated by interstellar dust emission, lensed galaxies and galaxies with surprisingly cold (10–13 K) dust. 11 per cent of 500 μm selected sources are lensing candidates. 70 per cent of the unlensed sources are ultraluminous infrared galaxies and 26 per cent are hyperluminous. 34 per cent are dominated by optically thin interstellar dust (‘cirrus’) emission, but most of these are due to cooler dust than is characteristic of our Galaxy. At the highest infrared luminosities we see SEDs dominated by M82, Arp 220 and young starburst types, in roughly equal proportions.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".