HerMES: point source catalogues from Herschel-SPIRE observations II★
Bibliographic record
Abstract
The Herschel Multi-tiered Extragalactic Survey (HerMES) is the largest Guaranteed Time Key Programme on the Herschel Space Observatory. With a wedding cake survey strategy, it consists of nested fields with varying depth and area totalling ∼380 deg2. In this paper, we present deep point source catalogues extracted from Herschel-Spectral and Photometric Imaging Receiver (SPIRE) observations of all HerMES fields, except for the later addition of the 270 deg2 HerMES Large-Mode Survey (HeLMS) field. These catalogues constitute the second Data Release (DR2) made in 2013 October. A sub-set of these catalogues, which consists of bright sources extracted from Herschel-SPIRE observations completed by 2010 May 1 (covering ∼74 deg2) were released earlier in the first extensive data release in 2012 March. Two different methods are used to generate the point source catalogues, the sussextractor point source extractor used in two earlier data releases (EDR and EDR2) and a new source detection and photometry method. The latter combines an iterative source detection algorithm, starfinder, and a De-blended SPIRE Photometry algorithm. We use end-to-end Herschel-SPIRE simulations with realistic number counts and clustering properties to characterize basic properties of the point source catalogues, such as the completeness, reliability, photometric and positional accuracy. Over 500 000 catalogue entries in HerMES fields (except HeLMS) are released to the public through the HeDAM (Herschel Database in Marseille) website (http://hedam.lam.fr/HerMES).
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.054 |
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".