HerMES: CANDIDATE HIGH-REDSHIFT GALAXIES DISCOVERED WITH<i>HERSCHEL</i>/SPIRE,
Bibliographic record
Abstract
We present a method for selecting z > 4 dusty, star-forming galaxies (DSFGs) using Herschel /Spectral and Photometric Imaging Receiver 250/350/500 μm flux densities to search for red sources. We apply this method to 21 deg 2 of data from the HerMES survey to produce a catalog of 38 high- z candidates. Follow-up of the first five of these sources confirms that this method is efficient at selecting high- z DSFGs, with 4/5 at z = 4.3–6.3 (and the remaining source at z = 3.4), and that they are some of the most luminous dusty sources known. Comparison with previous DSFG samples, mostly selected at longer wavelengths (e.g., 850 μm) and in single-band surveys, shows that our method is much more efficient at selecting high- z DSFGs, in the sense that a much larger fraction are at z > 3. Correcting for the selection completeness and purity, we find that the number of bright ( S 500 μm ⩾ 30 mJy), red Herschel sources is 3.3 ± 0.8 deg −2 . This is much higher than the number predicted by current models, suggesting that the DSFG population extends to higher redshifts than previously believed. If the shape of the luminosity function for high- z DSFGs is similar to that at z ∼ 2, rest-frame UV based studies may be missing a significant component of the star formation density at z = 4–6, even after correction for extinction.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".