HerMES: SPIRE detection of high-redshift massive compact galaxies in GOODS-N field
Bibliographic record
Abstract
Abstract We have analysed the rest-frame far-infrared properties of a sample of massive (M★ > 1011 M⊙) galaxies at 2 ≲z≲ 3 in the Great Observatories Origins Deep Survey-North (GOODS-N) field using the Spectral and Photometric Imaging Receiver (SPIRE) instrument aboard the Herschel Space Observatory. To conduct this analysis we take advantage of the data from the Herschel Multi-tiered Extragalactic Survey (HerMES) key programme. The sample comprises 45 massive galaxies with structural parameters characterized with HST NICMOS-3. We study detections at submm Herschel bands, together with Spitzer 24-μm data, as a function of the morphological type, mass and size. We find that 26/45 sources are detected at MIPS 24 μm and 15/45 (all MIPS 24-μm detections) are detected at SPIRE 250 μm, with disc-like galaxies more easily detected. We derive star formation rates (SFRs) and specific star formation rates (sSFRs) by fitting the spectral energy distribution of our sources, taking into account non-detections for SPIRE and systematic effects for MIPS derived quantities. We find that the mean SFR for the spheroidal galaxies (∼50–100 M⊙ yr−1) is substantially (a factor ∼3) lower than the mean value presented by disc-like galaxies (∼250–300 M⊙ yr−1).
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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) | 0.002 | 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".