HerMES: SPECTRAL ENERGY DISTRIBUTIONS OF SUBMILLIMETER GALAXIES AT<i>z</i>> 4
Bibliographic record
Abstract
We present a study of the infrared properties for a sample of seven spectroscopically confirmed submillimeter galaxies (SMGs) at z > 4.0. By combining ground-based near-infrared, Spitzer IRAC and MIPS, Herschel SPIRE, and ground-based submillimeter/millimeter photometry, we construct their spectral energy distributions (SEDs) and a composite model to fit the SEDs. The model includes a stellar emission component at λ rest < 3.5 μm, a hot dust component peaking at λ rest ∼ 5 μm, and cold dust component which becomes significant for λ rest > 50 μm. Six objects in the sample are detected at 250 and 350 μm. The dust temperatures for the sources in this sample are in the range of 40–80 K, and their L FIR ∼ 10 13 L ☉ qualifies them as hyper-luminous infrared galaxies. The mean FIR-radio index for this sample is around 〈 q 〉 = 2.2 indicating no radio excess in their radio emission. Most sources in the sample have 24 μm detections corresponding to a rest-frame 4.5 μm luminosity of Log 10 ( L 4.5 / L ☉ ) = 11 ∼ 11.5. Their L 4.5 / L FIR ratios are very similar to those of starburst-dominated SMGs at z ∼ 2. The L CO − L FIR relation for this sample is consistent with that determined for local ULIRGs and SMGs at z ∼ 2. We conclude that SMGs at z > 4 are hotter and more luminous in the FIR but otherwise very similar to those at z ∼ 2. None of these sources show any sign of the strong QSO phase being triggered.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".