The Infrared Properties of Submillimeter Galaxies: Clues from Ultradeep 70 μm Imaging
Bibliographic record
Abstract
We present 70 μm properties of submillimeter galaxies (SMGs) in the Great Observatories Origins Deep Survey (GOODS) North field. Out of 30 submillimeter galaxies ( S 850 > 2 mJy) in the central GOODS-N region, we find two with secure 70 μm detections. These are the first 70 μm detections of SMGs. One of the matched SMGs is at z ~ 0.5, and has S 70 / S 850 and S 70 / S 24 ratios consistent with a cool galaxy. The second SMG ( z = 1.2) has infrared-submillimeter colors that indicate it is more actively forming stars. We examine the average 70 μm properties of the SMGs by performing a stacking analysis, which also allows us to estimate that S 850 > 2 mJy SMGs contribute 9% ± 3% of the 70 μm background light. The S 850 / S 70 colors of the SMG population as a whole is best fit by cool galaxies, and because of the redshifting effects these constraints are mainly on the lower z subsample. We fit spectral energy distributions (SEDs) to the far-infrared data points of the two detected SMGs and the average low-redshift SMG ( z median = 1.4). We find that the average low- z SMG has a cooler dust temperature than local ultraluminous infrared galaxies (ULIRGs) of similar luminosity and an SED that is best fit by scaled-up versions of normal spiral galaxies. The average low- z SMG is found to have a typical dust temperature T = 21-33 K and infrared luminosity L 8-1000 μm = 8.0 × 10 11 L ☉ . We estimate the AGN contribution to the total infrared luminosity of low- z SMGs is less than 23%.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".