The Hubble Deep Field-North SCUBA Super-map - IV. Characterizing submillimetre galaxies using deep Spitzer imaging
Bibliographic record
Abstract
We present SEDs, Spitzer colours, and IR luminosities for 850 micron selected galaxies in the GOODS-N field. Using the deep Spitzer Legacy images and new data and reductions of the VLA-HDF radio data, we find statistically secure counterparts for 60 per cent (21/35) of our submm sample, and identify tentative counterparts for another 12 objects. This is the largest sample of submm galaxies with statistically secure counterparts detected in the radio and with Spitzer. We find that in most cases the 850 micron emission is dominated by a single 24 micron source. A composite rest-frame SED shows that the submm sources peak at longer wavelengths than those of local ULIRGs of the same luminosity and therefore appear to be cooler. The SEDs of submm galaxies are also different from those of their high redshift neighbours, the near-IR selected BzK galaxies, whose mid-IR to radio SEDs are more like those of local ULIRGs. Using 24 micron. 850 micron and 1.4 GHz observations, we fit templates that span the mid-IR through radio to derived the integrated IR luminosity of the submm galaxies. By themselves, 24 micron and radio fluxes are able to predict LIR reasonably well because they are relatively insensitive to temperature. However, the submm flux by itself consistently overpredicts LIR when using spectral templates which obey the local ULIRG temperature-luminosity relation. The shorter Spitzer wavelengths sample the stellar bump at the redshifts of the submm sources, and we find that the Spitzer photometry alone provides a model independent estimate of the redshift. Using X-ray and mid-IR data, only 5 per cent of our secure counterparts show strong evidence for an active galactic nucleus dominating the IR luminosity.
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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 | 0.003 | 0.003 |
| 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) | 0.006 | 0.002 |
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".