Sub-mm and near-IR observations of galaxies selected at 170 microns
Bibliographic record
Abstract
We present results from JCMT sub-mm observations of sources selected from the {\sl ISO} FIRBACK (Far-IR BACKground) survey, along with UKIRT near-IR imaging of a sub-sample. This gives valuable insight into the brightest $\sim$10% of galaxies which contribute to the Cosmic Infrared Background (CIB). We estimate the photometric redshifts and luminosities of these sources by fitting their Spectral Energy Distributions (SEDs). The data appear to show a bimodal galaxy distribution, with normal star-forming galaxies at $z\simeq0$, and a much more luminous population at $z\sim0.4$--0.9. These are similar to the ultraluminous infrared galaxies which are found to evolve rapidly with redshift in other surveys. The detectability threshold of FIRBACK biases the sample away from much higher redshift ($z\stackrel{>}{_{\sim}}1.5$) objects. Nevertheless, the handful of $z\sim0.5$ sources which we identify are likely to be the low-$z$ counterparts of the typically higher-$z$ sources found in blank field sub-mm observations. This sub-sample, being much more nearby than the average SCUBA galaxies, has the virtue of being relatively easy to study in the optical. Hence their detailed investigation could help elucidate the nature of the sub-mm bright galaxies.
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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 | 0.000 |
| 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".