A 1200-micron MAMBO survey of the GOODS-N field: a significant population of submillimetre drop-out galaxies
Bibliographic record
Abstract
We present a 1200-micron image of the Great Observatories Origin Deep Survey North (GOODS-N) field, obtained with the Max Planck Millimeter Bolometer array (MAMBO) on the IRAM 30-m telescope. The survey covers a contiguous area of 287 square arcmin to a near-uniform noise level of ~0.7mJy/beam. After Bayesian flux deboosting, a total of 30 sources are recovered (>=3.5sigma). An optimal combination of our 1200-micron data and an existing 850-micron image from the Submillimetre Common-User Bolometer Array (SCUBA) yielded 33 sources (>=4sigma). We combine our GOODS-N sample with those obtained in the Lockman Hole and ELAIS-N2 fields (Scott et al. 2002; Greve et al. 2004) in order to explore the degree of overlap between 1200-micron- and 850-micron-selected galaxies (hereafter SMGs), finding no significant difference between their 850-micron to 1200-micron flux density distributions. However, a noise-weighted stacking analysis yields a significant detection of the 1200-micron-blank SCUBA sources, whereas no significant 850-micron signal is found for the 850-micron-blank MAMBO sources. The hypothesis that the 850/1200-micron flux density distribution of SCUBA sources is also representative of the MAMBO population is rejected at the ~4sigma level, via Monte Carlo simulations. Therefore, although the populations overlap, galaxies selected at 850 and 1200micron are different, and there is compelling evidence for a significant 1200-micron-detected population which is not recovered at 850micron. These are submm drop-outs (SDOs), with S_850/S_1200 = 0.7-1.7, requiring very cold dust or unusual spectral energy distributions (T_d ~ 10K; beta ~ 1), unless SDOs reside beyond the redshift range observed for radio-identified SMGs, i.e. at z > 4.
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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 | 0.001 | 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 | 0.000 |
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".