An ALMA survey of submillimetre galaxies in the Extended Chandra Deep Field South: high-resolution 870 μm source counts
Bibliographic record
Abstract
We report the first counts of faint submillimetre galaxies (SMGs) in the 870-μm band derived from arcsecond-resolution observations with the Atacama Large Millimeter Array (ALMA). We have used ALMA to map a sample of 122 870-μm-selected submillimetre sources drawn from the 0.5°×0.5° the Large Apex BOlometer CAmera (LABOCA) Extended Chandra Deep Field South submillimetre survey (LESS). These ALMA maps have an average depth of σ870 μm ˜ 0.4 mJy, some approximately three times deeper than the original LABOCA survey and critically the angular resolution is more than an order of magnitude higher, FWHM of ˜1.5 arcsec compared to ˜19 arcsec for the LABOCA discovery map. This combination of sensitivity and resolution allows us to precisely pinpoint the SMGs contributing to the submillimetre sources from the LABOCA map, free from the effects of confusion. We show that our ALMA-derived SMG counts broadly agree with the submillimetre source counts from previous, lower resolution single-dish surveys, demonstrating that the bulk of the submillimetre sources are not caused by blending of unresolved SMGs. The difficulty which well-constrained theoretical models have in reproducing the high surface densities of SMGs, thus remains. However, our observations do show that all of the very brightest sources in the LESS sample, S870 μm ≳ 12 mJy, comprise emission from multiple, fainter SMGs, each with 870-μm fluxes of ≲9 mJy. This implies a natural limit to the star formation rate in SMGs of ≲103 M⊙ yr-1, which in turn suggests that the space densities of z > 1 galaxies with gas masses in excess of ˜5 × 1010 M⊙ is <10-5 Mpc-3. We also discuss the influence of this blending on the identification and characterization of the SMG counterparts to these bright submillimetre sources and suggest that it may be responsible for previous claims that they lie at higher redshifts than fainter SMGs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".