ALMA REDSHIFTS OF MILLIMETER-SELECTED GALAXIES FROM THE SPT SURVEY: THE REDSHIFT DISTRIBUTION OF DUSTY STAR-FORMING GALAXIES
Bibliographic record
Abstract
Using the Atacama Large Millimeter/submillimeter Array, we have conducted a blind redshift survey in the 3 mm atmospheric transmission window for 26 strongly lensed dusty star-forming galaxies (DSFGs) selected with the South Pole Telescope. The sources were selected to have S 1.4 mm > 20 mJy and a dust-like spectrum and, to remove low- z sources, not have bright radio ( S 843 MHz < 6 mJy) or far-infrared counterparts ( S 100 μm < 1 Jy, S 60 μm < 200 mJy). We robustly detect 44 line features in our survey, which we identify as redshifted emission lines of 12 CO, 13 CO, C i , H 2 O, and H 2 O + . We find one or more spectral features in 23 sources yielding a ∼90% detection rate for this survey; in 12 of these sources we detect multiple lines, while in 11 sources we detect only a single line. For the sources with only one detected line, we break the redshift degeneracy with additional spectroscopic observations if available, or infer the most likely line identification based on photometric data. This yields secure redshifts for ∼70% of the sample. The three sources with no lines detected are tentatively placed in the redshift desert between 1.7 < z < 2.0. The resulting mean redshift of our sample is = 3.5. This finding is in contrast to the redshift distribution of radio-identified DSFGs, which have a significantly lower mean redshift of = 2.3 and for which only 10%–15% of the population is expected to be at z > 3. We discuss the effect of gravitational lensing on the redshift distribution and compare our measured redshift distribution to that of models in the literature.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".