An ALMA Survey of Submillimeter Galaxies in the Extended Chandra Deep Field South: Spectroscopic Redshifts
Bibliographic record
Abstract
Abstract We present spectroscopic redshifts of mJy submillimeter galaxies (SMGs), which have been identified from the ALMA follow-up observations of 870 μ m detected sources in the Extended Chandra Deep Field South (the ALMA-LESS survey). We derive spectroscopic redshifts for 52 SMGs, with a median of z = 2.4 ± 0.1. However, the distribution features a high-redshift tail, with ∼23% of the SMGs at . Spectral diagnostics suggest that the SMGs are young starbursts, and the velocity offsets between the nebular emission and UV ISM absorption lines suggest that many are driving winds, with velocity offsets of up to 2000 km s −1 . Using the spectroscopic redshifts and the extensive UV-to-radio photometry in this field, we produce optimized spectral energy distributions (SEDs) using Magphys , and use the SEDs to infer a median stellar mass of = (6 ± 1)× 10 10 M for our SMGs with spectroscopic redshift. By combining these stellar masses with the star formation rates (measured from the far-infrared SEDs), we show that SMGs (on average) lie a factor of ∼5 above the so-called “main sequence” at . We provide this library of 52 template fits with robust and uniquely well-sampled SEDs as a resource for future studies of SMGs, and also release the spectroscopic catalog of ∼2000 (mostly infrared-selected) galaxies targeted as part of the spectroscopic campaign.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".