Dark progenitors and massive descendants: A first ALMA perspective of radio-selected near-IR-dark galaxies in the COSMOS field
Bibliographic record
Abstract
We present the first spectroscopic ALMA follow-up for a pilot sample of nine radio-selected near-IR-dark galaxies in the COSMOS field. These sources were initially selected as radio-detected sources (S3 GHz > 12.65 μJy) without an optical or near-IR (NIR) counterpart in the COSMOS2015 catalog (Ks ≳ 24.7 mag), and just three of them were subsequently detected in the deeper COSMOS2020. Several studies highlighted that this selection could provide a population of highly dust-obscured, massive, and star-bursting galaxies. With these new ALMA observations, we assess the spectroscopic redshifts of this pilot sample of sources and improve the quality of the physical properties estimated through SED-fitting. Moreover, we measure the quantity of molecular gas inside these galaxies and forecast their potential evolutionary path, finding that the radio-selected NIR-dark galaxies might likely represent a population of high-z progenitors of the massive and passive galaxies that were discovered at z ∼ 3. Finally, we present some initial constraints on the kinematics of the interstellar medium within the analyzed galaxies, reporting a high fraction (∼55%) of double-peaked lines that can be interpreted as the signature of a rotating structure in our targets or as the presence of major mergers in our sample. The results we present here show the scientific potential of (sub)mm observations for this elusive population of galaxies and highlight the potential contribution of these sources to the evolution of the massive and passive galaxies at high z.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".