Missing [C <scp>ii</scp>] emission from early galaxies
Bibliographic record
Abstract
ABSTRACT ALMA observations have revealed that [C ii] 158 μm line emission in high-z galaxies is ≈2–3 × more extended than the UV continuum emission. Here we explore whether surface brightness dimming (SBD) of the [C ii] line is responsible for the reported [C ii] deficit, and the large $L_{\rm [O\, \small {III}]}/L_{\rm [C\, \small {II}]}$ luminosity ratio measured in early galaxies. We first analyse archival ALMA images of nine z > 6 galaxies observed in both [C ii] and [O iii]. After performing several uv-tapering experiments to optimize the identification of extended line emission, we detect [C ii] emission in the whole sample, with an extent systematically larger than the [O iii] emission. Next, we use interferometric simulations to study the effect of SBD on the line luminosity estimate. About 40 per cent of the extended [C ii] component might be missed at an angular resolution of 0.8 arcsec, implying that $L_{\rm [C\, \small {II}]}$ is underestimated by a factor ≈2 in data at low (<7) signal-to-noise ratio. By combining these results, we conclude that $L_{\rm [C\, \small {II}]}$ of z > 6 galaxies lies, on average, slightly below the local $L_{\rm [C\, \small {II}]}-\mathrm{ SFR}$ relation (Δz = 6–9 = −0.07 ± 0.3), but within the intrinsic dispersion of the relation. SBD correction also yields $L_{\rm [O\, \small {III}]}/L_{\rm [C\, \small {II}]}\lt 10$, i.e. more in line with current hydrodynamical simulations.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".