Spatially resolved [CII]–gas conversion factor in early galaxies
Bibliographic record
Abstract
Aims. Determining how efficiently gas collapses into stars at high redshifts is key to understanding galaxy evolution in the epoch of reionization (EoR). Globally, this process is quantified by the gas depletion time (tdep); on resolved scales, it is quantified by the slope and normalization of the Kennicutt-Schmidt (KS) relation. This work explores the global (α[CII]) and spatially resolved (W[CII]) [CII]-to-gas conversion factors at high-z and their use when inferring gas masses, surface densities, and tdep in the EoR. Methods. We selected galaxies at 4 < z < 9 from the SERRA cosmological zoom-in simulation, which features on-the-fly radiative transfer and resolves interstellar medium properties down to ≈30 pc. The [CII] emission modeling from photodissociation regions allows us to derive the global α[CII] and maps of W[CII]. We study their dependence on gas metallicity (Z), density (n), Mach number (ℳ), and burstiness parameter (κs), and provide best-fit relations. Results. The α[CII] decreases with increasing Z and galaxy compactness, while the resolved W[CII] shows two regimes: at Z < 0.2 Z⊙, it anticorrelates with n and Z but not with κs; above this threshold, it also depends on κs, with burstier regions having lower conversion factors. This implies W[CII] ∝ Σ[CII]−0.5, as dense, metal-rich, and bursty regions exhibit higher [CII] surface brightnesses. Applying a constant α[CII] leads to an overestimation of Σgas in bright Σ[CII] patches; this in turn flattens the KS slope and leads to overestimations of tdep by up to a factor of 4.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".