The informativeness of [C II] line-intensity mapping as a probe of the H I content and metallicity of galaxies at the end of reionization
Bibliographic record
Abstract
Line-intensity mapping (LIM) experiments coming online now will survey fluctuations in aggregate emission in the [C II] ionized carbon line from galaxies at the end of reionization. Experimental progress must be matched by theoretical reassessments of approaches to modelling and the information content of the signal. We present a new model for the halo-[C II] connection, building upon results from the FIRE simulations suggesting that gas mass and metallicity most directly determine [C II] luminosity. Applying our new model to an ensemble of peak-patch halo lightcones, we generate new predictions for the [C II] LIM signal at $z\gtrsim6$. We expect a baseline 4000-hour LIM survey from the CCAT facility to have the fundamental sensitivity to detect the [C II] power spectrum at a significance of $5σ$ at $z\sim6$, with an extended or successor Stage 2 experiment improving significance to $48σ$ at $z\sim6$ and achieving $11σ$ at $z\sim7.5$. Cross-correlation through stacking, simulated against a mock narrow-band Lyman-break galaxy survey, would yield a strong detection of the radial profile of cosmological [C II] emission surrounding star-forming galaxies. We also analyse the role of a few of our model's parameters through the pointwise relative entropy (PRE) of the distribution of [C II] intensities. While the PRE signature of different model parameters can become degenerate or diminished after factoring in observational distortions, various parameters do imprint themselves differently on the one-point statistics of the intrinsic signal. Further work can pave the way to access this information and distinguish different sources of non-Gaussianity in the [C II] LIM observation.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".