Probing Cosmic Reionization and Molecular Gas Growth with TIME
Bibliographic record
Abstract
Abstract Line intensity mapping (LIM) provides a unique and powerful means to probe cosmic structures by measuring the aggregate line emission from all galaxies across redshift. The method is complementary to conventional galaxy redshift surveys that are object based and demand exquisite point-source sensitivity. The Tomographic Ionized-carbon Mapping Experiment (TIME) will measure the star formation rate during cosmic reionization by observing the redshifted [C ii ] 158 μ m line (6 ≲ z ≲ 9) in the LIM regime. TIME will simultaneously study the abundance of molecular gas during the era of peak star formation by observing the rotational CO lines emitted by galaxies at 0.5 ≲ z ≲ 2. We present the modeling framework that predicts the constraining power of TIME on a number of observables, including the line luminosity function and the auto- and cross-correlation power spectra, including synergies with external galaxy tracers. Based on an optimized survey strategy and fiducial model parameters informed by existing observations, we forecast constraints on physical quantities relevant to reionization and galaxy evolution, such as the escape fraction of ionizing photons during reionization, the faint-end slope of the galaxy luminosity function at high redshift, and the cosmic molecular gas density at cosmic noon. We discuss how these constraints can advance our understanding of cosmological galaxy evolution at the two distinct cosmic epochs for TIME, starting in 2021, and how they could be improved in future phases of the experiment.
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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 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".