Cosmic Expansion History from Line-Intensity Mapping
Bibliographic record
Abstract
Line-intensity mapping (LIM) of emission from star-forming galaxies can be used to measure the baryon acoustic oscillation (BAO) scale as far back as the epoch of reionization. This provides a standard cosmic ruler to constrain the expansion rate of the Universe at redshifts which cannot be directly probed otherwise. In light of growing tension between measurements of the current expansion rate using the local distance ladder and those inferred from the cosmic microwave background, extending the constraints on the expansion history to bridge between the late and early Universe is of paramount importance. Using a newly derived methodology to robustly extract cosmological information from LIM, which minimizes the inherent degeneracy with unknown astrophysics, we show that present and future experiments can gradually improve the measurement precision of the expansion rate history, ultimately reaching percent-level constraints on the BAO scale. Specifically, we provide detailed forecasts for the SPHEREx satellite, which will target the Hα and Lyman-α lines, for a near-future stage-2 experiment targeting CII, and for the ground-based COMAP instrument-as well as a future stage-3 experiment-that will target the CO rotational lines. Besides weighing in on the so-called Hubble tension, reliable LIM cosmic rulers can enable wide-ranging tests of dark matter, dark energy, and modified gravity.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.003 | 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".