Carbon monoxide and ionized carbon line emission global signals: foregrounds and targets for absolute microwave spectrometry
Bibliographic record
Abstract
(abr.) We consider the potential of future microwave spectrometers akin to PIXIE in light of the sky-averaged global signal expected from the total intensity of extragalactic carbon monoxide (CO) and ionized carbon ([CII]) line emission. We start from models originally developed for forecasts of line-intensity mapping (LIM) observations targeting the same line emission at specific redshifts, extrapolating them across all of cosmic time. We then calculate Fisher forecasts for uncertainties on parameters describing relic spectral deviations, the CO/[CII] global signal, and a range of other Galactic and extragalactic foregrounds considered in previous work. We find that the measurement of the CO/[CII] global signal with a future CMB spectrometer presents an exciting opportunity to constrain the evolution of metallicity and molecular gas in galaxies across cosmic time. From PIXIE to its enhanced version, SuperPIXIE, microwave spectrometers would have the fundamental sensitivity to constrain the redshift evolution of average kinetic temperature and cosmic molecular gas density at a level of 10% to 1%, respectively. Taking a spectral distortion-centric perspective, when combined with other foregrounds, sky-averaged CO/[CII] emission can mimic $μ$- and to a lesser extent $y$-type distortions. Under fiducial parameters, marginalising over the CO/[CII] model parameters increases the error on $μ$ by $\simeq86$%, and the error on $y$ by $\simeq10$%. Incorporating information from planned CO LIM surveys can recover some of this loss in precision. Future work should deploy a more general treatment of the microwave sky to quantify in more detail the potential synergies between PIXIE-like and CO LIM experiments, which complement each other strongly in breadth versus depth, and ways to optimise both spectrometer and LIM surveys to improve foreground cleaning and maximise the science return for each.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".