Joint Power Spectrum and Voxel Intensity Distribution Forecast on the CO Luminosity Function with COMAP
Bibliographic record
Abstract
Abstract We develop a framework for joint constraints on the CO luminosity function based on power spectra (PS) and voxel intensity distributions (VID) and apply this to simulations of CO Mapping Array Pathfinder (COMAP), a CO intensity mapping experiment. This Bayesian framework is based on a Markov chain Monte Carlo (MCMC) sampler coupled to a Gaussian likelihood with a joint PS + VID covariance matrix computed from a large number of fiducial simulations and re-calibrated with a small number of simulations per MCMC step. The simulations are based on dark matter halos from fast peak patch simulations combined with the L CO(M halo) model of Li et al. We find that the relative power to constrain the CO luminosity function depends on the luminosity range of interest. In particular, the VID is more sensitive at large luminosities, while the PS and the VID are both competitive at small and intermediate luminosities. The joint analysis is superior to using either observable separately. When averaging over CO luminosities ranging between , and over 10 cosmological realizations of COMAP Phase 2, the uncertainties (in dex) are larger by 58% and 30% for the PS and VID, respectively, when compared to the joint analysis (PS + VID). This method is generally applicable to any other random field, with a complicated likelihood, as long a fast simulation procedure is available.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".