Synergies between the COMAP CO Line Intensity Mapping mission and a Lyα galaxy survey: How to probe the early universe with voxel based analysis of observational data
Bibliographic record
Abstract
Line Intensity Mapping (LIM) offers a novel avenue to observe and characterize our universe. LIM data of CO spectral lines are becoming available, such as those obtained by the CO Mapping Array Project (COMAP). COMAP data can be used to probe the molecular gas content of the universe from the last stages of the Epoch of Reionization (EoR) ($z < 8.0$) to $z \sim 2.5$. In this work, we examine the prospects for deriving voxel-level statistical constraints on high-redshift galaxies from COMAP data by considering the additional information available from observations of LAEs galaxies using the Visible Integral-Field Replicable Unit Spectrograph (VIRUS) on the Hobby-Eberly Telescope (HET). We post-process the IllustrisTNG300 galaxy-formation simulation with a set of prescriptions to consistently determine CO and Ly$α$ line luminosities. The different line prescriptions span the uncertainty in the CO line luminosity according to current observations by the VLA high-z CO surveys and set the Ly$α$ emission to be compatible with observational LAE luminosity functions. We produce mock observations for the two surveys over a $(300\, {\rm Mpc})^3$ volume. These are then used to formulate and test methodologies for data analysis and to predict COMAP constraints on CO emission. We use combinations of masking, stacking, voxel intensity distribution (VID), and other statistics. We find that in combination with VIRUS/HET, a voxel-level analysis of the COMAP Pathfinder survey can detect and characterize the CO signal from $z\sim3$ and improve current constraints on the $z\sim6$ signal, identify individual voxels with bright CO(1-0) emission at $z\sim3$ and probe the redshift evolution of the CO emission. This study illustrates the potential of synergies between LIM and galaxy surveys both to improve the significance of a detection and to aid the interpretation of noisy LIM data.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".