Cross-correlations between mm-wave line-intensity mapping and weak-lensing surveys: preliminary consideration of long-term prospects
Bibliographic record
Abstract
ABSTRACT The field of millimetre-wave line-intensity mapping (LIM) is seeing increased experimental activity with pathfinder surveys already deployed or deploying in the next few years, making spectroscopic measurements of unresolved atomic and molecular line emission tracing the large-scale structure of the Universe. The next decade will also see the Rubin Observatory Legacy Survey of Space and Time (LSST) undertake a photometric galaxy survey programme of unprecedented scope, including measurements of cosmic shear exploiting weak gravitational lensing (WL) of background galaxies to map projected large-scale structure. We consider prospects for detecting angular cross-power spectra between non-tomographic cosmic shear and mm-wave LIM surveys that measure emission from CO lines at z = 0.5–1. We forecast that once the LSST Year 10 WL data set is available, a future LIM experiment, conceivably deployed in the next 10–15 yr, would enable such a cross-correlation detection with an overall signal-to-noise ratio of 50, although the current pathfinder generation of CO/[C ii] surveys are more likely to achieve a marginal 2σ detection against an earlier-stage LSST WL data set. The signal has modest astrophysical constraining power yielding competitive constraints on cosmic molecular gas density at z ≲ 1, and degeneracies between astrophysical parameters and the intrinsic alignment amplitude mean that external information on either one could allow the cross-correlation analysis to significantly improve its constraints on the other.
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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 | 0.024 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".