Probing early structure and model-independent neutrino mass with high-redshift CMB lensing mass maps
Bibliographic record
Abstract
CMB lensing maps probe the mass distribution in projection out to high redshifts, but significant sensitivity to low-redshift structure remains. In this paper we discuss a method to remove the low-redshift contributions from CMB lensing mass maps by subtracting suitably scaled galaxy density maps, nulling the low redshift structure with a model-insensitive procedure that is similar to delensing. This results in a high-$z$-only mass map that can provide a probe of structure growth at uniquely high redshifts: if systematics can be controlled, we forecast that CMB-S4 lensing combined with a Rubin-LSST-like galaxy survey can probe the amplitude of structure at redshifts $z>3.75$ ($z>5$) to within 2.3% (3.3%). We then discuss other example applications of such high-$z$ CMB lensing maps. In standard analyses of CMB lensing, assuming the wrong dark energy model (or wrong model parametrization) can lead to biases in neutrino mass constraints. In contrast, we show with forecasts that a high-$z$ mass map constructed from CMB-S4 lensing and LSST galaxies can provide a nearly model-independent neutrino mass constraint, with only negligible sensitivity to the presence of nonstandard dark energy models, irrespective of their parametrization.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".