Improving models of the cosmic infrared background using CMB lensing mass maps
Bibliographic record
Abstract
The cosmic infrared background (CIB) sourced by infrared emission from dusty star-forming galaxies is a valuable source of information on the star formation history of the Universe. In measurements of the millimeter sky at frequencies higher than $\ensuremath{\sim}300\text{ }\text{ }\mathrm{GHz}$, the CIB and thermal emission from Galactic dust dominate. Insufficient understanding of the CIB contribution at lower frequencies can hinder efforts to measure the kinetic Sunyaev-Zeldovich spectrum on small scales as well as new physics that affects the damping tail of the cosmic microwave background (CMB). The Planck satellite has measured with high fidelity the CIB at 217, 353, 545 and 857 GHz. On very large scales, this measurement is limited by our ability to separate the CIB from Galactic dust, but on intermediate scales, the measurements are limited by sample variance in the underlying matter field traced by the CIB. We show how significant improvements (20--100%) can be obtained on parameters of star formation models by cross-correlating the CIB (as measured from existing Planck maps or upcoming CCAT-prime maps) with upcoming mass maps inferred from gravitational lensing of the CMB. This improvement comes from improved knowledge of the redshift distribution of star-forming galaxies as well as through the use of the unbiased matter density inferred from CMB lensing mass maps to cancel the sample variance in the CIB field. We also find that further improvements can be obtained on CIB model parameters if the cross-correlation of the CIB with CMB lensing is measured over a wider area while restricting the more challenging CIB autospectrum measurement to the cleanest 5% of the sky.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".