Subtracting the kinetic Sunyaev-Zeldovich effect from the cosmic microwave background with surveys of large-scale structure
Bibliographic record
Abstract
The kinetic Sunyaev-Zeldovich (kSZ) effect will be an important source of cosmological and astrophysical information in upcoming surveys of the cosmic microwave background (CMB). However, the kSZ effect will also act as the dominant source of noise for several other measurements that use small angular scales in CMB temperature maps, since its blackbody nature implies that standard component separation techniques cannot be used to remove it from observed maps. In this paper, we explore the idea of ``de-kSZing'': constructing a template for the late-time kSZ effect using external surveys of large-scale structure, and then subtracting this template from CMB temperature maps in order to remove some portion of the kSZ signal. After building intuition for general aspects of the de-kSZing procedure, we perform forecasts for the de-kSZing efficiency of several large-scale structure surveys, including BOSS, DESI, Roman, MegaMapper, and PUMA. We also highlight potential applications of de-kSZing to cosmological constraints from the CMB temperature power spectrum, CMB lensing reconstruction, and the moving-lens effect. While our forecasts predict achievable de-kSZing efficiencies of 10%--20% at best, these results are specific to the de-kSZing formalism adopted in this work, and we expect that higher efficiencies are possible using improved versions of this formalism.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".