Investigating the origin of CMB large-scale features using LiteBIRD and CMB-S4
Bibliographic record
Abstract
Abstract Several missions following Planck are currently under development, which will provide high-precision measurements of the Cosmic Microwave Background (CMB) anisotropies. Specifically, measurements of the E modes will become nearly limited by cosmic variance, which, especially when considering the sharpness of the E-mode transfer functions, may allow for the ability to detect deviations from the concordance model in the CMB data. We investigate the capability of upcoming missions to scrutinize models that have been proposed to address large-scale anomalies observed in the temperature spectra from WMAP and Planck. To this purpose, we consider four benchmarks that modify the CMB angular power spectra at large scales: models producing suppression, a dip, and amplification in the primordial scalar power spectrum, as well as a beyond-ΛCDM prescription of dark energy. Our analysis shows that large-scale measurements from LiteBIRD will be able to distinguish between various types of primordial and late-time models that predict modifications to the angular spectra at these scales. Moreover, if these deviations from the standard cosmological model are determined to be systematic and do not reflect the true universe model, future experiments could potentially dismiss these features as statistical fluctuations. We also show that additional measurements from CMB-S4 can impose more stringent constraints by probing correlated signals that these models predict at smaller scales (ℓ≳ 100). A byproduct of our analysis is that a recently proposed “Dark Dimension” scenario, featuring power amplification at large scales, is strongly bound by current data, pushing the deviation from the standard model to unobservable scales. Overall, our results demonstrate that future CMB measurements can provide valuable insights into large-scale anomalies that are present in the current CMB 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".