Baryonic effects on CMB lensing and neutrino mass constraints
Bibliographic record
Abstract
Measurements of gravitational lensing of the cosmic microwave background (CMB) hold the promise of yielding unique insights into cosmology at high redshift. Uncertainties due to baryonic effects associated with galaxy formation and evolution, including gas cooling, star formation, and feedback from active galactic nuclei (AGN) and supernovae, have typically been neglected when forecasting the sensitivity of future CMB surveys. In this paper, we determine the impact of these effects using four suites of hydrodynamical simulations which incorporate various prescriptions for baryonic processes, namely OWLS, BAHAMAS, Horizon, and IllustrisTNG. Our analysis shows characteristic power suppressions of several percent in CMB lensing due to baryonic effects, compared to dark-matter only simulations, at experimentally observable angular scales. We investigate the associated bias in the inferred neutrino mass for experiments like the upcoming Simons Observatory and CMB-S4. Depending on the experimental precision and the strength of the baryonic feedback within the simulations, biases in the neutrino mass sum show significant dispersion, ranging from very small to an overestimation by $1.1\ensuremath{\sigma}$. We conclude that baryonic effects will likely be non-negligible for a detection of neutrino mass using CMB lensing.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".