The Impact of Baryonic Physics on the Kinetic Sunyaev–Zel’dovich Effect
Bibliographic record
Abstract
Abstract Poorly understood “baryonic physics” impacts our ability to predict the power spectrum of the kinetic Sunyaev–Zel’dovich (kSZ) effect. We study this in a sample high-resolution simulation of galaxy formation and feedback, Illustris. The high resolution of Illustris allows us to probe the kSZ power spectrum on multipoles . Strong AGN feedback in Illustris nearly wipes out gas fluctuations at and at late times, likely somewhat underpredicting the kSZ power generated at . The post-reionization kSZ power spectrum for Illustris is well-fit by over 10,000, somewhat lower than most other reported values but consistent with the analysis of Shaw et al. Our analysis of the bias of free electrons reveals subtle effects associated with the multi-phase gas physics and stellar fractions that affect even linear scales. In particular, there are fewer electrons in biased galaxies, due to gas-cooling and star formation, and this leads to an electron bias of less than one, even at low wavenumbers. The combination of bias and electron fraction that determines the overall suppression is relatively constant, , but more simulations are needed to see if this is Illustris-specific. By separating the kSZ power into different terms, we find that at least 6% (10%) of the signal at ℓ = 3000 (10,000) comes from non-Gaussian connected four-point density and velocity correlations, , even without correcting for the Illustris simulation box-size. A challenge going forward will be accurately modeling long-wave velocity modes simultaneously with Illustris-like high resolution to capture the complexities of galaxy formation and its correlations with large-scale flows.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".