Patchy Kinetic Sunyaev–Zel’dovich Effect with Controlled Reionization History and Morphology
Bibliographic record
Abstract
Abstract Using the novel semi-numerical code for reionization AMBER, we model the patchy kinetic Sunyaev–Zel’dovich (kSZ) effect by directly specifying the reionization history with the redshift midpoint z mid, duration Δz, and asymmetry A z. We further control the ionizing sources and radiation through the minimum halo mass M h and the radiation mean free path λ mfp. AMBER reproduces the free-electron number density and the patchy kSZ power spectrum of radiation–hydrodynamic simulations at the target resolution (1 Mpc h −1) with matched reionization parameters. With a suite of (2 Gpc/h)3 simulations using AMBER, we first constrain the redshift midpoint 6.0 < z mid < 8.9 using the Planck 2018 Thomson optical depth result (95% CL). Then, assuming z mid = 8, we find that the amplitude of D ℓ = 3000 pkSZ scales linearly with the duration of reionization Δ z and is consistent with the 1σ upper limit from South Pole Telescope (SPT) results up to Δ z < 5.1 (Δ z encloses 5%–95% ionization). Moreover, a shorter λ mfp can lead to a ∼10% lower D ℓ = 3000 pkSZ and a flatter slope in the D ℓ = 3000 pkSZ − Δ z scaling relation, thereby affecting the constraints on Δ z at ℓ = 3000. Allowing z mid and λ mfp to vary simultaneously, we get spectra consistent with the SPT result (95% CL) up to Δ z = 12.8 (but A z > 8 is needed to ensure the end of reionization before z = 5.5). We show that constraints on the asymmetry require ∼0.1 μ k 2 measurement accuracy at multipoles other than ℓ = 3000. Finally, we find that the amplitude and shape of the kSZ spectrum are only weakly sensitive to M h under a fixed reionization history and radiation mean free path.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".