Improved halo model calibrations for mixed dark matter models of ultralight axions
Bibliographic record
Abstract
ABSTRACT We study the implications of relaxing the requirement for ultralight axions to account for all dark matter in the Universe by examining mixed dark matter (MDM) cosmologies with axion fractions $f \le 0.3$ within the fuzzy dark matter window $10^{-25}$ eV $\lesssim m \lesssim 10^{-23}$ eV. Our simulations, using a new MDM gravity solver implemented in AxiREPO, capture wave dynamics across various scales with high accuracy down to redshifts $z\approx 1$. We identify haloes with Rockstar using the cold dark matter component and find good agreement of inferred halo mass functions and concentration–mass relations with theoretical models across redshifts $z=1{\!-\!}10$. This justifies our halo finder approach a posteriori as well as the assumptions underlying the MDM halo model AxionHMcode. Using the inferred axion halo mass–cold halo mass relation $M_{\text{a}}(M_{\text{c}})$ and calibrating a generalized smoothing parameter $\alpha$ to our MDM simulations, we present a new version of AxionHMcode. The code exhibits excellent agreement with simulations on scales $k\lt 20 \, h \, \text{cMpc}^{-1}$ at redshifts $z=1{\!-\!}3.5$ for $f\le 0.1$ around the fiducial axion mass $m = 10^{-24.5}\, \text{eV} = 3.16\times 10^{-25}\, \text{eV}$, with maximum deviations remaining below 10 per cent. For axion fractions $f\le 0.3$, the model maintains accuracy with deviations under 20 per cent at redshifts $z\approx 1$ and scales $k\lt 10 \, h \, \text{cMpc}^{-1}$, though deviations can reach up to 30 per cent for higher redshifts when $f=0.3$. Reducing the run-time for a single evaluation of AxionHMcode to below 1 min, these results highlight the potential of AxionHMcode to provide a robust framework for parameter sampling across MDM cosmologies in Bayesian constraint and forecast analyses.
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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 | 0.000 |
| 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".