Galaxy UV-luminosity function and reionization constraints on axion dark matter
Bibliographic record
Abstract
If the dark matter (DM) were composed of axions, then structure formation in the Universe would be suppressed below the axion Jeans scale. Using an analytic model for the halo mass function of a mixed DM model with axions and cold dark matter, combined with the abundance-matching technique, we construct the UV-luminosity function. Axions suppress high-z galaxy formation and the UV-luminosity function is truncated at a faintest limiting magnitude. From the UV-luminosity function, we predict the reionization history of the universe and find that axion DM causes reionization to occur at lower redshift. We search for evidence of axions using the Hubble Ultra Deep Field UV-luminosity function in the redshift range z = 6–10, and the optical depth to reionization, τ, as measured from cosmic microwave background polarization. All probes we consider consistently exclude ma ≲ 10−23 eV from contributing more than half of the DM, with our strongest constraint ruling this model out at more than 8σ significance. In conservative models of reionization a dominant component of DM with ma = 10−22 eV is in 3σ tension with the measured value of τ, putting pressure on an axion solution to the cusp-core problem. Tension is reduced to 2σ for the axion contributing only half of the DM. A future measurement of the UV-luminosity function in the range z = 10–13 by JWST would provide further evidence for or against ma = 10−22 eV. Probing still higher masses of ma = 10−21 eV will be possible using future measurements of the kinetic Sunyaev–Zel'dovich effect by Advanced ACTPol to constrain the time and duration of reionization.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".