High-redshift, Small-scale Tests of Ultralight Axion Dark Matter Using Hubble and Webb Galaxy UV Luminosities
Bibliographic record
Abstract
Abstract We calculate the abundance of UV-bright galaxies in the presence of ultralight axion (ULA) dark matter (DM), finding that axions suppress their formation with a non-trivial dependence on redshift and luminosity. We set limits on axion DM using UV luminosity function (UVLF) data, excluding a single axion as all the DM for m ax < 10−21.6 eV and limiting axions with − 26 ≤ log ( m ax / eV ) ≤ − 23 to be less than 22% of the DM (both at 95% credibility). These limits use UVLF measurements from 24,000 sources from the Hubble Space Telescope (HST) at redshifts 4 ≤ z ≤ 10. We marginalize over a parametric model connecting halo mass and UV luminosity. Our results bridge a window in axion mass and fraction previously unconstrained by cosmological data, between large-scale cosmic microwave background and galaxy clustering and the small-scale Lyα forest. These high-z measurements provide a powerful consistency check of low-z tests of axion DM, including the recent hint for a sub-dominant ULA DM fraction in Lyα forest data. We also consider a sample of 25 spectroscopically confirmed high-z galaxies from the James Webb Space Telescope (JWST), finding these data to be consistent with HST. Combining HST and JWST UVLF data does not improve our constraints beyond HST alone, but future JWST measurements have the potential to improve these results. We also find an excess of low-mass halos (<109 M ⊙) at z < 3, which could be probed by subgalactic structure probes (e.g., stellar streams, satellite galaxies, and strong 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".