High-redshift, small-scale tests of ultralight axion dark matter using Hubble and Webb galaxy UV luminosities
Bibliographic record
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 both Planck cosmic microwave background (CMB) and UV luminosity function (UVLF) data. We exclude a single axion as all the DM for $m_{ax} < 10^{-21.6}$ eV and limit axions with $-26 < \log( m_{ax}/{eV}) < -23$ to be less than $22\%$ of the DM (both limits at $95\%$ credibility). These limits use UVLF measurements from 24,000 sources from the Hubble Space Telescope (HST) that probe small-scale structure at redshifts $4 < z < 10$. We marginalize over a parametric model that connects halo mass and UV luminosity that has been shown to match hydrodynamical simulations. Our results bridge a window in axion mass and DM fraction previously unconstrained by cosmological data, between large-scale CMB and galaxy clustering and the small-scale Lyman-$α$ forest. These high-$z$ measurements provide a powerful consistency check of low-$z$ tests of axion DM, which include the recent hint for a sub-dominant ULA DM fraction in Lyman-$α$ forest data. We also consider a sample of 25 spectroscopically-confirmed high-$z$ galaxies from the James Webb Space Telescope (JWST). We find that these data are consistent with the HST UVLF assuming $Λ$CDM and our flexible parametric model of UV luminosity. 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 significantly. We also find an excess of low-mass halos ($< 10^9 M_\odot$) at $z < 3$, which could be probed by sub-galactic 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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".