Using H <scp>i</scp> observations of low-mass galaxies to test ultralight axion dark matter
Bibliographic record
Abstract
ABSTRACT We evaluate recent and upcoming low-redshift neutral hydrogen (H i) surveys as a cosmological probe of small scale structure with a goal of determining the survey criteria necessary to test ultralight axion (ULA) dark matter models. Standard cold dark matter (CDM) models predict a large population of low-mass galactic haloes, whereas ULA models demonstrate significant suppression in this small-scale regime, with halo mass cutoffs of $10^{12}\, \mathrm{M}_{\odot }$ to $10^{7}\, \mathrm{M}_{\odot }$ corresponding to ULA masses of $10^{-24}\,$ to $10^{-20}\,$ eV, respectively, if ULAs compose all of the dark matter. We generate random, homogeneously populated mock universes with cosmological parameters adjusted to match CDM and ULA models. We simulate observations of these mock universes with hypothetical analogues of the mass-limited ALFALFA and WALLABY H i surveys and reconstruct the corresponding H i mass function (HIMF). We find that the ALFALFA HIMF can test for the presence of ULA DM with $m_{a}\lesssim 10^{-21.5}~{\rm eV}$, while WALLABY could reach the larger window $m_{a}\lesssim 10^{-20.9}~{\rm eV}$. These constraints are complementary to other probes of ULA dark matter, demonstrating the utility of local Universe H i surveys in testing dark matter models.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".