Quantum fluctuations masquerade as haloes: bounds on ultra-light dark matter from quadruply imaged quasars
Bibliographic record
Abstract
ABSTRACT Ultra-light dark matter (ULDM) refers to a class of theories, including ultra-light axions, in which particles with mass $m_{\psi } \lt 10^{-20}\, \rm {eV}$ comprise a significant fraction of the dark matter. A galactic scale de Broglie wavelength distinguishes these theories from cold dark matter (CDM), suppressing the overall abundance of structure on sub-galactic scales, and producing wave-like interference phenomena in the density profiles of haloes. With the aim of constraining the particle mass, we analyse the flux ratios in a sample of 11 quadruple-image strong gravitational lenses. We account for the suppression of the halo mass function and concentration–mass relation predicted by ULDM theories, and the wave-like fluctuations in the host halo density profile, calibrating the model for the wave interference against numerical simulations of galactic-scale haloes. We show that the granular structure of halo density profiles, in particular, the amplitude of the fluctuations, significantly impacts image flux ratios, and therefore inferences on the particle mass derived from these data. We infer relative likelihoods of CDM to ULDM of 8:1, 7:1, 6:1, and 4:1 for particle masses $\log _{10}(m_\psi /\rm {eV})\in [-22.5,-22.25], [-22.25,-22.0],[-22.0,-21.75], [-21.75,-21.5]$, respectively. Repeating the analysis and omitting fluctuations associated with the wave interference effects, we obtain relative likelihoods of CDM to ULDM with a particle mass in the same ranges of 98:1, 48:1, 26:1, and 18:1, highlighting the significant perturbation to image flux ratios associated with the fluctuations. Nevertheless, our results disfavour the lightest particle masses with $m_{\psi } \lt 10^{-21.5}\, \rm {eV}$, adding to mounting pressure on ultra-light axions as a viable dark matter candidate.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".