Resonant Absorption of Bosonic Dark Matter in Molecules
Bibliographic record
Abstract
We propose a new class of bosonic dark matter (DM) detectors based on resonant absorption onto a gas of small polyatomic molecules. Bosonic DM acts on the molecules as a narrow-band perturbation, like an intense but weakly coupled laser. The excited molecules emit the absorbed energy into fluorescence photons that are picked up by sensitive photodetectors with low dark count rates. This setup is sensitive to any DM candidate that couples to electrons, photons, and nuclei, and may improve on current searches by several orders of magnitude in coupling for DM masses between 0.2 eV and 20 eV. This type of detector has excellent intrinsic energy resolution, along with several control variables-pressure, temperature, external electromagnetic fields, and molecular species or isotopes-that allow for powerful background rejection methods as well as precision studies of a potential DM signal. The proposed experiment does not require usage of novel exotic materials or futuristic technologies, relying instead on the well-established field of molecular spectroscopy and on recent advances in single-photon detection. Cooperative radiation effects, which arise due to the large spatial coherence of the nonrelativistic DM field in certain detector geometries, can tightly focus the DM-induced radiative emission in a direction that depends on the DM's velocity, possibly permitting a detailed reconstruction of the full 3D velocity distribution in our Galactic neighborhood, as well as further background rejection.
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".