Superradiant interactions of the cosmic neutrino background, axions, dark matter, and reactor neutrinos
Bibliographic record
Abstract
In this paper, we do three things. First, we outline the conditions under which the interaction rate of processes that change the internal state of a system of N targets scales as N 2 . This is an effect distinct from coherent scattering but with the same scaling. Second, we compute example rates for such processes for various weakly interacting particles. Finally, we point to potential quantum observables for these processes that go beyond traditional energy exchange. Maximal coherence in inelastic processes is achieved when the targets are placed in an equal superposition of the ground and excited states. These coherent inelastic processes are analogous to Dicke superradiance, where cooperative effects reinforce the emission of radiation from matter, and we thus refer to them as interactions. We compute the superradiant interaction rates for the cosmic neutrino background ( C ν B ), dark matter scattering and absorption, and late-universe particles, such as reactor neutrinos, when the two-level system is realized by nuclear or electron spins in a magnetic field. The rates we find can be quite sizable on macroscopic yet small targets. For example, the C ν B interacts with a rate of O ( Hz ) when scattering off a 10 cm liquid or solid-state density spin-polarized sphere, a O ( 10 21 ) enhancement compared to the incoherent inelastic contribution. For QCD axion dark matter, similar rates can be achieved with much smaller samples, N ∼ O ( 10 15 ) ( m 2 × 10 − 8 eV ) − 1 / 2 , where m is the axion mass. Using the Lindblad formalism for open quantum systems, we show that these superradiant interactions can manifest as a source of noise on the system. This noise is tunable however and can serve as a signature of new physics, as the energy splitting controls the momentum transfer and hence, the amount of macroscopic coherence. These considerations point to new observables that go beyond traditional net energy exchange. These observables are sensitive to the of the excitation and deexcitation rates—instead of the net energy exchange rate which can be very suppressed—and can be viewed as introducing diffusion and decoherence to the system. While we postpone to upcoming work proposing a concrete protocol that extracts these effects from a macroscopic ensemble of atoms, the effects presented in this paper may point to a new class of ultra-low threshold detectors.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".