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 inelastic processes that change the internal state of a system of $N$ targets scales as $N^2$. This is an effect distinct from coherent elastic scattering, but with the same scaling. Second, we compute rates for such processes for various weakly interacting particles. Finally, we point to potential quantum observables for these processes, beyond 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, and we thus refer to them as superradiant 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 can be sizeable on macroscopic yet small targets. For example, the C$ν$B interacts with a rate of $\mathcal{O}(\text{Hz})$ when scattering off a 10~cm liquid or solid-state density spin-polarized sphere, a $\mathcal{O}(10^{21})$ enhancement compared to the incoherent contribution. For QCD axion dark matter, similar rates can be achieved with much smaller samples, $N \sim \mathcal{O}(10^{15})\left(\frac{m}{2\times 10^{-8}~\text{eV}}\right)^{-1/2}$, where $m$ is the axion mass. Using the Lindblad formalism, we show that these superradiant interactions can manifest as a source of noise on the system. This points to new observables, sensitive to the sum of the excitation and de-excitation rates, and can be viewed as introducing diffusion and decoherence to the system. 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 | 0.001 |
| 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".