Superradiant Interactions for Relic Detection with Entangled Nuclear Spins
Bibliographic record
Abstract
We recently showed that macroscopic nuclear spin ensembles prepared in coherent spin states can dramatically enhance the interaction rates of weakly interacting cosmic relics-such as dark matter and the cosmic neutrino background-through collective quantum effects analogous to Dicke superradiance, where the de-excitation and excitation rates scale as the square of the number of spins, $N^2$. We thus coined these processes superradiant interactions. In this paper, we propose a protocol to realize this enhancement and boost the discovery potential for such relics. We show how concepts from quantum optics can be adapted to nuclear spins coupled to superconducting circuits, enabling high-sensitivity systems. The spins are first initialized into a coherent spin state via a $π/2$ Rabi pulse from the ground state. When the circuit is sufficiently detuned from resonance, the spin-circuit interaction implements a squeezing Hamiltonian. Because squeezing must outpace spin relaxation and dephasing, the protocol favors macroscopic ensembles and high-quality superconducting circuits. During this squeezing phase, the standard quantum variance is reduced by up to 4.8 orders of magnitude-equivalent to 48 dB of squeezing-for circuits with quality factors $Q \sim 10^8$-$10^9$. The signal imprinted on the spins during the squeezing protocol can be magnified by further utilizing the squeezing interactions, easing the requirement for shot-noise-limited readout. This protocol has the potential to significantly accelerate axion and dark photon dark matter searches and extend the reach of existing axion experiments to probe QCD axion-nuclear spin couplings. More broadly, it paves the way for detecting coherent inelastic interactions from other cosmic relics-most notably the cosmic neutrino background-and establishes nuclear-spin-based systems as a new class of quantum, ultra-low-threshold detectors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".