Multi-frequency torque magnetometry: contribution of the Einstein-de Haas effect, and direct detection of overlapping magnetic and mechanical resonance modes
Bibliographic record
Abstract
High-finesse optical nanocavities coupled with nanomechanical torque sensors have enabled highly sensitive and broadband readout of magnetic torques, from timescales involving quasi-static hysteresis response to radio-frequency magnetic susceptibility [1-3]. The extension of torque magnetometry to higher mechanical frequencies will grant further access to spin dynamics, including mechanical investigations of spin-lattice relaxation times. For nanomechanical torque magnetometry measurements into radio frequencies, the contribution of the Einstein-de Haas (EdH) effect can become comparable to, and even exceed, the conventional magnetic torque (cross-product of magnetic moment and applied field) signal [4]. Extending sensitive optomechanical detection across a ladder of higher-order mechanical modes is a natural way to extract further information, through examination of the relative scaling of EdH and cross-product torques. Sufficiently high-order mechanical modes have application to co-resonant detection of magnetic resonances. Magnetic vortex resonances intersecting the mechanical resonance spectrum will be described, and allow for the observation of dynamic vortex core interactions with magnetic inhomogeneities. [1] M. Wu et al. Nat. Nanotechnol. 12, 127 (2017). [2] G. Hajisalem et al. New J. Phys. 21, 095005 (2019). [3] J. Losby et al. J. Phys D. 51, 483001 (2018). [4] K. Mori et al. Phys. Rev. B. 102, 054415 (2020).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".