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Observation of Collective Resonance Modes in a Chiral Spin Soliton Lattice with Tunable Magnon Dispersion

2022· article· en· W4283009393 on OpenAlexaff
Y. Shimamoto, Y. Matsushima, T. Hasegawa, Yusuke Kousaka, Igor Proskurin, Jun‐ichiro Kishine, А. С. Овчинников, F. J. T. Gonçalves, Yoshihiko Togawa

Bibliographic record

VenuePhysical Review Letters · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic properties of thin films
Canadian institutionsUniversity of Manitoba
FundersJapan Society for the Promotion of ScienceMinistry of Science and Higher Education of the Russian FederationToyota Physical and Chemical Research InstituteRussian Foundation for Basic Research
KeywordsPhysicsCondensed matter physicsPhononTranslational symmetryMagnonExcitationSpinsDispersion relationLattice (music)Resonance (particle physics)Symmetry breakingQuantum mechanicsFerromagnetism

Abstract

fetched live from OpenAlex

A chiral spin soliton lattice (CSL), one of the representative systems of a magnetic superstructure, exhibits reconfigurability in periodicity over a macroscopic length scale. Such coherent and tunable characteristics of the CSL lead to an emergence of elementary excitation of the CSL as phononlike modes due to translational symmetry breaking and bring a controllability of the dispersion relation of the CSL phonon. Using a broadband microwave spectroscopy technique, we directly found that higher-order magnetic resonance modes appear in the CSL phase of a chiral helimagnet CrNb_{3}S_{6}, which is ascribed to the CSL phonon response. The resonance frequency of the CSL phonon can be tuned between 16 and 40 GHz in the vicinity of the critical field, where the CSL period alters rapidly. The frequency range of the CSL phonon is expected to extend over 100 GHz as extrapolated on the basis of the theoretical model. The present results indicate that chiral helimagnets could work as materials useful for broadband signal processing in the millimeter-wave band.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.439
Threshold uncertainty score0.453

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.251
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations19
Published2022
Admission routes1
Has abstractyes

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