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Record W4407321282 · doi:10.1103/physrevb.111.l060405

Observing altermagnetism using polarized neutrons

2025· article· en· W4407321282 on OpenAlexafffund
Paul McClarty, Arsen Gukasov, Jeffrey G. Rau

Bibliographic record

VenuePhysical review. B./Physical review. B · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAtomic and Subatomic Physics Research
Canadian institutionsUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of CanadaCentre National de la Recherche ScientifiqueUniversity of Windsor
KeywordsNeutronPhysicsNuclear physics

Abstract

fetched live from OpenAlex

Altermagnets are collinear compensated magnets whose magnetic symmetries at zero spin-orbit coupling break spin degeneracy leading to spin-split electronic and magnonic bands that reflect an underlying multipolar order. When there is an approximate $U(1)$ symmetry the magnons in altermagnets are split into equal and opposite chiral pairs. We show that in altermagnets polarized neutrons provide a means to detect the population of time-reversed domains and allow direct measurement of the magnon chirality anisotropy in momentum space---the central signature of the altermagnetic phase. We demonstrate this response to polarized neutrons in two candidate materials ${\mathrm{MnF}}_{2}$ and MnTe and show that the presence of these chiralities is stable to small perturbations that break spin-rotation symmetry. This provides a magnonic analog of spin-polarized ARPES that has been used to discern altermagnetism in the electronic band structures of various candidate materials.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.031
GPT teacher head0.429
Teacher spread0.398 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations16
Published2025
Admission routes2
Has abstractyes

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