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Islands of chiral solitons in integer-spin Kitaev chains

2023· article· en· W4361204470 on OpenAlexafffund
Erik S. Sørensen, Jonathon Riddell, Hae‐Young Kee

Bibliographic record

VenuePhysical Review Research · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Condensed Matter Physics
Canadian institutionsCanadian Institute for Advanced ResearchUniversity of TorontoMcMaster University
FundersAlliance de recherche numérique du CanadaNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCanadian Institute for Advanced Research
KeywordsPhysicsDegenerate energy levelsSolitonGround stateSemiclassical physicsExcited statePhase (matter)Condensed matter physicsSpin (aerodynamics)Quantum mechanicsHalf-integerChain (unit)Mathematical physicsNonlinear system

Abstract

fetched live from OpenAlex

An intriguing chiral soliton phase has recently been identified in the $S=\frac{1}{2}$ Kitaev spin chain. Here we show that for $S$ = 1, 2, 3, 4, 5 an analogous phase can be identified, but contrary to the $S=\frac{1}{2}$ case the chiral soliton phases appear as islands within the sea of the polarized phase. In fact, a small field applied in a general direction will adiabatically connect the integer spin Kitaev chain to the polarized phase. Only at sizable intermediate fields along symmetry directions does the soliton phase appear centered around the special point ${h}_{x}^{★}={h}_{y}^{★}=S$ where two exact product ground states can be identified. The large-$S$ limit can be understood from a semiclassical analysis, and variational calculations provide a detailed picture of the $S=1$ soliton phase. Under open boundary conditions, the chain has a single soliton in the ground state, which can be excited, leading to a proliferation of in-gap states. In contrast, even length periodic chains exhibit a gap above a twice-degenerate ground state. The presence of solitons leaves a distinct imprint on the low-temperature specific heat.

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)0.001
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.138
Threshold uncertainty score0.772

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.088
GPT teacher head0.490
Teacher spread0.401 · 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 designTheoretical or conceptual
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

Citations8
Published2023
Admission routes2
Has abstractyes

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