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Record W2032525003 · doi:10.1103/physrevb.89.104519

Tuning between singlet, triplet, and mixed pairing states in an extended Hubbard chain

2014· article· en· W2032525003 on OpenAlexfundno aff
Kuei Sun, Ching‐Kai Chiu, Hsiang-Hsuan Hung, Jiansheng Wu

Bibliographic record

VenuePhysical Review B · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPhysics of Superconductivity and Magnetism
Canadian institutionsnot available
FundersMaterials Research Science and Engineering Center, Harvard UniversityMinistero dello Sviluppo EconomicoCalifornia NanoSystems InstituteArmy Research OfficeInstitut Périmètre de physique théoriqueDivision of Materials ResearchHong Kong University of Science and TechnologyNational Science FoundationGovernment of CanadaIndustry CanadaDefense Advanced Research Projects AgencyUniversity of Cincinnati
KeywordsPairingPhysicsSinglet stateHamiltonian (control theory)FermionQuantum mechanicsHubbard modelCondensed matter physicsTopology (electrical circuits)SuperconductivityExcited stateMathematics

Abstract

fetched live from OpenAlex

We study spin-half fermions in a one-dimensional extended Hubbard chain at low filling. We identify three triplet and one singlet pairing channels in the system, which are independently tunable as a function of nearest-neighbor charge and spin interactions. In a large-size system with translational invariance, we derive gap equations for the corresponding pairing gaps and obtain a Bogoliubov--de Gennes Hamiltonian with its nontrivial topology determined by the interplay of these gaps. In an open-end system with a fixed number of particles, we compute the exact many-body ground state and identify the dominant pairing revealed by the pair density matrix. Both cases show competition between the four pairing states, resulting in broad regions for each of them and relatively narrow regions for mixed-pairing states in the parameter space. Our results enable the possibility of tuning a nanowire between singlet and triplet pairing states without breaking time-reversal or $\text{SU}(2)$ symmetry, accompanied by a change in the system's topology.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.920
Threshold uncertainty score0.822

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.027
GPT teacher head0.294
Teacher spread0.268 · 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

Citations28
Published2014
Admission routes1
Has abstractyes

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