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Record W4416159272 · doi:10.48550/arxiv.2511.07331

Characterizing Mott Insulators in the Interacting One-Body Picture

2025· preprint· W4416159272 on OpenAlexfundno aff
Théo Nathaniel Dionne, Santiago Villodre, Mikel Iraola, Maia G. Vergniory

Bibliographic record

VenuearXiv (Cornell University) · 2025
Typepreprint
Language
FieldPhysics and Astronomy
TopicQuantum many-body systems
Canadian institutionsnot available
FundersAgencia Estatal de InvestigaciónUniversité de SherbrookeEuropean CommissionNatural Sciences and Engineering Research Council of CanadaCanada Excellence Research Chairs, Government of CanadaFonds de recherche du Québec – Nature et technologies
KeywordsDensity matrix renormalization groupMott insulatorHubbard modelCoupling (piping)Brillouin zoneRenormalization groupTensor (intrinsic definition)Charge (physics)Matrix (chemical analysis)Density matrix

Abstract

fetched live from OpenAlex

We present a framework to characterize Mott insulating phases within the interacting one-body picture, focusing on the Hubbard diamond chain featuring both Hubbard interactions and spin-orbit coupling simulated within cellular dynamical mean field theory. Using symmetry analysis of the single-particle Green's function, we classify spectral functions by irreducible representations at high-symmetry points of the Brillouin zone. Complementarily, we calculate the one-body reduced density matrix which allows us to reach both a qualitative description of charge distribution and an analysis of the state purity. Moreover, within the Tensor Network framework, we employ a Density Matrix Renormalization Group approach to confirm the presence of three distinct phases and their corresponding phase transitions. Our results highlight how symmetry-labelled spectral functions and effective orbital analysis provide accessible single-particle tools for probing correlation-driven insulating phases.

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 categoriesMeta-epidemiology (narrow), Research integrity
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 score0.734
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0030.002
Research integrity0.0010.003
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 head0.055
GPT teacher head0.201
Teacher spread0.146 · 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.

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

Citations0
Published2025
Admission routes1
Has abstractyes

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