Finite-temperature dynamics in 0-flux and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>π</mml:mi></mml:math>-flux quantum spin ice: Self-consistent exclusive boson approach
Bibliographic record
Abstract
Quantum spin ice (QSI) is an emblematic three-dimensional $U(1)$ quantum spin liquid (QSL) on the pyrochlore lattice that hosts gapless photon-like modes and spinon excitations. Despite its notable status and the current rise of strong material candidates ${\mathrm{Ce}}_{2}$(Zr, Sn, ${\mathrm{Hf})}_{2}{\mathrm{O}}_{7}$, there are still only a few analytical approaches to model the low-energy physics of QSI. These analytical methods are essential to gain insight into the physical interpretation of measurements. We here introduce the self-consistent exclusive boson representation (SCEBR) to model emergent spinon excitations in QSI. By treating the presence of other emergent charges in an average way, the SCEBR extends the range of validity of the exclusive boson representation previously introduced in [Hao, Day, and Gingras, Phys. Rev. B 90, 214430 (2014)] to numerous cases of physical relevance. We extensively benchmark the approach and provide detailed analytical expressions for the spinon dispersion, the Bogoliubov transformation that diagonalizes the system, and the dynamical spin structure factor for 0- and $\ensuremath{\pi}$-flux QSI. Finite-temperature properties are further investigated to highlight essential differences between the thermodynamic behavior of the 0- and $\ensuremath{\pi}$-flux phases. We notably show that the SCEBR predicts a reduction of the spinon bandwidth with increasing temperature, consistent with previous quantum Monte Carlo results, through suppression of spinon hopping by thermal occupation. The SCEBR thus provides a powerful analytical tool to interpret experiments on current and future candidate material that has several advantages over other widely used methods.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".