Estimates of the Dynamic Structure Factor for the Uniform Electron Gas in the Warm Dense Matter Regime via Analytic Continuation of Quantum Monte Carlo Data
Bibliographic record
Abstract
Quantum Monte Carlo (QMC) simulations are one of the few methods which can describe the structure of the plasma in Warm Dense Matter. However, the imaginary time correlation functions (ITCF) estimated by QMC simulation must be analytically continuated back to real time to extract dynamic information about the system. One of the most ubiquitous approaches to analytic continuation is the maximum entropy method (MEM). The MEM is typically used with Bryan's controversial algorithm [Rothkopf, “Bryan's Maximum Entropy Method” Data 5.3 (2020)]. We supply a dual Newton optimization algorithm to be used within the MEM that addresses known issues. We pay special attention to our uncertainty providing analytic bounds for the algorithm's error as well as numerical estimates of the uncertainty arising from both the ITCF and the regularization weight. We use the MEM to investigate authentic quantum Monte Carlo data for the uniform electron gas and further substantiate the roton-type feature in the dispersion relation, demonstrating that the all-purpose maximum entropy method can reliably estimate the DSF from QMC data. This establishes a path towards model-free estimates of the DSF in ion-electron systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".