Dynamic structure factor of superfluid <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mmultiscripts><mml:mi>He</mml:mi><mml:mprescripts/><mml:none/><mml:mn>4</mml:mn></mml:mmultiscripts></mml:math> from quantum Monte Carlo: Maximum entropy revisited
Bibliographic record
Abstract
We use the maximum entropy method (MaxEnt) to estimate the dynamic structure factor of superfluid $^{4}\mathrm{He}$ at $T=1$ K, by inverting imaginary-time density correlation functions computed by quantum Monte Carlo (QMC) simulation. Our procedure consists of a Metropolis random walk in the space of all possible spectral images, sampled from a probability density which includes the entropic prior, in the context of the so-called ``classic'' MaxEnt. Comparison with recent work by other authors shows that contrary to what is often stated, sharp features in the reconstructed image are not ``washed out'' by the entropic prior if the underlying QMC data have sufficient precision. Only spurious features that tend to appear in a straightforward ${\ensuremath{\chi}}^{2}$ minimization are suppressed.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".