Exploring the crossover between high-energy-density plasma and ultracold neutral plasma physics
Bibliographic record
Abstract
In this paper, we present ideas that were part of the miniconference on the crossover between High Energy Density Plasmas (HEDP) and Ultracold Neutral Plasmas (UNPs) at the 60th Annual Meeting of the American Physical Society Division of Plasma Physics, November 2018. We give an overview of UNP experiments with an emphasis on measurements of the time-evolving ion density and velocity distributions, the electron-ion thermalization rate, and plasma self-assembly—all just inside the strongly coupled plasma regime. We also present theoretical and computational models that were developed to understand a subset of HEDP experiments. However, because HEDP experiments display similar degrees of strong coupling, many aspects of these models can be vetted using precision studies of UNPs. This comparison is important because some statistical assumptions used for ideal plasmas are of questionable validity in the strongly coupled plasma regime. We summarize two theoretical approaches that extend kinetic theories into the strong-coupling regime and show good agreement for momentum transfer and self-diffusion. As capabilities improve, both computationally and experimentally, UNP measurements may help guide the ongoing development of HEDP-appropriate plasma models. Future opportunities in viscosity, energy relaxation, and magnetized plasmas are discussed.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".