Diagnostics for fast ignition science (invited)
Bibliographic record
Abstract
The ignition concept for electron fast ignition inertial confinement fusion requires sufficient energy be transferred from an approximately 20 ps laser pulse to the compressed fuel via approximately MeV electrons. We have assembled a suite of diagnostics to characterize such transfer, simultaneously fielding absolutely calibrated extreme ultraviolet multilayer imagers at 68 and 256 eV; spherically bent crystal imagers at 4.5 and 8 keV; multi-keV crystal spectrometers; MeV x-ray bremmstrahlung, electron and proton spectrometers (along the same line of sight), and a picosecond optical probe interferometer. These diagnostics allow careful measurement of energy transport and deposition during and following the laser-plasma interactions at extremely high intensities in both planar and conical targets. Together with accurate on-shot laser focal spot and prepulse characterization, these measurements are yielding new insights into energy coupling and are providing critical data for validating numerical particle-in-cell (PIC) and hybrid PIC simulation codes in an area crucial for fast ignition and other applications. Novel aspects of these diagnostics and how they are combined to extract quantitative data on ultrahigh intensity laser-plasma interactions are discussed.
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.011 |
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".