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Record W2995308952 · doi:10.1063/1.5116337

Proton acceleration by collisionless shocks using a supersonic H2 gas-jet target and high-power infrared laser pulses

2019· article· en· W2995308952 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenuePhysics of Plasmas · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLaser-Plasma Interactions and Diagnostics
Canadian institutionsInstitut National de la Recherche Scientifique
FundersConseil Régional AquitaineCentre National de la Recherche ScientifiqueAgence Nationale de la RechercheCompute Canada
KeywordsPhysicsAccelerationJet (fluid)Supersonic speedLaserAtomic physicsPlasmaProtonParticle accelerationInfraredIonNuclear physicsComputational physicsOpticsMechanicsClassical mechanics

Abstract

fetched live from OpenAlex

For most laser-driven ion acceleration applications, a well-characterized intense ion beam with a low divergence and a controllable energy spectrum produced at a high repetition rate is needed. Gas-jet targets have given promising results in simulations, and they have several technical advantages for high-repetition-rate lasers. In this work, we report on proton acceleration to energies up to 6 MeV using a supersonic H2 gas-jet target at the LULI PICO2000 laser facility. The experimental results are compared with the plasma hydrodynamics and the particle-in-cell simulations to identify the acceleration mechanisms at play.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.803
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.011
GPT teacher head0.243
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it