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Record W1972460383 · doi:10.1063/1.2480610

Energetic protons generated by ultrahigh contrast laser pulses interacting with ultrathin targets

2007· article· en· W1972460383 on OpenAlexaff
P. Antici, J. Fuchs, E. d’Humières, E. Lefebvre, M. Borghesi, E. Brambrink, C. A. Cecchetti, Ségolène Gaillard, L. Romagnani, Y. Sentoku, T. Toncian, O. Willi, P. Audebert, H. Pépin

Bibliographic record

VenuePhysics of Plasmas · 2007
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLaser-Plasma Interactions and Diagnostics
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsLaserPhysicsProtonAtomic physicsPlasmaPulse (music)ElectronAbsorption (acoustics)IonAccelerationPulse durationIntensity (physics)IrradiationOpticsNuclear physicsDetector

Abstract

fetched live from OpenAlex

A regime of laser acceleration of protons, which relies on the interaction of ultrahigh contrast laser pulses with ultrathin targets, has been validated using experiments and simulations. Proton beams were accelerated to a maximum energy of ∼7.3MeV from targets as thin as 30nm irradiated at 1018Wcm−2μm2 (1J, 320fs) with an estimated peak laser pulse to pedestal intensity contrast ratio of 1011. This represents nearly a tenfold increase in proton energy compared to the highest energies obtainable using non contrast enhanced pulses and thicker targets (>5μm) at the same intensity. To obtain similar proton energy with thicker targets and the same laser pulse duration, a much higher laser intensity (i.e., above 1019Wcm−2μm2) is required. The simulations are in close agreement with the experimental results, showing efficient electron heating compared to the case of thicker targets. Rapid target expansion, allowing laser absorption in density gradients, is key to enhanced electron heating and ion acceleration in ultrathin targets.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.008
GPT teacher head0.239
Teacher spread0.231 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations99
Published2007
Admission routes1
Has abstractyes

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