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Ion energy scaling under optimum conditions of laser plasma acceleration from solid density targets

2015· article· en· W314526746 on OpenAlexafffund
A. V. Brantov, E. A. Govras, V. Yu. Bychenkov, W. Rozmus

Bibliographic record

VenuePhysical Review Special Topics - Accelerators and Beams · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLaser-Plasma Interactions and Diagnostics
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaRussian Science FoundationRussian Foundation for Basic Research
KeywordsLaserScalingAtomic physicsRange (aeronautics)FemtosecondIonProtonPlasmaAccelerationEnergy (signal processing)FOIL methodMaterials sciencePhysicsPulse (music)Radiation pressureOpticsComputational physicsNuclear physicsClassical mechanics

Abstract

fetched live from OpenAlex

A new, maximum proton energy, $ϵ$, scaling law with the laser pulse energy, ${E}_{L}$, has been derived for solid density foils from the results of 3D particle-in-cell simulations. Utilizing numerical modeling, protons are accelerated during interactions of the femtosecond relativistic laser pulses with the plain semitransparent targets of optimum thickness. The scaling, $ϵ\ensuremath{\sim}{E}_{L}^{0.7}$, has been obtained for the wide range of laser energies, different spot sizes, and laser pulse durations. Our results show that the proper selection of foil target optimum thicknesses results in a very promising increase of the proton energy with the laser intensity even in the range of parameters below the radiation pressure (light sail) regime. The proposed analytical model is consistent with numerical simulations.

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)0.001
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.028
GPT teacher head0.308
Teacher spread0.279 · 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

Citations36
Published2015
Admission routes2
Has abstractyes

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