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Record W2290446546 · doi:10.1063/1.4953895

High quality electron beam acceleration by ionization injection in laser wakefields with mid-infrared dual-color lasers

2016· article· en· W2290446546 on OpenAlexfundno aff
Ming Zeng, Ji Luo, Min Chen, W. B. Mori, Z. M. Sheng, B. Hidding

Bibliographic record

VenuePhysics of Plasmas · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLaser-Plasma Interactions and Diagnostics
Canadian institutionsnot available
FundersEupraxia PharmaceuticalsNational Key Research and Development Program of ChinaShanghai Jiao Tong UniversityLeverhulme Trust
KeywordsPhysicsLaserIonizationLaser beam qualityAtomic physicsElectronFar-infrared laserPlasmaLaser power scalingOpticsAccelerationWavelengthPlasma accelerationLaser beamsNuclear physicsIon

Abstract

fetched live from OpenAlex

For the laser wakefield acceleration, suppression of beam energy spread while keeping sufficient charge is one of the key challenges. In order to achieve this, we propose bichromatic laser ionization injection with combined laser wavelengths of 2.4 μm and 0.8 μm for wakefield excitation and triggering electron injection via field ionization, respectively. A laser pulse at 2.4 μm wavelength enables one to drive an intense acceleration structure with a relatively low laser power. To further reduce the requirement of laser power, we also propose to use carbon dioxide as the working gas medium, where carbon acts as the injection element. Our three dimensional particle-in-cell simulations show that electron beams at the GeV energy level with both low energy spreads (around 1%) and high charges (several tens of picocoulomb) can be obtained by the use of this scheme with laser peak power totaling sub-100 TW.

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 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 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 score0.555
Threshold uncertainty score0.582

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)0.0000.000

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 head0.010
GPT teacher head0.249
Teacher spread0.239 · 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 teacher head, 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

Citations16
Published2016
Admission routes1
Has abstractyes

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