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Record W3124722015 · doi:10.1088/1367-2630/abdf9a

Towards the optimisation of direct laser acceleration

2021· article· en· W3124722015 on OpenAlexfundno aff
Amina Hussein, Alexey Arefiev, Thomas Batson, H Chen, R. S. Craxton, Andrew Davies, D. H. Froula, Zheng Gong, D. Haberberger, Y. Ma, P. M. Nilson, W. Theobald, T. Wang, Kathleen Weichman, G. J. Williams, L. Willingale

Bibliographic record

VenueNew Journal of Physics · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLaser-Plasma Interactions and Diagnostics
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaLawrence Livermore National LaboratoryEngineering and Physical Sciences Research CouncilU.S. Department of Energy
KeywordsPhysicsElectronPlasma channelLaserAtomic physicsAccelerationPlasmaPulse (music)PicosecondMagnetic fieldElectric fieldTransverse planeOpticsNuclear physicsClassical mechanicsQuantum mechanics

Abstract

fetched live from OpenAlex

Abstract Experimental measurements using the OMEGA EP laser facility demonstrated direct laser acceleration (DLA) of electron beams to (505 ± 75) MeV with (140 ± 30) nC of charge from a low-density plasma target using a 400 J, picosecond duration pulse. Similar trends of electron energy with target density are also observed in self-consistent two-dimensional particle-in-cell simulations. The intensity of the laser pulse is sufficiently large that the electrons are rapidly expelled along the laser pulse propagation axis to form a channel. The dominant acceleration mechanism is confirmed to be DLA and the effect of quasi-static channel fields on energetic electron dynamics is examined. A strong channel magnetic field, self-generated by the accelerated electrons, is found to play a comparable role to the transverse electric channel field in defining the boundary of electron motion.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.027
GPT teacher head0.274
Teacher spread0.247 · 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 designSimulation or modeling
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

Citations59
Published2021
Admission routes1
Has abstractyes

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