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Record W2059718826 · doi:10.1109/plasma.2012.6383998

Generation of GeV energy electrons from laser wakefield acceleration via ionization induced injection

2012· article· en· W2059718826 on OpenAlexaff
Mianzhen Mo, A. W. Ali, Neda Naseri, W. Rozmus, R. Fedosejevs, S. Fourmaux, Philippe Lassonde, J. C. Kieffer, P. E. Masson-Laborde

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLaser-Plasma Interactions and Diagnostics
Canadian institutionsInstitut National de la Recherche ScientifiqueUniversity of Alberta
Fundersnot available
KeywordsElectronAtomic physicsIonizationAccelerationPlasmaLaserPhysicsPlasma accelerationHeliumHydrogenNuclear physicsIonOptics

Abstract

fetched live from OpenAlex

Laser wakefield acceleration (LWFA) is a promising approach to realize table-top accelerators. The injection process into the wakefield bubble to some extent determines the charge, divergence, energy gain as well as the energy distribution of the accelerated electrons. Traditionally, self injection using pure helium or hydrogen gas as the interaction medium was employed to accelerate the electrons. However, very high laser powers are required to achieve self injection at the low densities which are compatible with acceleration to GeV energies. Recently, a new technique, ionization induced injection, which takes advantage of the large ionization potential difference between the inner and outer shell electrons of trace atoms in the plasma, has been demonstrated to generate electron beams beyond 1 GeV at lower threshold laser powers than self injection would require [1].

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 score0.003
Threshold uncertainty score0.009

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.255
Teacher spread0.227 · 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

Citations0
Published2012
Admission routes1
Has abstractyes

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