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Scaling the Yield of Laser-Driven Electron-Positron Jets to Laboratory Astrophysical Applications

2015· article· en· W1791388022 on OpenAlexaff
Hui Chen, Frederico Fiúza, A. Link, A. Hazi, M. P. Hill, D. J. Hoarty, S. F. James, S. Kerr, D. D. Meyerhofer, J. F. Myatt, Jaebum Park, Y. Sentoku, G. J. Williams

Bibliographic record

VenuePhysical Review Letters · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLaser-Plasma Interactions and Diagnostics
Canadian institutionsUniversity of Alberta
FundersLaboratory Directed Research and DevelopmentBonnie J. Addario Lung Cancer FoundationLawrence Livermore National LaboratoryUniversity of California, Los AngelesU.S. Department of Energy
KeywordsPhysicsScalingPositronNuclear physicsElectronYield (engineering)LaserPositron emissionAtomic physicsOpticsThermodynamics

Abstract

fetched live from OpenAlex

We report new experimental results obtained on three different laser facilities that show directed laser-driven relativistic electron-positron jets with up to 30 times larger yields than previously obtained and a quadratic (∼E_{L}^{2}) dependence of the positron yield on the laser energy. This favorable scaling stems from a combination of higher energy electrons due to increased laser intensity and the recirculation of MeV electrons in the mm-thick target. Based on this scaling, first principles simulations predict the possibility of using such electron-positron jets, produced at upcoming high-energy laser facilities, to probe the physics of relativistic collisionless shocks in the laboratory.

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)0.001
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.016
GPT teacher head0.293
Teacher spread0.277 · 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

Citations131
Published2015
Admission routes1
Has abstractyes

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