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Record W2888438017 · doi:10.1038/s41586-018-0485-4

Acceleration of electrons in the plasma wakefield of a proton bunch

2018· article· en· W2888438017 on OpenAlexafffund
E. Adli, Amit Ahuja, Ö. Apsimon, R. Apsimon, A.-M. Bachmann, D. Barrientos, Fabian Batsch, J. Bauche, Veronica Olsen, M. Bernardini, Thomas Bohl, Chiara Bracco, F. Braunmüller, Graeme Burt, B. Buttenschön, A. Caldwell, M. Cascella, J. Chappell, E. Chevallay, М. Chung, D. Cooke, Heiko Damerau, L. Deacon, L. H. Deubner, A. Dexter, S. Doebert, John Farmer, V. N. Fedosseev, R. Fiorito, Ricardo Fonseca, F. Friebel, Luca Garolfi, Spencer Gessner, Ishkhan Gorgisyan, A. A. Gorn, E. Granados, O. Grulke, Y. Kadi, J. B. Hansen, Anton Helm, James R. Henderson, M. Hüther, M. Ibison, L. Jensen, S. Jolly, F. Keeble, S. Kim, Florian Kraus, Y. Li, S. Liu, N. Lopes, К. В. Лотов, L. Maricalva Brun, Mikhail Martyanov, S. Mazzoni, D. Medina Godoy, V. A. Minakov, J. Mitchell, John Molendijk, J. T. Moody, M. Moreira, P. Muggli, E. Öz, C. Pasquino, A. Pardons, F. Peña Asmus, K. Pépitone, A. Perera, Alexey Petrenko, Sam Pitman, A. Pukhov, S. Rey, K. Rieger, H. Rühl, Janet Schmidt, Irina Shalimova, L. O. Silva, L. Søby, A. P. Sosedkin, R. Speroni, Р. И. Спицын, П. В. Туев, M. Turner, Francesco Velotti, L. Verra, V. Verzilov, J. Vieira, Carsten Welsch, B. Williamson, M. Wing, Benjamin Woolley, Guoxing Xia

Bibliographic record

VenueNature · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLaser-Plasma Interactions and Diagnostics
Canadian institutionsTRIUMF
FundersFundação para a Ciência e a TecnologiaNational Research FoundationScience and Technology Facilities CouncilNational Research Council CanadaDeutsches Elektronen-SynchrotronNatural Sciences and Engineering Research Council of CanadaRussian Science FoundationNorges ForskningsrådNational Research Foundation of KoreaCERNDeutsche ForschungsgemeinschaftLeverhulme Trust
KeywordsAccelerationPlasma accelerationProtonElectronPhysicsPlasmaNuclear physicsAtomic physicsClassical mechanics

Abstract

fetched live from OpenAlex

Abstract High-energy particle accelerators have been crucial in providing a deeper understanding of fundamental particles and the forces that govern their interactions. To increase the energy of the particles or to reduce the size of the accelerator, new acceleration schemes need to be developed. Plasma wakefield acceleration 1–5 , in which the electrons in a plasma are excited, leading to strong electric fields (so called ‘wakefields’), is one such promising acceleration technique. Experiments have shown that an intense laser pulse 6–9 or electron bunch 10,11 traversing a plasma can drive electric fields of tens of gigavolts per metre and above—well beyond those achieved in conventional radio-frequency accelerators (about 0.1 gigavolt per metre). However, the low stored energy of laser pulses and electron bunches means that multiple acceleration stages are needed to reach very high particle energies 5,12 . The use of proton bunches is compelling because they have the potential to drive wakefields and to accelerate electrons to high energy in a single acceleration stage 13 . Long, thin proton bunches can be used because they undergo a process called self-modulation 14–16 , a particle–plasma interaction that splits the bunch longitudinally into a series of high-density microbunches, which then act resonantly to create large wakefields. The Advanced Wakefield (AWAKE) experiment at CERN 17–19 uses high-intensity proton bunches—in which each proton has an energy of 400 gigaelectronvolts, resulting in a total bunch energy of 19 kilojoules—to drive a wakefield in a ten-metre-long plasma. Electron bunches are then injected into this wakefield. Here we present measurements of electrons accelerated up to two gigaelectronvolts at the AWAKE experiment, in a demonstration of proton-driven plasma wakefield acceleration. Measurements were conducted under various plasma conditions and the acceleration was found to be consistent and reliable. The potential for this scheme to produce very high-energy electron bunches in a single accelerating stage 20 means that our results are an important step towards the development of future high-energy particle accelerators 21,22 .

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 score0.010

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 science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.008
GPT teacher head0.276
Teacher spread0.268 · 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

Citations260
Published2018
Admission routes2
Has abstractyes

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