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Towards high-repetition rate petawatt laser experiments with cryogenic jets using a mechanical chopper system

2023· article· en· W4294526384 on OpenAlexaff
Martin Rehwald, Stefan Assenbaum, Constantin Bernert, C. B. Curry, M. Gauthier, S. H. Glenzer, S. Göde, Christopher Schoenwaelder, U. Schramm, F. Treffert, Karl Zeil

Bibliographic record

VenueJournal of Physics Conference Series · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLaser-Plasma Interactions and Diagnostics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsLaserChopperPlasmaOpticsJet (fluid)Materials sciencePhysicsNuclear physicsMechanicsVoltage

Abstract

fetched live from OpenAlex

Abstract Laser-plasma based ion accelerators require suitable high-repetition rate target systems that enable systematic studies at controlled plasma conditions and application-relevant particle flux. Self-refreshing, micrometer-sized cryogenic jets have proven to be an ideal target platform. Yet, operation of such systems in the harsh environmental conditions of high power laser induced plasma experiments have turned out to be challenging. Here we report on recent experiments deploying a cryogenic hydrogen jet as a source of pure proton beams generated with the PW-class ultrashort pulse laser DRACO. Damage to the jet target system during application of full energy laser shots was prevented by implementation of a mechanical chopper system interrupting the direct line of sight between the laser plasma interaction zone and the jet source.

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)0.000
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.030
GPT teacher head0.275
Teacher spread0.244 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

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