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Record W2789952176 · doi:10.1088/2399-6528/aaaf3a

Induced transparency in the XUV: a pump-probe test of laser-cluster interactions

2018· article· en· W2789952176 on OpenAlexaff
Rishi Pandit, Kasey Barrington, Thomas Teague, Valerie R. Becker, Jeremy Thurston, Zachary Hartwick, Nicolas Bigaouette, Lora Ramunno, Edward Ackad

Bibliographic record

VenueJournal of Physics Communications · 2018
Typearticle
Languageen
FieldEngineering
TopicLaser-induced spectroscopy and plasma
Canadian institutionsUniversity of Ottawa
FundersAir Force Office of Scientific ResearchOffice of Advanced Cyberinfrastructure
KeywordsExtreme ultravioletTransparency (behavior)Cluster (spacecraft)LaserOpticsPhysicsComputer scienceComputer security

Abstract

fetched live from OpenAlex

An experiment is proposed to distinguish between different laser-cluster atomistic models and their predictions. The induced transparency of rare-gas clusters, post-interaction with an extreme ultraviolet (XUV) pump-pulse, is predicted by using an atomistic hybrid quantum–classical molecular dynamics model. We find there is an intensity range for which an XUV probe-pulse has no lasting effect on the average charge state of a cluster after being saturated by an XUV pump-pulse: the cluster is transparent to the probe-pulse. Multiple complete experimental signals are calculated which include the effect of the pulse's spatial distribution as well as the cluster size distribution. The calculated experimental signals and trends are also accomplished with the addition of an ionization potential lowering model that results in effectively removing the induced transparency effect. Thus, the proposed experiment is expected to either find the new phenomenon of induced transparency in clusters or give strong evidence for the existence of the enhanced ionization phenomenon, ionization potential lowering, in nanoplasmas.

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)0.001
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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.042
GPT teacher head0.298
Teacher spread0.256 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

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