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Record W4415227661 · doi:10.1063/5.0273161

Ionization dynamics of intense laser-produced argon plasmas revealed by NLTE modeling

2025· article· en· W4415227661 on OpenAlexaff
Min Sang Cho, A. L. Milder, W. Rozmus, Hai Le, H. A. Scott, D. T. Bishel, D. Turnbull, Stephen B. Libby, Mark Foord

Bibliographic record

VenuePhysics of Plasmas · 2025
Typearticle
Languageen
FieldEngineering
TopicLaser-induced spectroscopy and plasma
Canadian institutionsUniversity of Alberta
FundersLawrence Livermore National LaboratoryNational Nuclear Security Administration
KeywordsIonizationPhotoionizationPlasmaExcitationMolar ionization energies of the elementsAtmospheric-pressure laser ionizationThermal ionizationDynamics (music)

Abstract

fetched live from OpenAlex

The ionization dynamics and transient behavior of under-dense plasma irradiated by an intense laser are investigated. We report two significant effects in the ionization behavior: (1) a surprisingly large delay in ionization response and (2) a stepwise ionization process which involves collisional and laser-driven photoionization (LDP) processes. Ionization induced by intense lasers can exhibit delayed responses due to rapid changes in conditions, particularly when atomic transition processes occur more slowly than the relevant time scales. Furthermore, modeling reveals that the two-step ionization process—collisional excitation followed by LDP—plays an important role in this ionization delay, with collisional excitation acting as the bottleneck. Even low-energy photons (∼3.5 eV) can predominantly ionize plasmas, challenging the conventional belief that such energies are insufficient to overcome the binding energy of bound electrons. These findings underscore the necessity of including such processes into plasma simulations for various laser-plasma experiments.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.284
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.216
Teacher spread0.208 · 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 teacher head, not a consensus.

Study designSimulation or modeling
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
Published2025
Admission routes1
Has abstractyes

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