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Record W4200167077 · doi:10.1364/josab.444438

High-resolution dual-energy sixteen-channel Kirkpatrick–Baez microscope for ultrafast laser plasma diagnostics

2021· article· en· W4200167077 on OpenAlexaff
Shengzhen Yi, Haoxuan Si, Ke Fang, Zhiheng Fang, Jiali Wu, Runze Qi, Xiaohui Yuan, Zhe Zhang, Zhanshan Wang

Bibliographic record

VenueJournal of the Optical Society of America B · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLaser-Plasma Interactions and Diagnostics
Canadian institutionsCanadian Association of Emergency Physicians
FundersFundamental Research Funds for the Central UniversitiesNational Key Research and Development Program of ChinaNational Natural Science Foundation of China
KeywordsMicroscopeOpticsResolution (logic)Inertial confinement fusionPlasmaSpectral resolutionLaserImage resolutionPhysicsMicroscopyFraming (construction)Ultrashort pulseTemporal resolutionMaterials scienceSpectral lineNuclear physicsComputer science

Abstract

fetched live from OpenAlex

High-resolution x-ray imaging diagnostics play a crucial role in fundamental research, such as inertial confinement fusion (ICF) and high-energy density physics (HEDP). Plasma signals are typically characterized by small scales, rapid evolution, and spectral complexity. These characteristics make it essential to develop x-ray diagnostics optics with high spatial resolution, collection efficiency, and spectral resolution. These requirements can be met using a combination of a high-resolution multi-channel Kirkpatrick–Baez (KB) microscope with spectrum-resolved multilayers and a time-resolved framing camera. This study describes the optical and multilayer design of a dual-energy sixteen-channel KB microscope. The calibrated results of online and offline imaging are shown. By utilizing a dual-energy multi-channel KB microscope, high-resolution backlighting and self-emission x-ray imaging can be realized and detailed information related to plasma density and temperature can be simultaneously obtained.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.703
Threshold uncertainty score0.535

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
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 head0.234
Teacher spread0.226 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations13
Published2021
Admission routes1
Has abstractyes

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