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Record W3124788326 · doi:10.1364/oe.417396

Bayesian framework for THz-TDS plasma diagnostics

2021· article· en· W3124788326 on OpenAlexfundno aff
Nathan P. Brown, Samuel J. Grauer, Jason A. Deibel, Mitchell L. R. Walker, Adam M. Steinberg

Bibliographic record

VenueOptics Express · 2021
Typearticle
Languageen
FieldEngineering
TopicTerahertz technology and applications
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaAchievement Rewards for College Scientists FoundationGeorgia Institute of TechnologyNational Science Foundation
KeywordsTerahertz radiationOpticsPlasmaPlasma diagnosticsPhysicsRefractionCollision frequencyNoise (video)SpectroscopyMeasure (data warehouse)Bayesian probabilityPlasma parametersTerahertz spectroscopy and technologyComputationComputational physicsComputer scienceAlgorithmArtificial intelligenceData mining

Abstract

fetched live from OpenAlex

Terahertz time-domain spectroscopy (THz-TDS) is an optical diagnostic used to noninvasively measure plasma electron density and collision frequency. Conventional methods for analyzing THz-TDS plasma diagnostic data often do not account for measurement artifacts and do not quantify parameter uncertainties. We introduce a novel Bayesian framework that overcomes these deficiencies. The framework enables computation of both the density and collision frequency, compensates for artifacts produced by refraction and delay line errors, and quantifies parameter uncertainties caused by noise and imprecise knowledge of unmeasured plasma properties. We demonstrate the framework with sample measurements of a radio frequency inductively-coupled plasma discharge.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.784
Threshold uncertainty score0.447

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.010
GPT teacher head0.240
Teacher spread0.229 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations15
Published2021
Admission routes1
Has abstractyes

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