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Record W1913411741 · doi:10.1109/nssmic.1997.672621

The effect of electron multiplication on the electroluminescence yield of pure xenon and xenon-neon gas proportional scintillation counters: experimental and simulation results

2002· article· en· W1913411741 on OpenAlexaff
T.H.V.T. Dias, F.P. Santos, P.J.B.M. Rachinhas, F.I.G.M. Borges, J.M.F. dos Santos, A D Stauffer, C.A.N. Conde

Bibliographic record

Venue1997 IEEE Nuclear Science Symposium Conference Record · 2002
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAtomic and Subatomic Physics Research
Canadian institutionsYork University
Fundersnot available
KeywordsXenonNeonScintillationElectroluminescenceScintillation counterAtomic physicsIonizationNoble gasPhysicsMaterials scienceDetectorOpticsArgonNanotechnology

Abstract

fetched live from OpenAlex

In applications of the gas proportional scintillation counter to the detection of very low energy X-rays, the addition of the light noble gas neon to the usual xenon filling improves the collection of primary electrons that would have originated near the detector window. However, xenon-neon mixtures produce lower electroluminescence yields than pure xenon. The highest electroluminescence yield that can be achieved without jeopardizing the energy resolution is limited by the additional fluctuations introduced by electron multiplication and, consequently these detectors are usually operated at reduced electric fields below the ionization threshold. In this work, a compromise between electroluminescence output and energy resolution is investigated for xenon-neon mixtures at a total pressure of 800 Torr (with 5%, 10%, 20%, 40%, 70%, 90% and 100%Xe), and for 5.9 keV X-rays. Using experimental and Monte Carlo studies, the effects of introducing a limited amount of charge multiplication on the electroluminescence yield and on the detector energy resolution are analysed and discussed, and the optimum operating conditions for gas proportional scintillation work are established.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.801
Threshold uncertainty score0.368

Codex and Gemma teacher scores by category

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 communication0.0000.000
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 head0.017
GPT teacher head0.271
Teacher spread0.254 · 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 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
Published2002
Admission routes1
Has abstractyes

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