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Record W7014340089

Pile-up corrections for high-precision superallowed β decay half-life measurements via γ-ray photopeak counting

2007· article· en· W7014340089 on OpenAlexaboutno aff

Bibliographic record

VenueCivil War Book Review · 2007
Typearticle
Languageen
FieldPhysics and Astronomy
TopicRadioactive Decay and Measurement Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsDetectorMonte Carlo methodSemiconductor detectorSpectrometerRadioactive decayLiquid scintillation countingPulse (music)Germanium
DOInot available

Abstract

fetched live from OpenAlex

A general technique that corrects γ-ray gated β decay-curve data for detector pulse pile-up is presented. The method includes corrections for non-zero time-resolution and energy-threshold effects in addition to a special treatment of saturating events due to cosmic rays. This technique is verified through a Monte Carlo simulation and experimental data using radioactive beams of 26Na implanted at the center of the 8 π γ-ray spectrometer at the ISAC facility at TRIUMF in Vancouver, Canada. The β-decay half-life of 26Na obtained from counting 1809-keV γ-ray photopeaks emitted by the daughter 26Mg was determined to be T1 / 2 = 1.07167 ± 0.00055 s following a 27 σ correction for detector pulse pile-up. This result is in excellent agreement with the result of a previous measurement that employed direct β counting and demonstrates the feasibility of high-precision β-decay half-life measurements through the use of high-purity germanium γ-ray detectors. The technique presented here, while motivated by superallowed-Fermi β decay studies, is general and can be used for all half-life determinations (e.g. α-, β-, X-ray, fission) in which a γ-ray photopeak is used to select the decays of a particular isotope. © 2007 Elsevier B.V. All rights reserved.

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)0.001
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.006

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 head0.038
GPT teacher head0.300
Teacher spread0.262 · 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

Citations0
Published2007
Admission routes1
Has abstractyes

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