PUC Program: the pulse pile up correction for X-ray and gamma ray spectrometry
Bibliographic record
Abstract
Pile up and dead time are two important corrections in the analysis of X-ray and gamma ray spectra. The most important of these is pile up correction because these peaks do not really exist in the spectra; they only seem to exist. For this reason, these peaks affect both the qualitative and quantitative accuracy of the analysis. In addition, the pile up pulses forming the pile up peaks increase the background count in the spectrum. Companies that produce X-ray or gamma ray detector systems design pile up reject circuits and integrate them into detector systems to prevent these pulses. These circuits have time limitations because they are made up of electronic devices. For this reason, the pile up problem cannot be solved completely in these circuits. Therefore, mathematical models based on a statistical approach are needed. Such a model was developed in this study. A computer program based on this model was developed. This developed program has been applied to X-ray and gamma ray spectra. It has been shown that this model provides about 2% correction in the main peak regions and significantly reduces background counts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".