New best practices for efficiency fitting for accurate gamma-ray spectroscopy with semi-conductor detectors
Bibliographic record
Abstract
Gamma spectroscopy of high energy photons is a commonly applied technique in fundamental en applied research. In such studies one of the goals is to determine an a-priori unknown source activity or intensity of a transition. In order to do so, one needs to evaluate the efficiency response function. This process involves the measurement of count rates of lines emitted in the decay of calibration sources, followed by an empirical fitting procedure. Such calibration sources have known activity, yet the uncertainty on this activity and the positioning of the source are often the biggest contributions to the total error budget. While investigations of such an efficiency response function are often performed, reports in literature neglect the correlation between efficiencies determined from different lines. Consequently, the fitted efficiency will have both an unreliable central value and uncertainty. Furthermore, in such cases, the relative efficiency between two energies becomes unreliable as well. In this work, we start by showing some common issues that occur in the conventional fitting methods. This is followed by several suggestions for best practices with increasing levels of complexity: (1) Scaling the energy to a reference value in order to reduce correlation; (2) Using Bayesian methods to treat the activities as fit parameters; (3) Increasing the robustness by including hyperpriors on the activity uncertainties.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.011 |
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".