A mathematical formalism for hyperspectral, multipoint plastic scintillation detectors
Bibliographic record
Abstract
The aim of this paper is to generalize and extend the mathematical formalism used with plastic scintillation detectors (PSDs). By doing so, we show the feasibility of multi-point PSD. The new formalism is based on the sole hypothesis that a PSD optical signal is a linear superposition of spectra. Two calibration scenarios were developed. Both involve solving a linear equation of the form Y = XB, but the process and input data depend on the information available on the detector system. Simulations were carried out to validate both scenarios and demonstrate the advantages of the new formalism. In this paper, we prove the following results. (1) Multi-point PSDs are feasible. Simulations have shown that six different spectra could be resolved accurately even in the presence of up to 10% Gaussian noise. (2) The new formalism leads to more precise PSD measurements. (3) By using the condition number of the measurement matrix, the ideal sets of calibration measurements can be identified. (4) By using principal component analysis it was possible to identify the best set of wavelength filters. We have shown through numerical simulations that multi-point detectors are feasible. This has potential for applications such as in vivo dose verification. Furthermore, our new formalism can be used to improve the robustness and ease of use of PSDs.
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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".