A new surface parameterization for modeling thin layers of reflector material in the DETECT2000 optical modeling program
Bibliographic record
Abstract
A new surface parameterization has been implemented in DETECT2000 that allows for the efficient representation of a thin film of reflector material with non-negligible transmission. Material is usually modeled in DETECT by specifying the optical attenuation and scatter lengths. For a material such as Teflon, commonly used as a reflector material for nuclear medicine scintillators, the scatter length can be very short. Modeling the propagation of photons in a material with a very short scattering length can be computationally intensive as there can be many scatter interactions before the photon either exits the material or is absorbed. In a detector design that makes generous use of Teflon, most of the computations can be spent simply transporting photons in the Teflon. To address this issue, a parameterized surface has been implemented which is specified not by the bulk optical scatter and attenuation lengths, but rather by a reflection and transmission coefficient. This allows computationally efficient modeling of thin film reflectors where surface properties and transmission are relevant.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".