DETECT2000: an improved Monte-Carlo simulator for the computer aided design of photon sensing devices
Bibliographic record
Abstract
We introduce a new version of DETECT. DETECT is a Monte-Carlo simulator developed for the Computer Aided Design (CAD) of optical photon sensing devices. The simulator generates individual emission photons in specified locations of a photon-emitting device and tracks their passage and interactions in active and passive components of the system. Extensive options are available in the simulator to model the geometry of the photon sensing device, to account for the time and wavelength distribution of emission photons, to track their interactions with surfaces, to account for their possible absorption and re-emission by a wave-shifting components and to model their detection by pixelated photomultipliers or photodiodes. DETECT2000 is a very significant upgrade of DETECT97, which has long been established in the nuclear medicine instrumentation community for its accuracy to model the performances of high resolution energy and position sensitive gamma-ray detectors. The 2000 version of DETECT offers an accelerated version of the simulator which has been redesigned in the object-oriented C++ language. New features such as the tracking of the time and wavelength history of individual optical photons have been added.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.009 | 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".