Performance and Inter-Comparison of Novel 4π-Optimized Compton Imagers
Bibliographic record
Abstract
Compton imagers determine the location of a gamma-emitting radioactive source by tracking the interactions of a gamma ray within a position-sensitive spectroscopic gamma detector. The Compton imaging technology is naturally capable of delineating multiple and extended sources as well as localizing point sources. With their graphic output of source location probability contours overlaid on a photograph, Compton imagers are finding application in astronomy, medical imaging, environmental remediation, nuclear nonproliferation and national security. Over the past several years this group has been developing the Silicon photomultiplier-based Compton Telescope for Safety and Security (SCoTSS) imager using a traditional two-plane design with a forward "scatter" plane and a rear "absorber’ plane [1]. At the 2019 IEEE NSS MIC conference we described adapted SCoTSS designs optimized for 4π imaging including cubic, spherical and cruciform geometries and discussed their performance determined from GEANT4 simulations [2]. In this submission we present the results of studies performed on the realized instruments in a laboratory setting, quantifying and comparing their point source response and their imaging uniformity over large fractions the 4π incident angle space. These results, using real data, permit verification of the performance expectations for the various design principles of the detectors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".