A detector with high detection efficiency in 4- and 5-photon-positronium annihilations
Bibliographic record
Abstract
We first measured 4- and 5-photon annihilations in positronium using a multiphoton spectrometer (UNI), which consists of 32 NaI(Tl) scintillators with lead shields, each being located on a surface of an icosidodecahedron. The front face of each scintillator is placed at a distance of L = 262 mm from the center of the UNI. With this setup, the detection efficiency of 4- and 5-photon-annihilation processes are too small to get a sufficient number of events to analyze the physics. To obtain a larger detection efficiency, we must set the NaI(Tl) scintillators closer to the target. The original principle in designing the UNI was to suppress backgrounds (BG) and make them as low as possible allowing modest efficiencies for 4- and 5-photon-annihilation events, i.e., to get the highest signal-to-noise ratio (S/N). The new concept is to get the highest S/σ where σ is an error of one standard deviation of the signal including BG effects. A higher S/σ means a larger number of events with smaller BG taking into account a statistical effect. The detection efficiencies with BG effects are studied with respect to L using a detector simulator based on the EGS4 code in which 2- to 5-photon-annihilation events are generated by quantum-electrodynamic processes based on GRACE and BASES/SPRING codes. As a result, the detection efficiency and S/σ of 5-photon annihilations at L = 136 mm are 529 and 17 times larger than those at L = 262 mm, respectively. PACS Nos.: 36.10Dr, 12.20Fv, 13.10+q
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".