Investigation of BGO Coincidence Time Resolution with Deep Learning
Bibliographic record
Abstract
Significant advancements in coincidence time resolution (CTR) of BGO scintillators have been made in recent years, driven by the enhanced capability of new SiPMs to detect Cherenkov photons. However, BGO also emits slower scintillation light, causing time walk and degrading CTR when measured with leading edge discrimination (LED). This can be partially counteracted by using a second, higher threshold to perform time walk correction (TWC). Convolutional neural networks (CNNs) were also shown to provide enhanced timing estimation in other scintillators by training with digitized waveforms. The present study compares the CTR performance of LED, TWC, and CNN approaches in BGO scintillators read out by NUV-HD-MT SiPMs and high-frequency electronics. For BGO 2×2×3 mm<sup>3</sup> utilizing TWC produces a CTR of 129 ± 2 ps FWHM, whereas the CNN achieves 115 ± 2 ps, representing 18% and 26% improvements over LED, respectively. For BGO 2×2×20 mm3, both approaches produce comparable CTR (around 240 ps FWHM, ~15% improvement over LED), but the CNN shows superior tail suppression in the coincidence time distribution. The increased complexity associated with waveform digitization required for CNNs might be alleviated by adopting a simpler dual-threshold approach, which so far appears to recover the most crucial features of the signal for enhancing CTR in longer BGO crystals.
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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 | 0.001 |
| Open science | 0.000 | 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".