A Novel Algorithm to Reconstruct Events in a Water Cherenkov Detector
Bibliographic record
Abstract
We have developed a novel approach to reconstruct events detected by a water-based Cherenkov detector such as Super- and Hyper-Kamiokande using an innovative deep learning algorithm. The algorithm is based on a generative neural network whose parameters are obtained by minimizing a loss function. In the training process with simulated single-particle events, the generative neural network is given the particle identification (ID) or type, 3d-momentum (p), and 3d-vertex position (V) as the inputs for each training event. Then the network generates a Cherenkov event that is compared with the corresponding true simulated event. Once the training is done, for the given Cherenkov event the algorithm will provide the best estimate on ID, p, and V by minimizing the loss function between the given event and the generated event over ranges of input values of ID, p and V. The algorithm serves as a type of fast simulation for a water Cherenkov detector with a fewer number of assumptions than traditional reconstruction methods. We will show some of the algorithm's excellent performance in addition of the architecture and principle of the network.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".