Modeling of heavy ion collisions using radial basis function and generalized regression neural networks
Bibliographic record
Abstract
Artificial neural networks (ANNs) have been applied to heavy ion collisions. In the present work, the possibility of using ANN methods for modeling the multiplicity distributions, P(ns), of shower particles produced from p, d, 4He, 6Li, 7Li, 12C, 16O, and 24Mg interactions with light (CNO) as well as heavy (AgBr) emulsions at 4.5 A GeV/c was investigated. Two different ANN approaches, namely radial basis function neural network (RBFNN) and generalized regression neural network (GRNN), were employed to obtain a mathematical formula describing these collisions. The results from RBFNN and GRNN models showed good agreement with the experimental data. GRNN models have a better performance than the RBFNN models. This study showed that the RBFNN and GRNN models are capable of accurately predicting the P(ns) of shower particles in the training and testing phases.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| 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".