Geant4 electromagnetic physics for Run3 and Phase2 LHC
Bibliographic record
Abstract
For the new Geant4 series 11.X, the electromagnetic (EM) physics sub-libraries were revised and reorganized in view of requirements for simulation of Phase-2 LHC experiments. EM physics simulation takes a significant fraction of the available CPU during massive production of Monte Carlo events for LHC experiments. We present the recent evolution of Geant4 EM sublibraries for the simulation of gamma, electron, and positron transport. Updates of other components of EM physics are also discussed. These developments are included in the new Geant4 version 11.1 (December 2022). The most important modifications concern the reorganization of the initialization of EM physics and the introduction of alternative tracking software. These modifications affect the CPU efficiency of any simulation, and CPU savings depend on geometry and physics configuration for the concrete experimental setup. We will discuss several methods: gamma general process, Woodcock tracking, transportation with multiple scattering process, alternative tracking manager, and the new G4HepEm library. These developments provide a basis for the implementation of EM particle transport on co-processors and GPU. We also will present very recent updates in physics processes and in configuration of EM physics.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.049 | 0.022 |
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".