DUNE/larnd-sim
Apr 2025 – Jun 2025
2 Months active
Languages Used
Python
Technical Skills
Data AnalysisData ProcessingData SimulationPerformance OptimizationScientific ComputingSimulation
Will Foreman
PROFILE
Will Foreman
William Foreman enhanced the DUNE/larnd-sim repository by developing a delayed-segment filtering and reporting mechanism for induction simulations, using Python and scientific computing techniques. He implemented logic to drop segments with excessively delayed timestamps, reducing memory spikes and improving simulation stability. Foreman also added detailed logging and warnings to increase observability and support performance tuning. In a subsequent update, he refined the quenching process by filtering out gamma and neutron segments, ensuring only relevant particle interactions contributed to charge and light simulations. His work demonstrated depth in data analysis, data processing, and particle physics, resulting in more accurate and maintainable simulations.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
4Total
Bugs
1
Commits
4
Features
1
Lines of code
71
Activity Months2
Your Network
16 people
Work History
June 2025
1 Commits
Jun 1, 2025June 2025: Delivered a targeted fix in DUNE/larnd-sim to improve quenching accuracy by filtering out ionization electrons and scintillation light for gamma (PDG 22) and neutron (PDG 2112) segments. The change ensures that only relevant particle interactions contribute to charge and light simulations, reducing spurious signals and aligning the detector response with physical expectations.
1 Commits
Jun 1, 2025June 2025: Delivered a targeted fix in DUNE/larnd-sim to improve quenching accuracy by filtering out ionization electrons and scintillation light for gamma (PDG 22) and neutron (PDG 2112) segments. The change ensures that only relevant particle interactions contribute to charge and light simulations, reducing spurious signals and aligning the detector response with physical expectations.
June 2025
April 2025
3 Commits • 1 Features
Apr 1, 2025April 2025: Delivered a robust delayed-segment filtering and reporting mechanism in the DUNE/larnd-sim induction simulation, addressing memory stability and enhancing observability. Implemented a filter to drop segments with excessively delayed timestamps during induction calculations, added warnings for dropped segments, and logged the number and specific t0 values of rejected segments. These changes reduce memory spikes in high-delay scenarios, improve reproducibility and traceability, and provide clearer diagnostics for performance tuning.
April 2025
3 Commits • 1 Features
Apr 1, 2025April 2025: Delivered a robust delayed-segment filtering and reporting mechanism in the DUNE/larnd-sim induction simulation, addressing memory stability and enhancing observability. Implemented a filter to drop segments with excessively delayed timestamps during induction calculations, added warnings for dropped segments, and logged the number and specific t0 values of rejected segments. These changes reduce memory spikes in high-delay scenarios, improve reproducibility and traceability, and provide clearer diagnostics for performance tuning.
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture65.0%
Performance65.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Data AnalysisData ProcessingData SimulationParticle PhysicsPerformance OptimizationPhysics SimulationScientific ComputingSimulation
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