DUNE/larnd-sim
Dec 2024 – Jun 2025
3 Months active
Languages Used
C++Python
Technical Skills
Data AnalysisData ProcessingData SimulationGarfield++PhysicsPhysics Simulation
Jiangmei Yang
PROFILE
Jiangmei Yang
Jiangyang Yang contributed to the DUNE/larnd-sim repository by developing and integrating data-driven simulation features focused on pixel pad response modeling. Over three months, Jiangyang implemented Python scripts and C++ workflows to simulate Field-Structured Drift responses with configurable parameters, enhancing the flexibility and reproducibility of detector simulations. He restructured response data formats to improve traceability and incorporated new binary and pre-computed datasets, such as 2mm and 3.72mm pixel pad responses, directly into the simulation pipeline. This work improved simulation fidelity, streamlined onboarding for researchers, and established a robust foundation for future parameter studies and validation against experimental data.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
5Total
Bugs
0
Commits
5
Features
3
Lines of code
298
Activity Months3
Your Network
29 people
Work History
June 2025
1 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary for DUNE/larnd-sim: Focused on integrating a dataset-driven 2mm pixel pad response into the simulation pipeline, improving accuracy and enabling streamlined analyses for 2mm pad configurations.
1 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary for DUNE/larnd-sim: Focused on integrating a dataset-driven 2mm pixel pad response into the simulation pipeline, improving accuracy and enabling streamlined analyses for 2mm pad configurations.
June 2025
March 2025
3 Commits • 1 Features
Mar 1, 2025Month: 2025-03 — Key features and accomplishments for DUNE/larnd-sim centered on FSD Response Simulation Tool and data-structure enhancements. Implemented a Python script to simulate Field-Structured Drift (FSD) response with configurable drift length and time_tick. Updated drift_length parameters across configurations and restructured response data to include explicit drift_length and time_tick fields, with the original response data nested for traceability. No major bugs fixed this month. Overall impact: improved modeling fidelity and reproducibility, enabling configurable experiments and easier downstream analyses. Technologies/skills demonstrated: Python scripting, data modeling, configuration-driven development, version control.
March 2025
3 Commits • 1 Features
Mar 1, 2025Month: 2025-03 — Key features and accomplishments for DUNE/larnd-sim centered on FSD Response Simulation Tool and data-structure enhancements. Implemented a Python script to simulate Field-Structured Drift (FSD) response with configurable drift length and time_tick. Updated drift_length parameters across configurations and restructured response data to include explicit drift_length and time_tick fields, with the original response data nested for traceability. No major bugs fixed this month. Overall impact: improved modeling fidelity and reproducibility, enabling configurable experiments and easier downstream analyses. Technologies/skills demonstrated: Python scripting, data modeling, configuration-driven development, version control.
December 2024
1 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for DUNE/larnd-sim: Focused on expanding dataset support by integrating new FsD v2d response data assets. No code changes were required; added binary data files for a 3.72 mm pitch pixel and 50 ns time tick, enabling immediate use of the latest FsD v2d dataset in simulations and validation. This work reduces integration friction, enhances data fidelity, and accelerates research workflows.
1 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for DUNE/larnd-sim: Focused on expanding dataset support by integrating new FsD v2d response data assets. No code changes were required; added binary data files for a 3.72 mm pitch pixel and 50 ns time tick, enabling immediate use of the latest FsD v2d dataset in simulations and validation. This work reduces integration friction, enhances data fidelity, and accelerates research workflows.
December 2024
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Quality Metrics
Correctness92.0%
Maintainability92.0%
Architecture92.0%
Performance84.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++Python
Technical Skills
Data AnalysisData ProcessingData SimulationGarfield++PhysicsPhysics SimulationSimulation
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