bmad-sim/BeamTracking.jl
Nov 2024 – Jul 2025
5 Months active
Languages Used
Julia
Technical Skills
Beam Optics SimulationCode CleanupCode RefactoringDependency ManagementLinear AlgebraNumerical Methods
Sophia Yang
PROFILE
Sophia Yang
Worked on BeamTracking.jl, delivering core enhancements to linear beam optics simulation for particle accelerator modeling. Over five months, developed new data structures for particles and magnetic elements, implemented SBend and Combined element tracking, and refactored drift and quadrupole logic using Julia. Focused on robust numerical methods and linear algebra, the work improved simulation fidelity and maintainability by expanding test coverage, correcting edge-case calculations, and standardizing APIs. Addressed bugs in matrix operations and ensured reliable results through regression testing. These updates established a scalable, validated foundation for future nonlinear optics features and streamlined integration for downstream accelerator design workflows.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
19Total
Bugs
3
Commits
19
Features
6
Lines of code
1,437
Activity Months5
Your Network
148 peopleSame Organization
@cornell.edu138
Shared Repositories
10
Alex HeMember
CompatHelper JuliaMember
David SaganMember
Eiad HamwiMember
eiad-hamwiMember
Joseph DevlinMember
Matt SignorelliMember
Matthew George SignorelliMember
ndwangMember
Work History
July 2025
1 Commits • 1 Features
Jul 1, 2025Delivered API signature standardization for linear_dipole_matrices in LinearTracking.jl (bmad-sim/BeamTracking.jl) with reordered arguments and optional defaults, improving usability and reducing integration friction. Implemented a keyword-argument handling fix during the refactor (commit 'fix kwarg'). Updated tests to reflect the new API and maintain regression protection. The work contributes to stronger API stability, easier onboarding for users, and better maintainability.
1 Commits • 1 Features
Jul 1, 2025Delivered API signature standardization for linear_dipole_matrices in LinearTracking.jl (bmad-sim/BeamTracking.jl) with reordered arguments and optional defaults, improving usability and reducing integration friction. Implemented a keyword-argument handling fix during the refactor (commit 'fix kwarg'). Updated tests to reflect the new API and maintain regression protection. The work contributes to stronger API stability, easier onboarding for users, and better maintainability.
July 2025
June 2025
5 Commits • 2 Features
Jun 1, 2025June 2025 monthly summary for BeamTracking.jl (bmad-sim). The work focused on delivering a robust, maintainable core for linear tracking and expanding validation to ensure accurate magnet modeling across configurations. The updates align with business goals of reliable simulation results and faster feature delivery.
June 2025
5 Commits • 2 Features
Jun 1, 2025June 2025 monthly summary for BeamTracking.jl (bmad-sim). The work focused on delivering a robust, maintainable core for linear tracking and expanding validation to ensure accurate magnet modeling across configurations. The updates align with business goals of reliable simulation results and faster feature delivery.
May 2025
3 Commits • 1 Features
May 1, 2025In May 2025, strengthened reliability and precision of beamline simulations in BeamTracking.jl through expanded testing and a critical edge-case fix. Focused on delivering business value via robust validation, deterministic results, and improved maintainability for downstream accelerator modeling workflows.
3 Commits • 1 Features
May 1, 2025In May 2025, strengthened reliability and precision of beamline simulations in BeamTracking.jl through expanded testing and a critical edge-case fix. Focused on delivering business value via robust validation, deterministic results, and improved maintainability for downstream accelerator modeling workflows.
May 2025
April 2025
6 Commits • 1 Features
Apr 1, 2025April 2025 — BeamTracking.jl (bmad-sim/BeamTracking.jl) Key features delivered: - Implemented linear_combined_matrices for combined matrix computations of linear magnetic elements in the tracking module. - Refactored to linear_dipole_matrices with a robust test suite validating behavior across various configurations. Major bugs fixed: - Correct wyL and syc calculations for negative K1 in linear_dipole_matrices; updated tests with precise expectations. Other changes: - No-op commit recorded (build message) as part of the CI-driven workflow. Overall impact and accomplishments: - Increased accuracy and reliability of linear tracking calculations, enabling more faithful beam dynamics simulations. - Improved test coverage and maintainability through focused refactoring and regression tests. - Establishes a scalable foundation for future enhancements in matrix-based tracking. Technologies/skills demonstrated: - Julia-based development, advanced linear algebra for beam dynamics, test-driven development, regression testing, and proactive code quality improvements. Business value: - More trustworthy simulations reduce downstream risk, support better design decisions, and accelerate validation workflows for accelerator modeling.
April 2025
6 Commits • 1 Features
Apr 1, 2025April 2025 — BeamTracking.jl (bmad-sim/BeamTracking.jl) Key features delivered: - Implemented linear_combined_matrices for combined matrix computations of linear magnetic elements in the tracking module. - Refactored to linear_dipole_matrices with a robust test suite validating behavior across various configurations. Major bugs fixed: - Correct wyL and syc calculations for negative K1 in linear_dipole_matrices; updated tests with precise expectations. Other changes: - No-op commit recorded (build message) as part of the CI-driven workflow. Overall impact and accomplishments: - Increased accuracy and reliability of linear tracking calculations, enabling more faithful beam dynamics simulations. - Improved test coverage and maintainability through focused refactoring and regression tests. - Establishes a scalable foundation for future enhancements in matrix-based tracking. Technologies/skills demonstrated: - Julia-based development, advanced linear algebra for beam dynamics, test-driven development, regression testing, and proactive code quality improvements. Business value: - More trustworthy simulations reduce downstream risk, support better design decisions, and accelerate validation workflows for accelerator modeling.
November 2024
4 Commits • 1 Features
Nov 1, 2024Month: 2024-11. Summary: Delivered a consolidated upgrade to the linear beam optics module in BeamTracking.jl. Implemented new data structures for particles and magnetic elements, added SBend and Combined element tracking, and refactored Drift and Quadrupole tracking with sinc/sinhc helpers. Corrected sinc usage to sincu, cleaned up code, and expanded tests. The changes improve simulation fidelity, reliability, and maintainability, setting the stage for future nonlinear optics work and broader accelerator support.
4 Commits • 1 Features
Nov 1, 2024Month: 2024-11. Summary: Delivered a consolidated upgrade to the linear beam optics module in BeamTracking.jl. Implemented new data structures for particles and magnetic elements, added SBend and Combined element tracking, and refactored Drift and Quadrupole tracking with sinc/sinhc helpers. Corrected sinc usage to sincu, cleaned up code, and expanded tests. The changes improve simulation fidelity, reliability, and maintainability, setting the stage for future nonlinear optics work and broader accelerator support.
November 2024
Activity
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Quality Metrics
Correctness86.8%
Maintainability81.6%
Architecture79.0%
Performance75.8%
AI Usage21.0%
Skills & Technologies
Programming Languages
Julia
Technical Skills
Beam DynamicsBeam OpticsBeam Optics SimulationBeam PhysicsCode CleanupCode RefactoringDebuggingDependency ManagementLinear AlgebraMatrix OperationsNumerical AnalysisNumerical MethodsNumerical SimulationParticle Accelerator DesignParticle Accelerator Physics
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