DUNE/sandreco
Mar 2025 – Apr 2025
2 Months active
Languages Used
C++C
Technical Skills
Build SystemsC++Kalman FilterParticle Physics ReconstructionSoftware ArchitectureSoftware Development
fbattist
PROFILE
Fbattist
Federico Battisti developed and enhanced seed-based Kalman filter initialization and track reconstruction features in the DUNE/sandreco repository over a two-month period. He implemented Monte Carlo–informed seeding, covariance smearing, and synthetic helix generation to improve the robustness and accuracy of state vector estimation. Using C++ and the ROOT framework, Federico refactored seeding pathways, initialization methods, and noise calculations, while integrating comprehensive logging of Monte Carlo and seed states for improved traceability. His work included updating tests and validation procedures, resulting in more maintainable and reliable reconstruction algorithms that address the challenges of particle physics simulation and data analysis.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
7Total
Bugs
0
Commits
7
Features
3
Lines of code
1,478
Activity Months2
Your Network
20 people
Work History
April 2025
5 Commits • 2 Features
Apr 1, 2025April 2025 monthly achievements for DUNE/sandreco: Implemented and hardened seed-based Kalman Filter seeding for track reconstruction, enabling Monte Carlo–informed seed initialization and smeared covariance for more robust seed states; introduced synthetic helix generation and comprehensive logging of MC/seed states and covariances to ROOT trees; refactored key seeding and trajectory generation components to improve robustness along the Z-axis and testing coverage; updated tests and validated via helix pull tests. These changes improve track reconstruction accuracy, traceability against MC, and maintainability.
5 Commits • 2 Features
Apr 1, 2025April 2025 monthly achievements for DUNE/sandreco: Implemented and hardened seed-based Kalman Filter seeding for track reconstruction, enabling Monte Carlo–informed seed initialization and smeared covariance for more robust seed states; introduced synthetic helix generation and comprehensive logging of MC/seed states and covariances to ROOT trees; refactored key seeding and trajectory generation components to improve robustness along the Z-axis and testing coverage; updated tests and validated via helix pull tests. These changes improve track reconstruction accuracy, traceability against MC, and maintainability.
April 2025
March 2025
2 Commits • 1 Features
Mar 1, 2025Monthly summary for 2025-03 focused on seed-based Kalman filter initialization and seed-based track reconstruction enhancements in DUNE/sandreco. Achievements include building and integrating a TestSeed executable, enabling seed-based initialization in the SANDKalmanFilter manager, and propagating without energy loss. Reworked seeding pathways, refactored initialization methods, and adjusted noise calculations and test file handling to improve seed-based track reconstruction. Two commits contributed to the feature baseline and reliability: f5ca97cd9f1295112045dbebc3d859054da5aa38 (Basic infrastructure definition) and 77108373866a6bc5f3437e04e63c2967ef477803 (Made project compile and enabled propagation without energy loss).
March 2025
2 Commits • 1 Features
Mar 1, 2025Monthly summary for 2025-03 focused on seed-based Kalman filter initialization and seed-based track reconstruction enhancements in DUNE/sandreco. Achievements include building and integrating a TestSeed executable, enabling seed-based initialization in the SANDKalmanFilter manager, and propagating without energy loss. Reworked seeding pathways, refactored initialization methods, and adjusted noise calculations and test file handling to improve seed-based track reconstruction. Two commits contributed to the feature baseline and reliability: f5ca97cd9f1295112045dbebc3d859054da5aa38 (Basic infrastructure definition) and 77108373866a6bc5f3437e04e63c2967ef477803 (Made project compile and enabled propagation without energy loss).
Activity
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Quality Metrics
Correctness82.8%
Maintainability80.0%
Architecture80.0%
Performance67.2%
AI Usage20.0%
Skills & Technologies
Programming Languages
CC++
Technical Skills
Algorithm ImplementationBuild SystemsC++C++ DevelopmentData AnalysisData StructuresKalman FilterLinear AlgebraParticle PhysicsParticle Physics ReconstructionParticle Physics SimulationPhysics SimulationROOT FrameworkScientific ComputingSoftware Architecture
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