SegmentLinking/cmssw
Nov 2024 – Nov 2024
1 Month active
Languages Used
C++
Technical Skills
Algorithm ImprovementDebuggingNumerical StabilityPerformance OptimizationPhysics SimulationSoftware Development
leonardo
PROFILE
Leonardo
Worked on enhancing the robustness of track fitting in the SegmentLinking/cmssw repository by delivering a more reliable Kalman filter propagation workflow. The approach involved correcting the propagation function call, introducing safeguards such as isFinite checks and value range guards, and implementing fallback mechanisms for non-finite values to handle edge cases without impacting performance. Adjustments to loop bounds ensured accurate processed counts, reducing failure modes and improving the stability of high-pT track analysis. This work leveraged C++ and focused on algorithm improvement, numerical stability, and performance optimization, laying a foundation for more stable and reliable downstream physics simulations.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
1
Lines of code
15
Activity Months1
Your Network
418 peopleSame Organization
@cern.ch336
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82
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Work History
November 2024
2 Commits • 1 Features
Nov 1, 2024This month (2024-11) focused on strengthening track-fitting robustness in SegmentLinking/cmssw by delivering a hardened Kalman filter propagation workflow and safeguards for edge-case values, with a tight integration into the repository's high-pT tracking path. Key improvements include correcting the propagation call, adding non-finite value fallbacks, and adjusting loop bounds to ensure accurate processed counts. These changes reduce failure modes, improve physics analysis reliability, and set the stage for further optimizations.
2 Commits • 1 Features
Nov 1, 2024This month (2024-11) focused on strengthening track-fitting robustness in SegmentLinking/cmssw by delivering a hardened Kalman filter propagation workflow and safeguards for edge-case values, with a tight integration into the repository's high-pT tracking path. Key improvements include correcting the propagation call, adding non-finite value fallbacks, and adjusting loop bounds to ensure accurate processed counts. These changes reduce failure modes, improve physics analysis reliability, and set the stage for further optimizations.
November 2024
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture60.0%
Performance60.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++
Technical Skills
Algorithm ImprovementDebuggingNumerical StabilityPerformance OptimizationPhysics SimulationSoftware Development
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