
Alexander Wilkinson contributed to the DUNE/dunereco repository by enhancing PFParticle classification and identification workflows. He refactored C++ code to integrate lar_pandora::LArPandoraHelper, correcting track and shower misclassifications and improving particle ID accuracy. Alexander also introduced association checks for PFParticles, clarifying the distinction between object existence and identification, and updated API naming for greater clarity. His work included adding Doxygen documentation and improving code maintainability through careful software refactoring. These changes resulted in more reliable particle identification and streamlined downstream analyses, demonstrating depth in C++ development, code documentation, and data analysis within the context of particle physics software.
February 2026 monthly summary for DUNE/dunereco focused on strengthening PFParticle handling to improve PID reliability and API clarity, complemented by documentation and code quality improvements.
February 2026 monthly summary for DUNE/dunereco focused on strengthening PFParticle handling to improve PID reliability and API clarity, complemented by documentation and code quality improvements.
Sep 2025 monthly focus on stabilizing PFParticle classification in DUNE/dunereco. Implemented a bug fix by refactoring PFParticleUtils isTrack/isShower to use lar_pandora::LArPandoraHelper, correcting track/shower detection and improving particle ID accuracy. The change is recorded in commit 5a010acb5f31bb23341f3a75f79708e2cdb0a55a. Results: more reliable PFParticle classifications and improved downstream physics selections. Demonstrated skills: C++ refactoring, integration with LAr Pandora tooling, code quality and regression safety.
Sep 2025 monthly focus on stabilizing PFParticle classification in DUNE/dunereco. Implemented a bug fix by refactoring PFParticleUtils isTrack/isShower to use lar_pandora::LArPandoraHelper, correcting track/shower detection and improving particle ID accuracy. The change is recorded in commit 5a010acb5f31bb23341f3a75f79708e2cdb0a55a. Results: more reliable PFParticle classifications and improved downstream physics selections. Demonstrated skills: C++ refactoring, integration with LAr Pandora tooling, code quality and regression safety.

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