
Contributed to the LDMX-Software/ldmx-sw repository by developing and enhancing data processing, reconstruction, and analysis tools for particle physics workflows. Built features such as pass name filtering for PF cluster producers, improved input validation, and electron identification enhancements for PFTrack, focusing on robust configuration management and error handling. Leveraged C++, Python, and shell scripting to implement modules for testbeam data processing, log comparison, and raw data decoding, while maintaining code quality through refactoring and formatting. Enhanced data integrity, reproducibility, and analysis readiness by refining event reconstruction algorithms and streamlining command-line tooling, demonstrating depth in software engineering and data analysis.
January 2026 | LDMX Software (ldmx-sw) monthly summary Key deliverables: - Particle Flow Hit Association Enhancements (Pileup Hit Finding): Enhanced PF hit association, added pileup hit finding capabilities, and improved handling of indices for hits, clusters, and tracks. - Commit: a5b2a7325e45c85b7223504dfa2b7256c8ba3131 (PF hit association (#1856)) - Rogue Extract Script Enhancements (CLI Options and Parameter Handling): Expanded rogue-extract.py with new CLI options for output file specification and debugging, and refined handling of subsystem and contributor parameters. - Commit: 36f421eb2bbe13c9d9f885e1be93b962a9c8ee42 (Update rogue-extract.py (#1917)) Major bugs fixed: - No major bugs fixed reported for this period based on provided data. Overall impact and accomplishments: - Strengthened data quality and analysis readiness by improving PF-based event reconstruction in high pileup scenarios, enabling cleaner physics analyses. - Streamlined data extraction and reproducibility through enhanced CLI tooling and parameter handling for rogue-extract.py, reducing setup overhead and debugging time. Technologies/skills demonstrated: - Python scripting and command-line tooling (rogue-extract.py). - PF hit association algorithms and pileup handling within LDMX software stack. - Version control discipline and commit-driven development."
January 2026 | LDMX Software (ldmx-sw) monthly summary Key deliverables: - Particle Flow Hit Association Enhancements (Pileup Hit Finding): Enhanced PF hit association, added pileup hit finding capabilities, and improved handling of indices for hits, clusters, and tracks. - Commit: a5b2a7325e45c85b7223504dfa2b7256c8ba3131 (PF hit association (#1856)) - Rogue Extract Script Enhancements (CLI Options and Parameter Handling): Expanded rogue-extract.py with new CLI options for output file specification and debugging, and refined handling of subsystem and contributor parameters. - Commit: 36f421eb2bbe13c9d9f885e1be93b962a9c8ee42 (Update rogue-extract.py (#1917)) Major bugs fixed: - No major bugs fixed reported for this period based on provided data. Overall impact and accomplishments: - Strengthened data quality and analysis readiness by improving PF-based event reconstruction in high pileup scenarios, enabling cleaner physics analyses. - Streamlined data extraction and reproducibility through enhanced CLI tooling and parameter handling for rogue-extract.py, reducing setup overhead and debugging time. Technologies/skills demonstrated: - Python scripting and command-line tooling (rogue-extract.py). - PF hit association algorithms and pileup handling within LDMX software stack. - Version control discipline and commit-driven development."
December 2025: Delivered ESA TS Data Format Decoder for ZCCM Raw Data Processing in LDMX-Software/ldmx-sw. Added configuration scripts and stabilised peak/gain fits to enable more reliable data analysis and easier integration with ZCCM raw data pipelines. Result: improved data quality, faster onboarding of new data formats, and stronger pipeline reliability. No critical bugs fixed this month.
December 2025: Delivered ESA TS Data Format Decoder for ZCCM Raw Data Processing in LDMX-Software/ldmx-sw. Added configuration scripts and stabilised peak/gain fits to enable more reliable data analysis and easier integration with ZCCM raw data pipelines. Result: improved data quality, faster onboarding of new data formats, and stronger pipeline reliability. No critical bugs fixed this month.
2025-11 monthly summary for LDMX-Software/ldmx-sw. Key feature delivered: Improved Log Comparison and Logging Clarity. This update enhances log diff evaluation by ignoring timestamps, reducing false differences and adjusts the logging level for clearer diagnostics. Commit d540a21f694b45e81935745b4c5c986f790abd06 (Improve log diff in check.sh (#1871)).
2025-11 monthly summary for LDMX-Software/ldmx-sw. Key feature delivered: Improved Log Comparison and Logging Clarity. This update enhances log diff evaluation by ignoring timestamps, reducing false differences and adjusts the logging level for clearer diagnostics. Commit d540a21f694b45e81935745b4c5c986f790abd06 (Improve log diff in check.sh (#1871)).
July 2025 monthly summary for LDMX-Software/ldmx-sw focusing on testbeam data processing enhancements and data workflow improvements.
July 2025 monthly summary for LDMX-Software/ldmx-sw focusing on testbeam data processing enhancements and data workflow improvements.
June 2025 monthly summary for LDMX-Software/ldmx-sw: Implemented significant enhancements to the Particle Flow (PFlow) module by enabling it to consume existing non-tailored Ecal cluster collections and adding RMS calculations for Ecal clusters. The changes include input handling refactor, code cleanup, and adherence to formatting standards to improve maintainability and interoperability with existing data products.
June 2025 monthly summary for LDMX-Software/ldmx-sw: Implemented significant enhancements to the Particle Flow (PFlow) module by enabling it to consume existing non-tailored Ecal cluster collections and adding RMS calculations for Ecal clusters. The changes include input handling refactor, code cleanup, and adherence to formatting standards to improve maintainability and interoperability with existing data products.
May 2025 focused on advancing PFTrack electron reconstruction within overlayed signal environments. Delivered enhancements to electron identification and energy correction, plus configuration options to tune electron tracking thresholds, resulting in more accurate particle reconstruction for ECAL/HCAL clusters. Implemented a targeted fix to ensure PFTrack correctly identifies electrons in signals with overlays, improving reconstruction fidelity in busy events and reducing mis-identification risk.
May 2025 focused on advancing PFTrack electron reconstruction within overlayed signal environments. Delivered enhancements to electron identification and energy correction, plus configuration options to tune electron tracking thresholds, resulting in more accurate particle reconstruction for ECAL/HCAL clusters. Implemented a targeted fix to ensure PFTrack correctly identifies electrons in signals with overlays, improving reconstruction fidelity in busy events and reducing mis-identification risk.
April 2025 monthly summary for LDMX-Software/ldmx-sw. This period delivered a critical improvement to input validation and error reporting, enhancing data integrity and troubleshooting capabilities across the data ingestion path.
April 2025 monthly summary for LDMX-Software/ldmx-sw. This period delivered a critical improvement to input validation and error reporting, enhancing data integrity and troubleshooting capabilities across the data ingestion path.
March 2025 monthly summary: Delivered pass name filtering for PF cluster producers to enable targeted hit collection and improved reconstruction control for Ecal/Hcal. Implemented a new pass name parameter (hitPassName_) and configuration (hitPassName_), updated Ecal/Hcal hit data retrieval logic, and added corresponding headers to support pass-based filtering. This work enhances data organization, reproducibility, and analyst workflow with minimal downstream disruption. No major bugs fixed this period; focus remained on feature delivery and code hygiene across Python and C++ sources.
March 2025 monthly summary: Delivered pass name filtering for PF cluster producers to enable targeted hit collection and improved reconstruction control for Ecal/Hcal. Implemented a new pass name parameter (hitPassName_) and configuration (hitPassName_), updated Ecal/Hcal hit data retrieval logic, and added corresponding headers to support pass-based filtering. This work enhances data organization, reproducibility, and analyst workflow with minimal downstream disruption. No major bugs fixed this period; focus remained on feature delivery and code hygiene across Python and C++ sources.

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