LDMX-Software/ldmx-sw
Jan 2025 – Aug 2025
6 Months active
Languages Used
C++Pythonc++pythonCMakeJavaScript
Technical Skills
C++Configuration ManagementData ProcessingDetector SimulationMachine LearningMachine Learning Integration
tylerhoroho-UVA
PROFILE
Tylerhoroho-uva
Thomas Hennessey developed and maintained advanced event processing and machine learning workflows for the LDMX-Software/ldmx-sw repository, focusing on HCAL visible veto and analysis pipelines. He integrated Python-based XGBoost models with C++ core processing, enabling end-to-end training, inference, and validation within the detector simulation framework. His work included modularizing BDT training data generation, refactoring code for maintainability, and enhancing data access and analysis fidelity. By improving configuration management, code organization, and testing infrastructure, Thomas addressed both performance and reliability, delivering robust solutions for signal/background discrimination and physics analysis. His contributions demonstrated depth in C++, Python, and scientific computing.
Overall Statistics
Feature vs Bugs
81%Features
Repository Contributions
39Total
Bugs
3
Commits
39
Features
13
Lines of code
3,768
Activity Months6
Your Network
51 people
Work History
August 2025
12 Commits • 2 Features
Aug 1, 2025Month 2025-08 focused on maintainability and validation fidelity in LDMX-Software/ldmx-sw. Delivered code quality cleanup and module reorganization for Hcal/Visibles, along with validation enhancements for Visibles BDT analysis. No critical bugs closed this month; the work reduces technical debt and strengthens future deployability and test coverage.
12 Commits • 2 Features
Aug 1, 2025Month 2025-08 focused on maintainability and validation fidelity in LDMX-Software/ldmx-sw. Delivered code quality cleanup and module reorganization for Hcal/Visibles, along with validation enhancements for Visibles BDT analysis. No critical bugs closed this month; the work reduces technical debt and strengthens future deployability and test coverage.
August 2025
July 2025
8 Commits • 2 Features
Jul 1, 2025Month: 2025-07. This period delivered key features and bug fixes across the LDMX-Software/ldmx-sw repository, driving robust analysis capabilities and improved veto performance. Major outcomes include ROC analysis enablement for cutflow, targeted numerical stability fixes in VisiblesCutflow and HCAL analysis, and a visible veto ML model upgrade with versioned assets and ONNX alignment. These changes collectively enhance measurement fidelity, reduce risk in data processing, and demonstrate strong capability in algorithm development, numerical correctness, and model deployment.
July 2025
8 Commits • 2 Features
Jul 1, 2025Month: 2025-07. This period delivered key features and bug fixes across the LDMX-Software/ldmx-sw repository, driving robust analysis capabilities and improved veto performance. Major outcomes include ROC analysis enablement for cutflow, targeted numerical stability fixes in VisiblesCutflow and HCAL analysis, and a visible veto ML model upgrade with versioned assets and ONNX alignment. These changes collectively enhance measurement fidelity, reduce risk in data processing, and demonstrate strong capability in algorithm development, numerical correctness, and model deployment.
June 2025
5 Commits • 4 Features
Jun 1, 2025June 2025: Delivered key features and stability improvements in LDMX-Software/ldmx-sw focused on beam analysis capabilities, data access, and testing/observability. These efforts improved analysis fidelity, debugging efficiency, and overall production reliability, aligning with the team's objective to accelerate physics results and ensure robust data workflows.
5 Commits • 4 Features
Jun 1, 2025June 2025: Delivered key features and stability improvements in LDMX-Software/ldmx-sw focused on beam analysis capabilities, data access, and testing/observability. These efforts improved analysis fidelity, debugging efficiency, and overall production reliability, aligning with the team's objective to accelerate physics results and ensure robust data workflows.
June 2025
May 2025
7 Commits • 2 Features
May 1, 2025May 2025 | Repository: LDMX-Software/ldmx-sw Key features delivered - HCAL Visible Veto: training architecture and integration. Introduced VisiblesFeatureProducer for BDT training data generation and refactored VisiblesVetoProcessor to delegate training. Added VisiblesVetoResult to the build system; BDT threshold configured. - HCAL Visible event filtering and analysis enhancements. Added recoil-based event cut, additional histograms, and a new VisiblesCutflow class to analyze visible particles and apply cuts/BDT scores for improved signal/background discrimination. Major bugs fixed - Bug: Fix compilation issues in VisiblesVetoProcessor. Resolved function name mismatches, include directives, and parameter handling to enable compilation and integration into the event processing framework. Overall impact and accomplishments - Stabilized and extended the HCAL Visible Veto pipeline, enabling end-to-end training data generation and evaluation within the existing event processing framework. - Improved signal/background discrimination through targeted analysis enhancements and a dedicated Visibles analysis workflow, setting the stage for performance gains in downstream physics results. Technologies/skills demonstrated - Codebase refactoring and modularization (training logic moved to VisiblesFeatureProducer) - Build-system integration (CMake) with new VisiblesVetoResult and training components - BDT training data generation and threshold tuning - Recoil-based cuts, histogram instrumentation, and VisiblesCutflow analytics
May 2025
7 Commits • 2 Features
May 1, 2025May 2025 | Repository: LDMX-Software/ldmx-sw Key features delivered - HCAL Visible Veto: training architecture and integration. Introduced VisiblesFeatureProducer for BDT training data generation and refactored VisiblesVetoProcessor to delegate training. Added VisiblesVetoResult to the build system; BDT threshold configured. - HCAL Visible event filtering and analysis enhancements. Added recoil-based event cut, additional histograms, and a new VisiblesCutflow class to analyze visible particles and apply cuts/BDT scores for improved signal/background discrimination. Major bugs fixed - Bug: Fix compilation issues in VisiblesVetoProcessor. Resolved function name mismatches, include directives, and parameter handling to enable compilation and integration into the event processing framework. Overall impact and accomplishments - Stabilized and extended the HCAL Visible Veto pipeline, enabling end-to-end training data generation and evaluation within the existing event processing framework. - Improved signal/background discrimination through targeted analysis enhancements and a dedicated Visibles analysis workflow, setting the stage for performance gains in downstream physics results. Technologies/skills demonstrated - Codebase refactoring and modularization (training logic moved to VisiblesFeatureProducer) - Build-system integration (CMake) with new VisiblesVetoResult and training components - BDT training data generation and threshold tuning - Recoil-based cuts, histogram instrumentation, and VisiblesCutflow analytics
February 2025
3 Commits • 2 Features
Feb 1, 2025February 2025 monthly summary for LDMX-Software/ldmx-sw: Focused on delivering foundational enhancements to HCAL event processing and establishing a machine learning-based event classifier pipeline. No major bug fixes were documented for this period; minor workflow tweaks were implemented to streamline HCAL processing.
3 Commits • 2 Features
Feb 1, 2025February 2025 monthly summary for LDMX-Software/ldmx-sw: Focused on delivering foundational enhancements to HCAL event processing and establishing a machine learning-based event classifier pipeline. No major bug fixes were documented for this period; minor workflow tweaks were implemented to streamline HCAL processing.
February 2025
January 2025
4 Commits • 1 Features
Jan 1, 2025January 2025 monthly summary for LDMX-Software/ldmx-sw focusing on bug fixes and ML integration in VisiblesVetoProcessor. Key themes: accuracy improvements, data-driven veto via BDTs, and maintainable configuration-driven workflows.
January 2025
4 Commits • 1 Features
Jan 1, 2025January 2025 monthly summary for LDMX-Software/ldmx-sw focusing on bug fixes and ML integration in VisiblesVetoProcessor. Key themes: accuracy improvements, data-driven veto via BDTs, and maintainable configuration-driven workflows.
Activity
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Quality Metrics
Correctness84.6%
Maintainability87.2%
Architecture81.0%
Performance72.8%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++CMakeJavaScriptPythonc++python
Technical Skills
Build System ConfigurationC++C++ DevelopmentCode OrganizationCode RefactoringConfigurationConfiguration ManagementData AnalysisData ProcessingData ValidationDetector SimulationDetector Simulation AnalysisEvent ProcessingMachine LearningMachine Learning Integration
Repositories Contributed To
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