EMPHATICSoft/emphaticsoft
May 2025 – Jun 2025
2 Months active
Languages Used
CC++CMakeROOTFHiCL
Technical Skills
Algorithm DesignAlgorithm ImplementationBuild System ConfigurationC++C++ DevelopmentCode Refactoring
Manuel Dallolio
PROFILE
Manuel Dallolio
Manuel Del Tutto contributed to the EMPHATICSoft/emphaticsoft repository by developing and enhancing data processing modules for particle physics analysis. He implemented TRB3 digitization and improved Hough transform-based circle detection, introducing modular C++ code and robust data structures using ROOT for efficient event reconstruction. Manuel refactored ARICH reconstruction workflows, modularizing hit processing and clustering to support scalable analytics and reliable data persistence. He also built the GetDataML module, enabling machine learning data extraction and analysis of aerogel-region tracks with ARICH cluster association. His work demonstrated depth in C++ development, algorithm design, and build system configuration, resulting in maintainable, production-ready code.
Overall Statistics
Feature vs Bugs
80%Features
Repository Contributions
9Total
Bugs
1
Commits
9
Features
4
Lines of code
247,288
Activity Months2
Your Network
16 peopleSame Organization
@emphaticgpvm03.fnal.gov2
Work History
June 2025
2 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary for EMPHATICSoft/emphaticsoft: Implemented the GetDataML module for ML data extraction and ARICH analysis, establishing a data processing pathway for ML/ML-LL comparisons focused on aerogel-region tracks and ARICH cluster association. The implementation includes scaffolding for CMakeLists.txt, FHiCL configuration, and a C++ module to handle raw digits and simulation tracks, storing results in a ROOT TTree. Completed initial repository scaffolding and addressed a missing-file issue to enable build and testing.
2 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary for EMPHATICSoft/emphaticsoft: Implemented the GetDataML module for ML data extraction and ARICH analysis, establishing a data processing pathway for ML/ML-LL comparisons focused on aerogel-region tracks and ARICH cluster association. The implementation includes scaffolding for CMakeLists.txt, FHiCL configuration, and a C++ module to handle raw digits and simulation tracks, storing results in a ROOT TTree. Completed initial repository scaffolding and addressed a missing-file issue to enable build and testing.
June 2025
May 2025
7 Commits • 3 Features
May 1, 2025May 2025 performance summary for EMPHATICSoft/emphaticsoft: Key delivery across TRB3 digitization, Hough circle detection, and ARICH reconstruction with reliability improvements. Key achievements include: 1) TRB3 Digitization and data acquisition enhancements delivering central blimp integration and targeted code cleanups to boost robustness and data processing; 2) Hough transform circle detection improvements introducing TH2D-based implementation, accurate bin-center extraction for EdgePoints, and a new public GetCirclesCenters API; 3) ARICH reconstruction enhancements and modularization with refactoring for hit processing and clustering, modularization into MakeArichCluster and MakeRing, and updated data structures/service integration; 4) ARICH tracking data save robustness fix ensuring tracks are saved when ARICH information is absent. These changes improve data quality, processing reliability, and scalability for ARICH-based analytics. Technologies/skills demonstrated include C++/ROOT-based data processing, modular design, data structure refactoring, code quality improvements, and robust data persistence.
May 2025
7 Commits • 3 Features
May 1, 2025May 2025 performance summary for EMPHATICSoft/emphaticsoft: Key delivery across TRB3 digitization, Hough circle detection, and ARICH reconstruction with reliability improvements. Key achievements include: 1) TRB3 Digitization and data acquisition enhancements delivering central blimp integration and targeted code cleanups to boost robustness and data processing; 2) Hough transform circle detection improvements introducing TH2D-based implementation, accurate bin-center extraction for EdgePoints, and a new public GetCirclesCenters API; 3) ARICH reconstruction enhancements and modularization with refactoring for hit processing and clustering, modularization into MakeArichCluster and MakeRing, and updated data structures/service integration; 4) ARICH tracking data save robustness fix ensuring tracks are saved when ARICH information is absent. These changes improve data quality, processing reliability, and scalability for ARICH-based analytics. Technologies/skills demonstrated include C++/ROOT-based data processing, modular design, data structure refactoring, code quality improvements, and robust data persistence.
Activity
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Quality Metrics
Correctness82.2%
Maintainability84.4%
Architecture84.4%
Performance71.2%
AI Usage20.0%
Skills & Technologies
Programming Languages
CC++CMakeFHiCLROOT
Technical Skills
ART FrameworkAlgorithm DesignAlgorithm ImplementationBuild System ConfigurationC++C++ DevelopmentCode RefactoringData AnalysisData DigitizationData ExtractionData ReconstructionData StructuresDetector SimulationEvent ReconstructionGeant4
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