ginnocen/MITHIGAnalysis2024
Oct 2025 – Oct 2025
1 Month active
Languages Used
C++
Technical Skills
C++ programmingdata analysisdata visualizationstatistical modeling
decoudav
PROFILE
Decoudav
During October 2025, David Decoud developed a Particle Identification (PID) Analysis Framework for the ginnocen/MITHIGAnalysis2024 repository, focusing on improving particle ID workflows in physics data analysis. He introduced a dedicated PID analysis folder, refined fitting procedures, and enhanced histogram outputs to increase consistency between experimental data and Monte Carlo simulations. Using C++ and Python, David tightened binning parameters and implemented helper functions to support reproducible analysis, while generating structured outputs in PDF and ROOT formats. His work emphasized repository hygiene by removing unnecessary files, resulting in a more reliable, efficient, and maintainable analysis pipeline for statistical modeling and data visualization.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
824
Activity Months1
Your Network
5 peopleWork History
October 2025
1 Commits • 1 Features
Oct 1, 2025October 2025: Delivered the Particle Identification (PID) Analysis Framework in ginnocen/MITHIGAnalysis2024, introducing a new PID Analysis Folder with refined fitting and histogram outputs to improve particle ID workflows. Tightened binning to p_bins = 0.05 and improved consistency between data and Monte Carlo fits; cleaned the repo by removing DS_Store clutter and added structured outputs (PDFs/ROOT files) containing histograms and fit results. Added helper functions to support reproducible analysis and streamlined workflows. No major bugs fixed this month; primary focus on feature delivery and repository hygiene. Overall impact: higher analysis reliability, reproducibility, and efficiency for PID analyses, enabling faster, data-driven physics insights. Technologies demonstrated: Python-based analysis pipelines, histogramming, ROOT I/O, data/MC validation, and collaborative development.
1 Commits • 1 Features
Oct 1, 2025October 2025: Delivered the Particle Identification (PID) Analysis Framework in ginnocen/MITHIGAnalysis2024, introducing a new PID Analysis Folder with refined fitting and histogram outputs to improve particle ID workflows. Tightened binning to p_bins = 0.05 and improved consistency between data and Monte Carlo fits; cleaned the repo by removing DS_Store clutter and added structured outputs (PDFs/ROOT files) containing histograms and fit results. Added helper functions to support reproducible analysis and streamlined workflows. No major bugs fixed this month; primary focus on feature delivery and repository hygiene. Overall impact: higher analysis reliability, reproducibility, and efficiency for PID analyses, enabling faster, data-driven physics insights. Technologies demonstrated: Python-based analysis pipelines, histogramming, ROOT I/O, data/MC validation, and collaborative development.
October 2025
Activity
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Quality Metrics
Correctness100.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++
Technical Skills
C++ programmingdata analysisdata visualizationstatistical modeling
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