ginnocen/MITHIGAnalysis2024
Oct 2025 – Oct 2025
1 Month active
Languages Used
C++
Technical Skills
C++ programmingdata analysisdata visualizationstatistical modeling
decoudav
PROFILE
Decoudav
Developed a Particle Identification (PID) Analysis Framework for the ginnocen/MITHIGAnalysis2024 repository, focusing on enhancing particle ID workflows through refined fitting processes and improved histogram outputs. Leveraged C++ and data analysis techniques to tighten binning parameters, achieving greater consistency between experimental data and Monte Carlo simulations. The work included generating structured outputs in both PDF and ROOT file formats, supporting reproducible and efficient analysis. Repository hygiene was maintained by removing unnecessary files and introducing helper functions to streamline workflows. This feature-driven approach improved the reliability and reproducibility of PID analyses, enabling faster, data-driven insights for physics research and collaboration.
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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