blackSwanCS/Higgs_collaboration_B
Jun 2025 – Jun 2025
1 Month active
Languages Used
Python
Technical Skills
Data AnalysisData VisualizationMachine LearningPlottingPythonScientific Computing
GHERBI Samuel
PROFILE
Gherbi Samuel
Samuel Gherbi developed two core features for the blackSwanCS/Higgs_collaboration_B repository, focusing on Transition Edge Sensor (TES) analysis. He unified systematic uncertainty handling across the full training set prior to signal and background splitting, which improved TES fitting accuracy and stabilized weight extraction. Using Python and scientific computing techniques, he also enhanced the plotting and organization of TES uncertainty outputs by separating background and signal visualizations, standardizing bin indexing, and refining output naming conventions. These changes increased modeling robustness, improved reproducibility, and streamlined quality assurance, demonstrating depth in data analysis, data visualization, and machine learning within a collaborative research environment.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
5Total
Bugs
0
Commits
5
Features
2
Lines of code
477
Activity Months1
Your Network
41 people
Work History
June 2025
5 Commits • 2 Features
Jun 1, 2025June 2025 monthly summary for blackSwanCS/Higgs_collaboration_B: Two major TES (Transition Edge Sensor) analysis features were delivered, delivering measurable improvements in fit quality, uncertainty handling, and output QA, with a clear business value in reliability and downstream readiness. Key features delivered: - TES Fitter Accuracy and Systematics Handling: Unified application of systematics to the full training set before the signal/background split, resulting in improved TES fitting accuracy and more robust weight extraction/prediction on the modified dataset. Notable bug fixes include addressing a shift issue (commits referenced as shift problem try and résolution du shift) to stabilize fit behavior. - TES Uncertainties Plotting and Output Organization: Enhanced plotting and output organization for TES uncertainties by separating background and signal plots, standardizing bin indexing and labels, and adopting consistent output naming. Includes code cleanup and experimental labeling to improve readability and QA for plots. Overall impact and accomplishments: - Increased modeling robustness and measurement fidelity, enabling more reliable discrimination between signal and background in subsequent analyses. - Improved reproducibility and QA through standardized outputs and clearer visualization pipelines, accelerating downstream review and integration. - Strengthened engineering practices around data handling and plotting, reducing review cycles and enabling smoother collaboration across teams. Technologies/skills demonstrated: - Data pipeline enhancements, systematic uncertainty handling, and TES physics/application domain knowledge. - Plotting/visualization quality improvements and output organization for QA. - Version control discipline, code cleanup, and internationally aware commit messages supporting maintainability.
5 Commits • 2 Features
Jun 1, 2025June 2025 monthly summary for blackSwanCS/Higgs_collaboration_B: Two major TES (Transition Edge Sensor) analysis features were delivered, delivering measurable improvements in fit quality, uncertainty handling, and output QA, with a clear business value in reliability and downstream readiness. Key features delivered: - TES Fitter Accuracy and Systematics Handling: Unified application of systematics to the full training set before the signal/background split, resulting in improved TES fitting accuracy and more robust weight extraction/prediction on the modified dataset. Notable bug fixes include addressing a shift issue (commits referenced as shift problem try and résolution du shift) to stabilize fit behavior. - TES Uncertainties Plotting and Output Organization: Enhanced plotting and output organization for TES uncertainties by separating background and signal plots, standardizing bin indexing and labels, and adopting consistent output naming. Includes code cleanup and experimental labeling to improve readability and QA for plots. Overall impact and accomplishments: - Increased modeling robustness and measurement fidelity, enabling more reliable discrimination between signal and background in subsequent analyses. - Improved reproducibility and QA through standardized outputs and clearer visualization pipelines, accelerating downstream review and integration. - Strengthened engineering practices around data handling and plotting, reducing review cycles and enabling smoother collaboration across teams. Technologies/skills demonstrated: - Data pipeline enhancements, systematic uncertainty handling, and TES physics/application domain knowledge. - Plotting/visualization quality improvements and output organization for QA. - Version control discipline, code cleanup, and internationally aware commit messages supporting maintainability.
June 2025
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture68.0%
Performance60.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Data AnalysisData VisualizationMachine LearningPlottingPythonScientific Computing
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