blackSwanCS/Higgs_collaboration_B
Jun 2025 – Jun 2025
1 Month active
Languages Used
Python
Technical Skills
Data AnalysisData VisualizationMachine LearningMatplotlibNumPyPandas
Lucas Desgranges
PROFILE
Lucas Desgranges
Developed a unified visualization and analysis toolkit for the blackSwanCS/Higgs_collaboration_B repository, focusing on physics data workflows. The work centered on enabling robust bias simulation and providing clear visual comparisons between signal and background datasets, addressing the need for transparent bias assessment in physics analysis. Using Python along with libraries such as Matplotlib, NumPy, and Pandas, the toolkit consolidated data analysis and visualization processes, improving the readability of plots and streamlining the evaluation of systematic effects. This feature enhanced data-driven decision making by making complex physics data more accessible and actionable for researchers, without introducing new bug fixes during the period.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
1
Lines of code
171
Activity Months1
Your Network
41 peopleShared Repositories
21
Work History
June 2025
3 Commits • 1 Features
Jun 1, 2025June 2025 performance summary for blackSwanCS/Higgs_collaboration_B: Delivered a unified Visualization and Analysis Toolkit for physics data, focusing on bias studies and clear signal/background comparisons, with significant improvements in plot readability. This work enhances data-driven decision making and accelerates physics analysis by consolidating visualization/analysis workflows and enabling robust bias assessment.
3 Commits • 1 Features
Jun 1, 2025June 2025 performance summary for blackSwanCS/Higgs_collaboration_B: Delivered a unified Visualization and Analysis Toolkit for physics data, focusing on bias studies and clear signal/background comparisons, with significant improvements in plot readability. This work enhances data-driven decision making and accelerates physics analysis by consolidating visualization/analysis workflows and enabling robust bias assessment.
June 2025
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture73.4%
Performance66.6%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Data AnalysisData VisualizationMachine LearningMatplotlibNumPyPandasPhysics SimulationSeaborn
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