eic/detector_benchmarks
Jul 2025 – Jul 2025
1 Month active
Languages Used
C++PythonShell
Technical Skills
CI/CDData AnalysisDetector SimulationHigh Energy PhysicsWorkflow Management (Snakemake)
Michał Sieczczyński
PROFILE
Michał Sieczczyński
During July 2025, Jan Kowalski developed automated benchmarks for the Backward Hcal detector in the eic/detector_benchmarks repository, focusing on reproducible performance evaluation in high energy physics. He integrated acceptance testing and basic distribution analysis into a CI/CD pipeline, leveraging Snakemake for workflow management and Python for simulation and data analysis. His approach automated simulation and analysis across various particle interactions and energy levels, establishing a consistent benchmarking baseline and accelerating feedback cycles. The work demonstrated depth in workflow automation and detector simulation, providing a maintainable framework for ongoing detector performance assessment without addressing bug fixes during this period.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
548
Activity Months1
Your Network
18 peopleSame Organization
@pw.edu.pl12
Work History
July 2025
1 Commits • 1 Features
Jul 1, 2025Implemented automated benchmarks for the Backward Hcal detector, with CI/CD integration to streamline performance evaluation. This includes acceptance testing and basic distribution analysis, plus Snakemake-driven workflows and configuration files to automate simulation and analysis across particle interactions and energy levels, enabling reproducible benchmarking and faster feedback.
1 Commits • 1 Features
Jul 1, 2025Implemented automated benchmarks for the Backward Hcal detector, with CI/CD integration to streamline performance evaluation. This includes acceptance testing and basic distribution analysis, plus Snakemake-driven workflows and configuration files to automate simulation and analysis across particle interactions and energy levels, enabling reproducible benchmarking and faster feedback.
July 2025
Activity
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Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture90.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++PythonShell
Technical Skills
CI/CDData AnalysisDetector SimulationHigh Energy PhysicsWorkflow Management (Snakemake)
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