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
Developed automated benchmarks for the Backward Hcal detector in the eic/detector_benchmarks repository, focusing on streamlining detector performance evaluation. Leveraged Python, C++, and Snakemake to create workflows and configuration files that automate simulation and analysis across various particle interactions and energy levels. Integrated these benchmarks into a CI/CD pipeline, enabling reproducible acceptance testing and basic distribution analysis. This approach established a consistent benchmarking baseline and provided rapid feedback through CI signals, supporting ongoing detector development. The work emphasized workflow management and data analysis, ensuring that performance metrics could be efficiently generated and validated as part of the continuous integration process.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
548
Activity Months1
Your Network
19 peopleSame Organization
@pw.edu.pl12
Shared Repositories
7
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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