Configuration and Benchmarking of $\mathrm{e}^+\mathrm{e}^-$ Processes with K4GeneratorsConfig

The next generation of electron-positron colliders will require unprecedented precision in both theory and experiment. Sophisticated software frameworks are essential to evaluate detector concepts, optimize designs, and simulating physical processes. In this context, Monte Carlo (MC) event generators play a central role, enabling realistic simulations of Standard Model processes and providing the basis for physics studies. However, technical consistency across different generators remains critical, particularly in domains where agreement is expected. To address this need, we present K4GeneratorsConfig, a Python-based package that automates the benchmarking process for MC generators. The tool translates universal physics inputs into generator-specific configurations, ensuring consistency, reproducibility, and reduced human error. Its modular design allows for straightforward integration of additional generators, supports batch processing, and provides compatibility with the Key4hep software stack

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Publication Details

Journal
ArXiv.org
Published
2025-09-24
Primary Topic
Distributed and Parallel Computing Systems
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article
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article

Configuration and Benchmarking of $\mathrm{e}^+\mathrm{e}^-$ Processes with K4GeneratorsConfig

D. Zerwas, Alan Price
ArXiv.org
Distributed and Parallel Computing Systems
article

Configuration and Benchmarking of $\mathrm{e}^+\mathrm{e}^-$ Processes with K4GeneratorsConfig

D. Zerwas, Alan Price
article en

Abstract

The next generation of electron-positron colliders will require unprecedented precision in both theory and experiment. Sophisticated software frameworks are essential to evaluate detector concepts, optimize designs, and simulating physical processes. In this context, Monte Carlo (MC) event generators play a central role, enabling realistic simulations of Standard Model processes and providing the basis for physics studies. However, technical consistency across different generators remains critical, particularly in domains where agreement is expected. To address this need, we present K4GeneratorsConfig, a Python-based package that automates the benchmarking process for MC generators. The tool translates universal physics inputs into generator-specific configurations, ensuring consistency, reproducibility, and reduced human error. Its modular design allows for straightforward integration of additional generators, supports batch processing, and provides compatibility with the Key4hep software stack

ArXiv.org
Openalex Percentile: Top 67%
Distributed and Parallel Computing Systems
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Configuration and Benchmarking of $\mathrm{e}^+\mathrm{e}^-$ Processes with K4GeneratorsConfig — D. Zerwas, Alan Price · ArXiv.org (2025) | TGRS Research Map | TGRS