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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.25557 |
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| _version_ | 1866917443998318592 |
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| author | Guo, Erdong Jackson, Paul Yang, Jin Min Zhu, Pengxuan |
| author_facet | Guo, Erdong Jackson, Paul Yang, Jin Min Zhu, Pengxuan |
| contents | High-energy physics phenomenology often requires linking multiple computational tools to evaluate observables, likelihoods, and experimental constraints across nontrivial parameter spaces. In this work, we introduce Jarvis-HEP, a lightweight Python framework for workflow composition and parameter scans in high-energy physics. The framework provides YAML-based workflow specification, dependency-aware execution, modular calculator integration, and asynchronous task scheduling for multi-step computational studies. It supports both external software packages and internally implemented components within a unified workflow, and the current implementation includes several built-in sampling backends for exploratory scans. This paper describes the design and user interface of Jarvis-HEP and illustrates its use with representative synthetic and phenomenological examples. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_25557 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Jarvis-HEP: A lightweight Python framework for workflow composition and parameter scans in high-energy physics Guo, Erdong Jackson, Paul Yang, Jin Min Zhu, Pengxuan High Energy Physics - Phenomenology Computational Physics High-energy physics phenomenology often requires linking multiple computational tools to evaluate observables, likelihoods, and experimental constraints across nontrivial parameter spaces. In this work, we introduce Jarvis-HEP, a lightweight Python framework for workflow composition and parameter scans in high-energy physics. The framework provides YAML-based workflow specification, dependency-aware execution, modular calculator integration, and asynchronous task scheduling for multi-step computational studies. It supports both external software packages and internally implemented components within a unified workflow, and the current implementation includes several built-in sampling backends for exploratory scans. This paper describes the design and user interface of Jarvis-HEP and illustrates its use with representative synthetic and phenomenological examples. |
| title | Jarvis-HEP: A lightweight Python framework for workflow composition and parameter scans in high-energy physics |
| topic | High Energy Physics - Phenomenology Computational Physics |
| url | https://arxiv.org/abs/2604.25557 |