Jarvis-HEP 1.6.1

Jarvis-HEP: likelihood-driven HEP scanning framework

Jarvis-HEP

Jarvis-HEP (Just a Really Viable Interface to Suites for High Energy Physics) is an open-source, modular Python framework for likelihood-based parameter scanning and global fits in high-energy physics phenomenology.

The project focuses on robust sampling strategies, nuisance-parameter handling, and scalable asynchronous workflows, with an emphasis on profile likelihood–oriented studies commonly encountered in modern collider phenomenology.

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Motivation

Modern HEP analyses often suffer from:

• Extremely sparse viable regions in high-dimensional parameter spaces
• Expensive external likelihood evaluations (collider simulations, Higgs constraints, flavour physics, etc.)
• Non-trivial treatment of nuisance parameters
• Poor reproducibility and opaque analysis pipelines

Jarvis-HEP is designed to address these issues by providing:

• A unified orchestration layer for sampling, likelihoods, and external tools
• Explicit separation between parameters of interest and nuisance parameters
• Flexible, engineering-oriented solutions such as profile likelihood–based inference
• Transparent data management and diagnostic logging

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Key Features

Sampling & Exploration

• Multiple sampling strategies (random, grid-like, adaptive, nested-style, and custom algorithms)
• Designed for highly constrained and fine-tuned parameter spaces
• Iterator-style, point-level sampling (not generation-locked)

Likelihood & Nuisance Parameters

• Native support for profile likelihood construction
• Dedicated nuisance-parameter samplers decoupled from main exploration
• Fast evaluation paths for nuisance optimization

Architecture

• Pure Python implementation
• Asynchronous execution of expensive external programs
• Modular factory-based design (samplers, likelihoods, IO, monitors)
• YAML-driven configuration for reproducibility

Data Handling & Diagnostics

• HDF5-based structured output
• Schema-driven CSV flattening for structured observables (samples.schema.json)
• Explicit resource and file-handle monitoring
• Detailed logging via loguru
• Designed to survive partial failures (no silent data loss)

Visualization

• Dynamic sampling visualizations (under active development)
• Designed to interface with the standalone plotting package JarvisPLOT

Example visualization of an adaptive sampling procedure:
👉 https://github.com/Pengxuan-Zhu-Phys/Jarvis-HEP/blob/master/simu/sample_dynamic_viz.gif

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📘 Full documentation and tutorials are hosted on a dedicated documentation site:

👉 https://pengxuan-zhu-phys.github.io/Jarvis-Docs/

Installation

Jarvis-HEP is a pure Python project.

Install from PyPI

python3 -m pip install Jarvis-HEP

After installation, run directly:

Jarvis --help

Install from source (developer mode)

python3 -m pip install -e .

Release helper (maintainer)

./jhrel 1.0.1 --dry
./jhrel 1.0.1 --testpypi
./jhrel 1.0.1 --reinstall

Create a standalone project workspace

Use --mkproject to create a fresh Jarvis project directory:

Jarvis --mkproject PROJECT_NAME

This creates:

• PROJECT_NAME/bin for YAML cards
• PROJECT_NAME/Library for source libraries
• PROJECT_NAME/Workshop for workflow files
• PROJECT_NAME/Result for outputs

Running

Jarvis-HEP can now be launched without changing into the source root:

Jarvis /path/to/project/bin/task.yaml

Or via module mode:

python -m jarvishep /path/to/project/bin/task.yaml

Path Markers

• &J/... resolves to the runtime task root (auto-inferred from the YAML location, typically the parent of bin/)
• &SRC/... resolves to the Jarvis-HEP source tree (internal cards/schema/logo)

Observable Schema And CSV Flattening

Jarvis-HEP stores scan data in HDF5 and exports CSV through a user-editable schema file:

• raw records: .../DATABASE/samples.N.hdf5
• schema rules: .../DATABASE/samples.schema.json
• CSV export: .../DATABASE/samples.N.csv

samples.schema.json records each column's type metadata and flatten rules.
You can edit flatten rules and regenerate CSV without rerunning sampling:

Jarvis /path/to/task.yaml --convert

Supported column flatten modes:

• scalar: scalar-first export (structured values fall back to JSON cell text)
• json: write structured value as one JSON string cell
• split: expand structured value to multiple CSV columns (supports name_map rename mapping)
• drop: omit column from CSV export

Detailed schema fields and examples:

• docs/OBSERVABLE_SCHEMA.md

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Contributing

Contributions are welcome in all forms, including feature proposals, documentation improvements, bug reports, and bug fixes.
Please refer to CONTRIBUTING.md for guidelines on how to get started.

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License

Jarvis-HEP is released under the MIT License.
See the LICENSE file for details.

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Acknowledgements

The author thanks
Yang Zhang and Liangliang Shang
for helpful discussions during the development of this project.

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References

• Exploring supersymmetry with machine learning
  Jie Ren, Lei Wu, Jin-Ming Yang, Jun Zhao
  Nuclear Physics B, 2019
  arXiv:1708.06615