SkillAgentSearch skills...

Smodels

SModelS

Install / Use

/learn @SModelS/Smodels
About this skill

Quality Score

0/100

Supported Platforms

Universal

README

.. image:: https://smodels.github.io/pics/banner.png

.. |PyPI version| image:: https://badge.fury.io/py/smodels.svg :target: https://badge.fury.io/py/smodels

.. |Anaconda version| image:: https://anaconda.org/conda-forge/smodels/badges/version.svg :target: https://anaconda.org/conda-forge/smodels/

.. |GitHub Project| image:: https://img.shields.io/badge/GitHub--blue?style=social&logo=GitHub :target: https://github.com/SModelS

.. |DOI| image:: https://zenodo.org/badge/DOI/10.5281/zenodo.1169739.svg :target: https://doi.org/10.5281/zenodo.116973

.. |CodeFactor| image:: https://www.codefactor.io/repository/github/smodels/smodels/badge/main :target: https://www.codefactor.io/repository/github/smodels/smodels/overview/main

.. |Docs| image:: https://img.shields.io/badge/docs-main-blue.svg
:target: https://smodels.readthedocs.io

.. |Colab| image:: https://colab.research.google.com/assets/colab-badge.svg :target: https://colab.research.google.com/github/SModelS/tutorials/blob/main/index.ipynb

|GitHub Project| |PyPI version| |Anaconda version| |CodeFactor| |Docs|

============== SModelS v3

SModelS -- A tool for interpreting simplified-model results from the LHC.

SModelS is an automatic, public tool for interpreting simplified-model results from the LHC. It is based on a general procedure to decompose Beyond the Standard Model (BSM) collider signatures into Simplified Model Spectrum (SMS) topologies. Our method provides a way to cast BSM predictions for the LHC in a model independent framework, which can be directly confronted with the relevant experimental constraints.

Installation

For instructions on how to install SModelS, see the section Installation <http://smodels.readthedocs.io/en/latest/Installation.html>_ of the SModelS online manual_.

Running SModelS

SModelS provides a command-line tool (runSModelS.py) for the basic functionalities, which can be executed as:

./runSModelS.py -p <parameter file> -f <input file or directory> -o <output directory>

For help instructions:

./runSModelS.py -h

An example file on how to call the SModelS libraries from your own Python code can be found in Example.py.

Detailed explanations on how to use SModelS, including explanations of the output, can be found in the section Using SModelS <http://smodels.readthedocs.io/en/latest/RunningSModelS.html>_ of the SModelS online manual_.

A few example input files are provided in the inputFiles folder and can be used to test runSModelS.py.

Citation

If you use this software please cite the SModelS v1-v3 manuals, the original SModelS publication, as well as the programs it makes use of. For your convenience, the relevant citations are provided in bibtex format in references.bib <https://github.com/SModelS/smodels/blob/main/references.bib>_.

For citing the experimental analyses in the database, you can use database.bib <https://github.com/SModelS/smodels-database-release/blob/main/database.bib>_.

.. _SModelS online manual: http://smodels.readthedocs.io/

Related Skills

View on GitHub
GitHub Stars16
CategoryDevelopment
Updated2mo ago
Forks6

Languages

Python

Security Score

75/100

Audited on Feb 4, 2026

No findings