Introduction

BSMArt is a powerful and lightweight code for exploring the parameter spaces of BSM theories. Written in python, it is ideal for both simple and sphisticated scans, especially those using machine-learning features.

To set up BSMArt, see Installation

For a list of included scans, see Included Scans

For a list of tools, see Tools included with BSMArt

Online documentation is found at bsmart-hep.github.io/core/

The code for version 2 and later can be perused at github.com/bsmart-hep/core while all code can be downloaded from goodsell.pages.in2p3.fr/bsmart/.

The community examples repository can be found at github.com/bsmart-hep/examples

Credits

BSMArt is maintained by Mark Goodsell and Miguel Crispim Romao in collaboration with:

• Ari Joury

• Asesh Datta

• Luc Darmé

• Johannes Braathen

• Martin Gabelmann

• Wojciech Kotlarski

• Farid Ibrahimov

• Fernando Abreu de Souza

• Werner Porod

If you use it, please cite:

• BSMArt: Simple and fast parameter space scans

Other relevant references include:

• Active learning BSM parameter spaces

• M_W in string derived Z’ models

• HackAnalysis 2: A powerful and hackable recasting tool

A references.bib file is generated as part of scan running to help, which should include references for the used tools and scans!

Contributing

Since it is so easy to add tools and BSMArt, ideally it should serve as a repository for scans developed to write papers. If you have a scan that you have written (even if it is not for BSMArt …) please get in touch!