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BSMArt: simple and fast parameter space scans

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arxiv 2301.01154 v1 pith:Q2CR46OH submitted 2023-01-03 hep-ph

Mark D. Goodsell , Ari Joury This is my paper · ORCID
classification hep-ph
keywords bsmartcontainsparameterscansimpletoolsactiveadditional
verification ladder T0 review T1 audit T2 compute T3 formal
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We introduce BSMArt, a python program for the exploration of parameter spaces of theories Beyond the Standard Model. Especially designed for use with the SARAH family of tools, it is also sufficiently flexible to be used with a wide variety of external codes. BSMArt contains the first public release of the Active Learning scan by the same authors; but contains several additional scanning algorithms, ranging from the very simple to MultiNest and Diver. A BSMArt scan can be set up in a matter of minutes with only minimal editing of configuration files; installation scripts for all relevant tools and examples are provided.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Deciphering compressed electroweakino excesses with MadAnalysis 5

    hep-ph 2025-07 conditional novelty 6.0 of 10

    MadAnalysis 5 v1.11 adds validated recasting of two ATLAS compressed-electroweakino searches and shows that a proposed NMSSM scenario fits the soft-lepton and monojet excesses less well than simpler models.

  2. A joint explanation for the soft lepton and monojet LHC excesses in the wino-bino model

    hep-ph 2025-06 conditional novelty 6.0 of 10

    A combined fit to four LHC Run 2 searches prefers a wino-bino supersymmetric spectrum with m(wino) ~ 320 GeV and mass splitting ~ 20 GeV, compatible with bino dark matter.

  3. DLScanner: A parameter space scanner package assisted by deep learning methods

    hep-ph 2024-12 conditional novelty 6.0 of 10

    A new scanner package combines a similarity-learning neural network with VEGAS adaptive sampling to collect valid points in BSM parameter scans faster than earlier ML-based methods.

  4. hep-aid: A Python Library for Sample Efficient Parameter Scans in Beyond the Standard Model Phenomenology

    hep-ph 2024-12 conditional novelty 4.0 of 10

    hep-aid is a modular Python library that packages active search, neural network, and MCMC parameter scan methods with a Higgs physics software stack, and its demonstrations show sample efficiency gains on test and BSM...

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