BSMScanner: a lightweight framework for beyond-the-Standard-Model global fits
Abstract
We present BSMScanner, a framework in which a beyond-the-Standard-Model (BSM) parameter scan is specified as a set of declarative YAML documents rather than written as a program. The user declares the free parameters and their priors, the mass matrices and the diagonalisations that act on them, the observables, the theory-validity conditions and the likelihood terms. The framework validates this specification, resolves it into a dependency graph and compiles it once into a typed C++ evaluation kernel, which is then driven by an independently chosen scanning engine: uniform sampling, two differential-evolution engines, or a staged basin-discovery orchestrator, optionally followed by Markov-chain posterior refinement. Flavour physics matrix diagonalisation, PMNS and CKM construction in fixed conventions, tabulated oscillation likelihoods and a library of common flavour observables are provided by the framework and imported by name, so that a model file contains only model-specific definitions; quantities without a closed form, such as a relic density, enter through a C++ or Python plugin interface. We describe the specification schema, the observable and likelihood library, the scanning engines and their configuration, and demonstrate the framework on six benchmark models with $4$ to $26$ free parameters, giving for each the specification, the scan configuration, the resulting best fit and analytic cross-checks of the result.
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BibTeX
@misc{belfkir2026bsmscanner,
title = {{BSMScanner: a lightweight framework for beyond-the-Standard-Model global fits}},
author = {Mohamed Belfkir},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.15096},
eprint = {2610.15096},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.15096},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - BSMScanner: a lightweight framework for beyond-the-Standard-Model global fits
AU - Mohamed Belfkir
PY - 2026
DA - 2026/10/10
PB - arXiv
DO - 10.48550/arxiv.2610.15096
UR - https://arxiv.org/abs/2610.15096
AB - We present BSMScanner, a framework in which a beyond-the-Standard-Model (BSM) parameter scan is specified as a set of declarative YAML documents rather than written as a program. The user declares the free parameters and their priors, the mass matrices and the diagonalisations that act on them, the observables, the theory-validity conditions and the likelihood terms. The framework validates this specification, resolves it into a dependency graph and compiles it once into a typed C++ evaluation kernel, which is then driven by an independently chosen scanning engine: uniform sampling, two differential-evolution engines, or a staged basin-discovery orchestrator, optionally followed by Markov-chain posterior refinement. Flavour physics matrix diagonalisation, PMNS and CKM construction in fixed conventions, tabulated oscillation likelihoods and a library of common flavour observables are provided by the framework and imported by name, so that a model file contains only model-specific definitions; quantities without a closed form, such as a relic density, enter through a C++ or Python plugin interface. We describe the specification schema, the observable and likelihood library, the scanning engines and their configuration, and demonstrate the framework on six benchmark models with $4$ to $26$ free parameters, giving for each the specification, the scan configuration, the resulting best fit and analytic cross-checks of the result.
ER -