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Exploring the BSM parameter space with Neural Network aided Simulation-Based Inference

A Chatterjee, A Choudhury, S Mitra, A Mondal… - arXiv preprint arXiv …, 2025 - arxiv.org
Computer Science paper hep-ph Suggest

… These likelihood-free inferences are often called Simulation-Based Inference (SBI) as it requires a simulator for calculating observables from parameters (discussed in …

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BibTeX

@article{2502.11928v1,
Author        = {Atrideb Chatterjee and Arghya Choudhury and Sourav Mitra and Arpita Mondal and Subhadeep Mondal},
Title         = {Exploring the BSM parameter space with Neural Network aided
  Simulation-Based Inference},
Eprint        = {2502.11928v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {hep-ph},
Abstract      = {Some of the issues that make sampling parameter spaces of various beyond the
Standard Model (BSM) scenarios computationally expensive are the high
dimensionality of the input parameter space, complex likelihoods, and stringent
experimental constraints. In this work, we explore likelihood-free approaches,
leveraging neural network-aided Simulation-Based Inference (SBI) to alleviate
this issue. We focus on three amortized SBI methods: Neural Posterior
Estimation (NPE), Neural Likelihood Estimation (NLE), and Neural Ratio
Estimation (NRE) and perform a comparative analysis through the validation test
known as the \textit{ Test of Accuracy with Random Points} (TARP), as well as
through posterior sample efficiency and computational time. As an example, we
focus on the scalar sector of the phenomenological minimal supersymmetric SM
(pMSSM) and observe that the NPE method outperforms the others and generates
correct posterior distributions of the parameters with a minimal number of
samples. The efficacy of this framework will be more evident with additional
experimental data, especially for high dimensional parameter space.},
Year          = {2025},
Month         = {Feb},
Url           = {http://arxiv.org/abs/2502.11928v1},
File          = {2502.11928v1.pdf}
}

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