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

TL;DR

This paper demonstrates how neural network-aided Simulation-Based Inference can efficiently explore complex BSM parameter spaces, outperforming traditional methods like MCMC in accuracy and sample efficiency, especially in high-dimensional scenarios.

Contribution

The study introduces and compares three amortized SBI methods for BSM parameter inference, highlighting NPE's superior performance in the pMSSM context.

Findings

NPE outperforms NLE and NRE in accuracy and efficiency.

SBI methods produce faithful posterior distributions even with added dark matter constraints.

Predicted dark matter candidate compositions vary with mass, consistent with physical expectations.

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 is tested on 5 parameter pMSSM with Higgs and flavor physics data and its performance is compared with the MCMC method. We further add dark matter (DM) observables to make the task more challenging and consider a 9 parameter pMSSM. We observe that even though the efficiency factor drops, the amortized SBI method still produces faithful posterior distributions. SBI predicted points satisfying DM constraints are mostly bino-dominated upto $\sim$ 1.5 TeV, and are mostly wino-dominated within the 1.5 - 2 TeV range.