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This study explores parameter space sampling for Beyond the Standard Model (BSM) physics, a computationally demanding challenge in High Energy Physics (HEP). Simulation-Based Inference (SBI) presents a compelling alternative to traditional likelihood-based methods by avoiding explicit likelihood calculations, which are often complex for BSM scenarios. We apply SBI techniques-Neural Posterior Estimation (NPE), Neural Likelihood Estimation (NLE), and Neural Ratio Estimation (NRE)-to the extended Higgs sector of the phenomenological Minimal Supersymmetric Model (pMSSM). Our results demonstrate that only NPE successfully samples the pMSSM parameter space, while NLE and NRE encounter notable limitations. Using these three SBI methods, we map the allowed parameter space in the \(M_A-\tan \beta \) plane.

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Investigating Extended Higgs Sector via Amortized Inference

  • 会议论文

  • 发表日期：

    2025年

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  • Atrideb Chatterjee,
  • Arghya Choudhury,
  • Sourav Mitra,
  • Arpita Mondal,
  • Subhadeep Mondal

摘要

This study explores parameter space sampling for Beyond the Standard Model (BSM) physics, a computationally demanding challenge in High Energy Physics (HEP). Simulation-Based Inference (SBI) presents a compelling alternative to traditional likelihood-based methods by avoiding explicit likelihood calculations, which are often complex for BSM scenarios. We apply SBI techniques-Neural Posterior Estimation (NPE), Neural Likelihood Estimation (NLE), and Neural Ratio Estimation (NRE)-to the extended Higgs sector of the phenomenological Minimal Supersymmetric Model (pMSSM). Our results demonstrate that only NPE successfully samples the pMSSM parameter space, while NLE and NRE encounter notable limitations. Using these three SBI methods, we map the allowed parameter space in the \(M_A-\tan \beta \) plane.