https://doi.org/10.1140/epjc/s10052-025-14502-5
Regular Article - Theoretical Physics
Normalizing flow-assisted nested sampling on Type-II Seesaw model
1 Department of Physics, SEAS, Bennett University, 201310, Greater Noida, Uttar Pradesh, India
2 Department of Physics, Bangabasi Evening College, 700009, Kolkata, West Bengal, India
Received:
5
February
2025
Accepted:
7
July
2025
Published online: 30 July 2025
We propose a novel technique for sampling particle physics model parameter space. The main sampling method applied is nested sampling (NS), which is boosted by the application of multiple machine learning (ML) networks, e.g., self-normalizing network (SNN) and Normalizing Flow (specifically RealNVP). We apply this to the Type-II Seesaw model to test the algorithm’s efficacy. We present the results of our detailed Bayesian exploration of the model parameter space subjected to theoretical constraints and experimental data corresponding to the 125 GeV Higgs boson, 
-parameter, and the oblique parameters. All associated data, figures, and trained ML models can be found here:GitHub
© The Author(s) 2025
Open Access
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