Normalizing Flow-Assisted Nested Sampling on Type-II Seesaw Model
Abstract
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 on Type-II Seesaw model to test the efficacy of the algorithm. 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: https://github.com/sunandopatra/MLNS-T2SS
Cite
@article{arxiv.2501.16432,
title = {Normalizing Flow-Assisted Nested Sampling on Type-II Seesaw Model},
author = {Rajneil Baruah and Subhadeep Mondal and Sunando Kumar Patra and Satyajit Roy},
journal= {arXiv preprint arXiv:2501.16432},
year = {2025}
}
Comments
19 pages, 10 figures, Version Submitted to EPJC