A novel scenario in the semi-constrained NMSSM

Abstract In this work, we develop a novel efficient scan method, combining the Heuristically Search (HS) and the Generative Adversarial Network (GAN), where the HS can shift marginal samples to perfect samples, and the GAN can generate a huge amount of recommended samples from noise in a short time....

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Bibliographic Details
Main Authors: Kun Wang, Jingya Zhu
Format: Article
Language:English
Published: SpringerOpen 2020-06-01
Series:Journal of High Energy Physics
Subjects:
Online Access:http://link.springer.com/article/10.1007/JHEP06(2020)078