arXivDaily arXiv每日学术速递 周一至周五更新
arXiv周末暂无论文更新,休息一下吧,周末愉快~~
arXiv 2510.02445hep-ph
收藏

利用机器学习调研复杂三双态模型

Surveying the complex three Higgs doublet model with Machine Learning

Rafael Boto, João A. C. Matos, Jorge C. Romão, João P. Silva

AI总结:

本文利用机器学习研究了包含CP破坏的三双态模型,探索了不同耦合选择和质量排列下的现象学特性,以寻找大伪标量耦合的可能性。

摘要:

随着LHC运行阶段3的持续进行,125 GeV希格斯耦合的测量精度正在不断提高。包含多个双态模型的理论允许对标准模型预测的潜在偏离。对于超过两个双态的情况,通过引入对称性可以避免树级中性耦合的味变化,共有五种可能的模型类型。我们考虑了一个软破缺的Z2xZ2三双态模型,其中标量部分存在显性的CP破坏,研究了所有五种可能的耦合选择和中性标量玻色子的五种质量排列。通过使用机器学习黑盒优化算法进行现象学研究,该算法高效地搜索了大伪标量杨-米尔斯耦合的可能性。我们确定了在考虑所有最近实验限制的情况下,允许纯伪标量耦合的模型选择,从而推动了提高实验精度的努力。

英文摘要:

The couplings of the 125 GeV Higgs are being measured with higher precision as the Run 3 stage of LHC continues. Models with multiple Higgs doublets allow potential deviations from the SM predictions. For more than two doublets, there are five possible types of models that avoid flavor changing neutral couplings at tree level by the addition of a symmetry. We consider a softly broken Z2xZ2 three-Higgs doublet model with explicit CP violation in the scalar sector, exploring all five possible types of coupling choices and all five mass orderings of the neutral scalar bosons. The phenomenological study is performed using a Machine Learning black box optimization algorithm that efficiently searches for the possibility of large pseudoscalar Yukawa couplings. We identify the model choices that allow a purely pseudoscalar coupling in light of all recent experimental limits, including direct searches for CP-violation, thus motivating increased effort into improving the experimental precision.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑