Scholarlink

<p>Motivated by recent results from particle physics analyses, we investigate the Next-to-Minimal Supersymmetric Standard Model (NMSSM) as a framework capable of accommodating a range of current data excesses across low- and high-energy experiments. These include the so-called 95 GeV and 650 GeV excesses from Higgs studies, the Electro-Weakino excess from Supersymmetry searches, the latest (<i>g</i> – 2)<sub><i>μ</i></sub> measurements as well as potential deviations from Standard Model (SM) predictions that would appear as a consequence in mono-<i>H</i> (where <i>H</i> = <i>h</i><sub>SM</sub>) and -<i>Z</i> signatures of Dark Matter. Our analysis demonstrates that viable NMSSM parameter regions exist where all these features can be accommodated at the 2<i>σ</i> level while remaining consistent with the most up-to-date theoretical and experimental constraints. To identify such regions, we employ an efficient numerical scanning strategy assisted by Deep Learning techniques. We further present several Benchmark Points that realize these scenarios, offering promising directions for future phenomenological studies.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Explaining data excesses over the NMSSM parameter space with Deep Learning techniques

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月5日

查看全文
  • A. Hammad,
  • Raymundo Ramos,
  • Amit Chakraborty,
  • Pyungwon Ko,
  • Stefano Moretti

摘要

Motivated by recent results from particle physics analyses, we investigate the Next-to-Minimal Supersymmetric Standard Model (NMSSM) as a framework capable of accommodating a range of current data excesses across low- and high-energy experiments. These include the so-called 95 GeV and 650 GeV excesses from Higgs studies, the Electro-Weakino excess from Supersymmetry searches, the latest (g – 2)μ measurements as well as potential deviations from Standard Model (SM) predictions that would appear as a consequence in mono-H (where H = hSM) and -Z signatures of Dark Matter. Our analysis demonstrates that viable NMSSM parameter regions exist where all these features can be accommodated at the 2σ level while remaining consistent with the most up-to-date theoretical and experimental constraints. To identify such regions, we employ an efficient numerical scanning strategy assisted by Deep Learning techniques. We further present several Benchmark Points that realize these scenarios, offering promising directions for future phenomenological studies.