Explaining Data Anomalies over the NMSSM Parameter Space with Deep Learning Techniques

TL;DR

This paper uses deep learning to identify NMSSM parameter regions that can explain multiple current particle physics anomalies while satisfying experimental constraints.

Contribution

It introduces a novel deep learning-assisted scanning method to find NMSSM scenarios that accommodate various experimental anomalies.

Findings

Viable NMSSM regions explain multiple anomalies at 2σ level

Deep learning accelerates parameter space exploration

Provides benchmark points for future studies

Abstract

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 anomalies across low- and high-energy experiments. These include the so-called 95GeV and 650GeV excesses from Higgs studies, the Electro-Weakino excess from Supersymmetry searches, the latest $(g-2)_\mu$ measurements as well as potential deviations from Standard Model (SM) predictions that would appear as a consequence in mono-$H$ (where $H=h_{\rm SM}$) and -$Z$ signatures of Dark Matter. Our analysis demonstrates that viable NMSSM parameter regions exist where all these features can be accommodated at the $2\sigma$ 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.