Explaining data anomalies over the NMSSM parameter space with deep learning techniques

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 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 = h 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 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.

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Identifiers

ORCID for Stefano Moretti: orcid.org/0000-0002-8601-7246

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