Machine Learning in the Hunt for Heavy Charged Higgs Bosons at Gamma-Gamma Colliders in the Type III Two Higgs Doublet Model

2025/04/09 by I. Ahmed, Abdul Quddus, Ahmed, Ijaz +4
Computer Science · Physics and Astronomy · #Computational Physics and Python Applications #FOS: Physical sciences #High Energy Physics - Phenomenology (hep-ph) #Neutrino Physics Research #Particle physics theoretical and experimental studies

paper · pdf · doi:10.48550/arxiv.2504.06656

openalex publication_date 2025/04/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We conduct a detailed exploration of charged Higgs boson masses M_H^± within the range of 100-190~GeV. This investigation is grounded in the benchmark points that comply with experimental constraints, allowing us to systematically account for uncertainties inherent in the analysis. Our results indicate significant production prospects for the process H⁺H^- → τν_τ τν_τ, which could provide essential insights into the properties of H^± bosons. By examining these decay channels, we aim to illuminate the interplay between the charged Higgs boson and the established Standard Model. The research uses machine learning methods like Boosted Decision Trees (BDT) and Multilayer Perceptrons (MLP), as well as Likelihood and LikelihoodD, to improve the identification of heavy charged Higgs bosons compared to Standard Model backgrounds at a 3.0 TeV γγ collider with an integrated luminosity of L_int=3000~fb^-1.

Related

• Charged Higgs Phenomenology in di-bjet channel with H^± → W^±h in 2HDM Type-II using Machine Learning Technique
• Neural Networks Search for Charged Higgs Boson of Two Doublet Higgs\n Model at the Hadrons Colliders
• Boosting Sensitivity to HH→ bb γγ with Graph Neural Networks and XGBoost
• Phenomenology of scalar particles assisted by machine learning
• Benchmarking Machine Learning Techniques with Di-Higgs Production at the LHC