Unearthing large pseudoscalar Yukawa couplings with Machine Learning

TL;DR

This paper introduces a Machine Learning approach, specifically an Evolutionary Strategy with a Novelty Reward, to efficiently explore large parameter spaces in multi-Higgs models with CP violation, revealing new regions and observable effects.

Contribution

It presents a novel ML-based sampling method that improves exploration of complex BSM models, demonstrated on a three-Higgs doublet model with CP violation.

Findings

Discovery of new parameter space regions with large pseudoscalar Yukawa couplings.

Enhanced sampling efficiency compared to previous techniques.

Potential to identify observable consequences in Higgs physics.

Abstract

With the Large Hadron Collider's Run 3 in progress, the 125 GeV Higgs boson couplings are being examined in greater detail, while searching for additional scalars. Multi-Higgs frameworks allow Higgs couplings to significantly deviate from Standard Model values, enabling indirect probes of extra scalars. We consider the possibility of large pseudoscalar Yukawa couplings in the softly-broken Z2xZ2' three-Higgs doublet model with CP violating coefficients. To explore the parameter space of the model, we employ a Machine Learning algorithm that significantly enhances sampling efficiency. Using it, we find new regions of parameter space and observable consequences, not found with previous techniques. This method leverages an Evolutionary Strategy to quickly converge towards valid regions with an additional Novelty Reward mechanism. We use this model as a prototype to illustrate the potential of the new techniques, applicable to any Physics Beyond the Standard Model scenario.