REVIEW 1 cited by
Constraining the 3HDM Parameter Space using Active Learning
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Constraining the 3HDM Parameter Space using Active Learning
read the original abstract
One of the standard ways to study scenarios beyond the Standard Model involves extending the Higgs Sector. This work examines the Three Higgs Doublet Model (3HDM) in a Type-Z or democratic setup, where each Higgs doublet couples exclusively to a specific type of fermion. The particle spectrum of the 3HDM includes four charged Higgs bosons, two CP-odd scalars, and three CP-even scalars. This work investigates the allowed mass and coupling parameter space in the Type-Z 3HDM after imposing all theoretical and experimental constraints. We extract the allowed parameter space under three distinct alignment-limit conditions or mass hierarchies leveraging machine learning techniques. Specifically, we analyze scenarios where the 125 GeV Higgs is the lightest, an intermediary, or the heaviest CP-even Higgs boson. Our findings indicate that while a single lighter CP-even Higgs boson below 125 GeV still remains a possibility, the presence of two lighter Higgses is ruled out.
Forward citations
Cited by 1 Pith paper
-
Machine Learning in the 2HDM2S model for Dark Matter
A 2HDM extended by two real scalar singlets is scanned with evolutionary strategies to locate regions satisfying vacuum, unitarity, oblique-parameter, collider and dark-matter constraints.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.