Context
'Learning the production cross-sections of the Inert Doublet Model'
Cite as
Humberto Reyes-Gonzlez, Andre Lessa, Sydney Otten. (2020). 'Learning the production cross sections of the Inert Doublet Model' training data set. [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3689678
Content
Pheno AI training dataset used in the ''Learning the production cross-sections of the Inert Doublet Model'' subproject, made of 50000 samples with
5 input values:
MH0
MA0
MHC
lam2
lamL
and
8 target values
xsec353513TeV
xsec363613TeV
xsec373713TeV
xsec353713TeV
xsec363713TeV
xsec373513TeV
xsec373613TeV
xsec353613TeV
from a parameter space of the Inert Doublet Model chosen as: 50 MH0, MA0, MHC3000GeV; 2 lam2, lamL2.
The cross-sections were computed at leading order using MADGRAPH2.6.4 and the IDM UFO implementation from the FeynRules database.
Inert Doublet Model
The inert doublet model, a minimal extension of the Standard Model by a second higgs doublet with no direct couplings to quarks or leptons, is one of the simplest scenarios that can explain the dark matter.
Additional information
Pheno AI training data
Les Houches project for a database of networks for regression and classification of quantities relevant for particle physics phenomenology.
Curated by: scaron123
Curation policy: Contact organisers of the Les Houches project
Created: June 25, 2019
Harvesting API: OAI-PMH Interface
Acknowledgements
darkmachines.org
phenomldata.org