33,824 research outputs found
From high-scale leptogenesis to low-scale one-loop neutrino mass generation
We show that a high-scale leptogenesis can be consistent with a low-scale
one-loop neutrino mass generation. Our models are based on the SU(3)_c\times
SU(2)_L\times U(1)_Y\times U(1)_{B-L} gauge groups. Except a complex singlet
scalar for the U(1)_{B-L} symmetry breaking, the other new scalars and fermions
(one scalar doublet, two or more real scalar singlets/triplets and three
right-handed neutrinos) are odd under an unbroken Z_2 discrete symmetry. The
real scalar decays can produce an asymmetry stored in the new scalar doublet
which subsequently decays into the standard model lepton doublets and the
right-handed neutrinos. The lepton asymmetry in the standard model leptons then
can be partially converted to a baryon asymmetry by the sphaleron processes. By
integrating out the heavy scalar singlets/triplets, we can realize an effective
theory to radiatively generate the small neutrino masses at the TeV scale.
Furthermore, the lightest right-handed neutrino can serve as a dark matter
candidate.Comment: 8 pages, 4 figure
Learning Large-Scale Bayesian Networks with the sparsebn Package
Learning graphical models from data is an important problem with wide
applications, ranging from genomics to the social sciences. Nowadays datasets
often have upwards of thousands---sometimes tens or hundreds of thousands---of
variables and far fewer samples. To meet this challenge, we have developed a
new R package called sparsebn for learning the structure of large, sparse
graphical models with a focus on Bayesian networks. While there are many
existing software packages for this task, this package focuses on the unique
setting of learning large networks from high-dimensional data, possibly with
interventions. As such, the methods provided place a premium on scalability and
consistency in a high-dimensional setting. Furthermore, in the presence of
interventions, the methods implemented here achieve the goal of learning a
causal network from data. Additionally, the sparsebn package is fully
compatible with existing software packages for network analysis.Comment: To appear in the Journal of Statistical Software, 39 pages, 7 figure
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