Abstract The Self-Organizing-Map (SOM) is a widely used neural network for dimensional reduction and clustering. It has yet to find its use in high energy physics. This paper discusses two applications of SOM: first, we map regions with relative content rare process ( H → WW *). Second obtain Monte Carlo normalization factors different physics processes by fitting the dimensionally reduced repr...

Although matter contributions to the graviton self-energy $-i[\mbox{}^{\mu\nu} \Sigma^{\rho\sigma}](x;x')$ must be separately conserved on $x^{\mu}$ and ${x'}^{\mu}$, obey weaker constraint of Ward identity, which involves a divergence both coordinates. On general homogeneous isotropic background this leads just four structure functions for but nine contributions. We propose convenient paramete...

We offer in this review a description of the vacuum energy self-similar systems. describe two views setting structures and point out main differences. A authors' work on subject is presented, where they treat system as many-object problem embedded regular smooth manifold. Focused Dirichlet boundary conditions, we report systematic way calculating Casimir bodies knowledge quantum single building...