Speaker
Dr
Silvia Tentindo
(Department of Physics-Florida State University)
Description
Neural networks (NN) are universal approximators. Therefore, in principle, it should be possible to use them to model any reasonably smooth probability density such as the probability density of fake missing transverse energy (MET). The modeling of fake MET is an important experimental issue in events such as
$Z \rightarrow l^+ l^-$+jets, which is an important background in high-mass Higgs searches at the Large Hadron Collider. We describe how Bayesian neural networks (BNN) can be used to model the MET in $\gamma$+jets events and how, in turn, the resulting BNN function can be used to model the missing transverse energy distribution in samples other than $\gamma$+jets in which the MET is largely due to instrumental effects.
Primary authors
Prof.
Harrison Prosper
(Department of Physics-Florida State University)
Dr
Silvia Tentindo
(Department of Physics-Florida State University)
