Speaker
Description
In this talk we discuss application of the recurrent neural networks to the
task of energy reconstruction at the NOvA experiment. NOvA is a long-baseline
accelerator based neutrino oscillation experiment that holds a leading
measurement of the $\Delta m_{32}^2$ oscillation parameter. In order to achieve
good estimation of the oscillation parameters it is imperative to have a good
neutrino energy estimation algorithm. We have developed a new energy estimation
algorithm that is based on a recurrent neural network. The new energy estimator
has better performance than the previous NOvA energy estimation algorithm, and
it is less affected by some of the major NOvA systematics. Using this new
energy estimator has potential to significantly improve NOvA sensitivity to the
oscillation parameters.
