Particle Physics Seminar

"Improving Neutrino Energy Reconstruction with Recurrent Neural Networks at NOvA"

Presented by Dr Dmitri Torbunov, Minnesota University

Thursday, February 25, 2021, 3:00 pm — Webcast

Abstract: In this talk I will discuss the application of Recurrent Neural Networks to the problem of neutrino energy reconstruction at the NOvA experiment. NOvA is a long-baseline accelerator based neutrino oscillation experiment that holds one of the leading measurements of the Δm232 oscillation parameter. In order to make precise measurements of the neutrino oscillation parameters, NOvA needs a good neutrino energy estimation algorithm.

A new energy estimation algorithm that is based on a recurrent neural network architecture has been developed for NOvA. The new energy estimator has 15% better energy reconstruction than the previous energy estimation algorithm, and it is 5 times less sensitive to the major systematic uncertainty at NOvA. Using the new energy estimator has the potential to significantly improve the precision of measurements of the neutrino oscillation parameters at NOvA and could potentially be adapted to other neutrino experiments.

Hosted by: Brett Viren and Hanyu Wei

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