Improving Neutrino Energy Reconstruction with Recurrent Neural Networks at NOvA

by Dr Dmitri Torbunov

Thursday 25 Feb 2021, 15:00 → 16:00 US/Eastern

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 $\Delta m_{32}^2$ 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.

 

Topic: BNL Particle Physics Seminar Feb 25 2021
Time: Feb 25, 2021 03:00 PM Eastern Time (US and Canada)

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2021-02-25_bnl_seminar.pdf