Deep Learning for Neutrino Physics on NOvA: Successes and Lessons*

NOvA is a long baseline neutrino experiment which measures muon-flavor to electron-flavor oscillations from neutrinos and anti-neutrinos produced in the NuMI beam at Fermilab. Over the past few years, we have adapted techniques from the field of computer vision to fundamental parts of NOvA's analysis  such as signal selection, final state identification, and energy reconstruction.

 The adaptation of deep learning algorithms into analysis tools has been fruitful, and is becoming more widespread within NOvA and in the field. In this overview of NOvA's deep learning program I will showcase our applications to detector data analysis, as well as the improvements in selection efficiency obtained for anti-neutrino events in our latest result. I will also highlight ongoing development of new applications for a varied range of reconstruction tasks. I will discuss challenges associated with adapting these techniques to detector data, and lessons learned from our uses of deep learning in combination with NOvA's traditional reconstruction methods.