Using deep learning techniques to measure neutrino oscillations
Important Note: Work from this project is migrating over to (title pending...) which will be the most up-to-date version. This repo will not be modfied further. (08/07/2024)
Project Supervisor: Prof. Ryan Nichol
Background
The NuMI (Neutrino at Main Injects) Off-axis $\nu_\text{e}$ Appearance (NOνA) experiment is a long baseline accelerator neutrino experiment that observes $\nu_\mu$ (or $\bar{\nu}_\mu$) disappearance and $\nu_\text{e}$ (or $\bar{\nu}_\text{e}$ ) appearance. NOνA consists of the 290 ton near detector and the 14 kton far detector that are located 1 km and 810 km away
from NuMI respectively.
Figure 1: The NOvA far detector. (Credit: Fermilab)
Figure 2: Live event display of the NOvA far detector. Updates when you refresh the page. (Credit: Fermilab)
Improve the selection of $\nu_\mu$ events at the far detector using deep learning techniques. The current analysis procedure applies strict and inefficient cuts based on track containment (also, cuts based on CVN PID score).
About
Using deep learning techniques to measure neutrino oscillations. (MSci Project)
