Speakers
Dr
Fernanda Psihas
(University of Texas at Arlington, Indiana University)Mr
Micah Groh
(Indiana University)
Description
The NOvA experiment has made both $\nu_\mu$ disappearance and $\nu_e$ appearance measurements in Fermilab's NuMI beam, and is working on cross section measurements using near detector data. At the core of NOvA's measurements is the use of deep learning algorithms for identification and reconstruction of the neutrino flavor and energy. These algorithms, used for the first time on NOvA in 2016, yielded large improvements in selection efficiency, and will be applied to our first anti-neutrino results to be released this year.
Presented here is the extension of our deep learning efforts for identification of neutrino signal events, final state identification, single particle tagging, and reconstruction using instance segmentation techniques.
We will describe the new implementations of modified Convolutional Neural Networks for anti-neutrino events, single particles and their performance for analysis final states selection, standard candle measurements, and reconstruction.
| Authorship annotation | For the NOvA Collaboration |
|---|---|
| Session and Location | Wednesday Session, Poster Wall #79 (Auditorium Gallery Left) |
| Poster included in proceedings: | yes |
Primary author
Dr
Fernanda Psihas
(University of Texas at Arlington, Indiana University)
Co-author
Mr
Micah Groh
(Indiana University)
