Speaker
Description
The ICARUS T600 detector is a liquid argon time projection chamber (LArTPC) installed at Fermilab, aimed towards a sensitive search for possible electron neutrino excess in the 200-600 MeV region. To investigate electron neutrino appearance signals in ICARUS, a fast and accurate algorithm for selecting electron neutrino events from a background of cosmic interactions is required. We present an application of the general-purpose deep learning based reconstruction algorithm developed at SLAC to the task of electron neutrino reconstruction in the ICARUS detector. We demonstrate its effectiveness using the ICARUS detector simulation dataset containing electron neutrino events and out-of-time cosmic interactions generated using the CORSIKA software. In addition, we compare the selection efficiency/purity and reconstructed energy resolution across different initial neutrino energy ranges, and discuss current efforts to improve reconstruction of low energy neutrino events.
| Poster prize | Yes |
|---|---|
| Given name | Dae Heun |
| Surname | Koh |
| First affiliation | Stanford University |
| Second affiliation | SLAC National Accelerator Laboratory |
| Institutional email | koh0207@stanford.edu |
| Gender | Male |
| Collaboration (if any) | ICARUS |
