| Title: |
Machine Learning Methods for Solar Neutrino Classification |
| Authors: |
Alejandro Yankelevich |
| Publisher Information: |
Zenodo |
| Publication Year: |
2022 |
| Collection: |
Zenodo |
| Description: |
Super-Kamiokande has observed boron-8 solar neutrino recoil electrons at kinetic energies as low as 3.49 MeV to study neutrino flavor conversion within the sun. At SK-observable energies, these conversions are dominated by the Mikheyev–Smirnov–Wolfenstein effect. An upturn in the electron survival probability in which vacuum neutrino oscillations become dominant is predicted to occur at lower energies, but radioactive background increases exponentially with decreasing energy. New machine learning approaches provide substantial background reduction in the 2.49 MeV - 3.49 MeV energy region such that statistical extraction of solar neutrino interactions becomes feasible. The solar angle distributions of events selected by a ResNet convolutional neural network trained on event display images as well as a boosted decision tree trained on reconstructed variables used in the SK solar analysis will be presented. |
| Document Type: |
text |
| Language: |
unknown |
| Relation: |
https://zenodo.org/communities/neutrino2022-posters/; https://zenodo.org/records/6759244; oai:zenodo.org:6759244; https://doi.org/10.5281/zenodo.6759244 |
| DOI: |
10.5281/zenodo.6759244 |
| Availability: |
https://doi.org/10.5281/zenodo.6759244; https://zenodo.org/records/6759244 |
| Rights: |
Creative Commons Attribution 4.0 International ; cc-by-4.0 ; https://creativecommons.org/licenses/by/4.0/legalcode |
| Accession Number: |
edsbas.DB0C101E |
| Database: |
BASE |