A neural network-based ultra-high energy neutrino reconstruction method with the Askaryan Radio Array
| Author(s) | Pan, Yue | |
| Date Accessioned | 2022-04-26T12:23:00Z | |
| Date Available | 2022-04-26T12:23:00Z | |
| Publication Date | 2022 | |
| SWORD Update | 2022-03-15T22:02:50Z | |
| Abstract | Ultra-high energy (UHE) neutrinos ($E_{\nu} > 10^{17}$~eV) are important messenger particles that can carry information about the most intense processes in the Universe, far beyond any experiment that human beings can do on Earth. They rarely interact with other particles, thus are not deflected or attenuated during propagation. Together with cosmic rays, gamma rays, and gravitational waves, an exciting new field of multi-messenger astronomy is growing. ☐ The Askaryan Radio Array (ARA) is an ultra-high energy (UHE) neutrino detector at the South Pole. ARA aims to utilize radio signals detected from UHE neutrino interactions in the glacial ice to infer properties about the interaction vertex as well as the incident neutrino. To retrieve these properties from data, the first step is to extract timing, amplitude, and frequency information from waveforms of different antennas buried in the deep ice. These features can then be utilized to reconstruct the neutrino interaction vertex position, incoming neutrino direction, and shower energy. So far, a vertex can be reconstructed through an interferometry technique while neutrino direction reconstruction is still under investigation. ☐ In this dissertation, I will present a reconstruction method based on multi-task deep neural networks which can perform reconstruction of both vertex and incoming neutrino direction with reasonable precision. It can achieve a comparable performance compared to a classic interferometric technique in vertex direction reconstruction, but can also reconstruct vertex distance and neutrino direction that are not achievable with an interferometric technique. After training, this solution is capable of rapid reconstructions (e.g. 0.1~ms/event compared to 10000~ms/event in a conventional interferometric routine) useful for trigger and filter decisions and can be easily generalized to different station configurations for both design and analysis purposes. The model has also be tested on the 2018 deep pulser data set and 2018 SpiceCore data set for its applicability to experimental data. | en_US |
| Advisor | Seckel, David | |
| Degree | Ph.D. | |
| Department | University of Delaware, Department of Physics and Astronomy | |
| DOI | https://doi.org/10.58088/2m74-2r89 | |
| Unique Identifier | 1312643785 | |
| URL | https://udspace.udel.edu/handle/19716/30817 | |
| Language | en | |
| Publisher | University of Delaware | en_US |
| URI | https://login.udel.idm.oclc.org/login?url=https://www.proquest.com/dissertations-theses/neural-network-based-ultra-high-energy-neutrino/docview/2642802940/se-2?accountid=10457 | |
| Keywords | Askaryan Radio Array | |
| Keywords | Astroparticle physics | |
| Keywords | Neural network | |
| Keywords | Neutrino | |
| Title | A neural network-based ultra-high energy neutrino reconstruction method with the Askaryan Radio Array | en_US |
| Type | Thesis | en_US |