Jul 12 – 23, 2021
Europe/Berlin timezone

A neural network based UHE neutrino reconstruction method for the Askaryan Radio Array (ARA)

Jul 15, 2021, 6:00 PM
1h 30m


Poster NU | Neutrinos & Muons Discussion


Yue Pan (University of Delaware)


The Askaryan Radio Array (ARA) is an ultra-high energy (UHE) neutrino (E_nu > 10^17 eV) detector at 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 experiment 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 in a neural network to reconstruct the neutrino interaction vertex position, incoming neutrino direction and shower energy. So far, vertex can be reconstructed through interferometry while neutrino reconstruction is still under investigation. Here I will present a solution based on multi-task deep neural networks which can perform reconstruction of both vertex and incoming neutrinos with a reasonable precision. After training, this solution is capable of rapid reconstructions (e.g. 0.1ms/event compared to 10000ms/event in a conventional routine) useful for trigger and filter decisions, and can be easily generalized to different station configurations for both design and analysis purposes.

Subcategory Experimental Results
Collaboration other (fill field below)
other Collaboration ARA

Primary authors

Yue Pan (University of Delaware) For the ARA Collaboration

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