The Calibration of the Geometry and Antenna delay in Askaryan Radio Array Station 4 and 5

The Askaryan Radio Array (ARA) experiment at the South Pole is designed to detect the radio signals produced by ultra high energy cosmic neutrino interactions in the ice. There are 5 independent ARA stations, one of which (A5) includes a low-threshold phased array trigger string. Each ARA station is designed to work as an autonomous detector. The Data Acquisition System in all ARA stations is equipped with the Ice Ray Sampler second-generation (IRS2) chip, a custom-made, application-specific integrated circuit (ASIC) for high-speed sampling and digitization. In this contribution, we describe the methodology used to calibrate the IRS2 digitizer chip and the station geometry, namely the relative timing between each pair of ARA antennas, deployed at 200 m below the Antarctic ice surface, and their geometrical positions in the ice, for ARA stations 4 and 5. Our calibration allows for proper timing correlations between incoming signals, which is crucial for radio vertex reconstruction and thus detection of ultra high energy neutrinos through the Askaryan effect. We achieve a signal timing precision on a sub-nanosecond level and an antenna position precision within 10 cm

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

The Askaryan Radio Array (ARA) is an ultra-high energy (UHE) neutrino (Eν > 1017 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. 0.1 ms/event compared to 10000 ms/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

A Template-based UHE Neutrino Search Strategy for the Askaryan Radio Array (ARA)

The Askaryan Radio Array (ARA) is a gigaton-size neutrino radio telescope located near the geographic South Pole. ARA has five independent stations designed to detect Askaryan emission coming from the interactions between ultra-high energy neutrinos (> 10 PeV) and Antarctic ice. Each station includes of 16 antenna deployed in a matrix shape at up to 200 m deep in the ice. A simulated neutrino template, including the detector response model, was implemented in a new search technique for reducing background noise and improving the vertex reconstruction resolution. The template is used to scan through the data using the matched filter method, inspired by LIGO, looking for a low SNR neutrino signature and ultimately aiming to lower the detector’s energy threshold at the analysis level. I will present the estimated sensitivity improvements to ARA analyses through the application of the template technique with results from simulation

Implementing a Low-Threshold Analysis with the Askaryan Radio Array (ARA)

The Askaryan Radio Array (ARA) is a ground-based radio detector at the South Pole designed to capture Askaryan emission from ultra-high energy neutrinos interacting within the Antarctic ice. The newest ARA station has been equipped with a phased array trigger, in which radio signals in multiple antennas are summed in predetermined directions prior to the trigger. In this way, impulsive signals add coherently, while noise likely does not, allowing the trigger threshold to be lower than a traditional ARA station. Early results on just a fraction of available data from this new system prove the feasibility of a low-threshold analysis