Polarization Reconstruction of Cosmic Rays with the ARIANNA Neutrino Radio Detector

Zhao, L.; Anker, A.; Baldi, Pierre; Barwick, S. W.; Beise, Jakob; Bernhoff, Hans; Besson, D.; Bingefors, N.; Cataldo, Maddalena; Chen, Pisin; Fernández, Daniel; Gaswint, Geoffrey; Gläser, Christian; Hallgren, A.; Hallmann, S.; Hanson, Jordan C.; Klein, S. R.; Kleinfelder, S.; Lahmann, R.; Liu, Jiayi; Magnuson, Mitchell; McAleer, Stephen; Meyers, Zach; Nam, J. W.; Nelles, A.; Novikov, Alexander; Paul, M. P.; Persichilli, Christopher; Plaisier, Ilse; Pyras, Lilly; Rice-Smith, Ryan; Tatar, J.; Wang, Shih-Hao; Arianna,

doi:10.22323/1.395.1156

Cited by 3 publications

(5 citation statements)

References 6 publications

(9 reference statements)

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“…The experimental data from station 52 are gathered from December 2018 to March 2019. Unlike the neutrino data set, these data contain tagged cosmic ray signal [17,37], which are removed from this set. Therefore, this data set is expected to contain only background noise data.…”

Section: Cosmic Ray Station 52mentioning

confidence: 99%

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Developing new analysis tools for near surface radio-based neutrino detectors

Anker,

Baldi,

Barwick

et al. 2023

J. Cosmol. Astropart. Phys.

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The ARIANNA experiment is an Askaryan radio detector designed to measure high-energy neutrino induced cascades within the Antarctic ice. Ultra-high-energy neutrinos above 1016 eV have an extremely low flux, so experimental data captured at trigger level need to be classified correctly to retain as much neutrino signal as possible. We first describe two new physics-based neutrino selection methods, or “cuts”, (the updown and dipole cut) that extend the previously published analysis to a specialized ARIANNA station with 8 antenna channels, which is double the number used in the prior analysis. For a standard trigger with a threshold signal to noise ratio at 4.4, the new cuts produce a neutrino efficiency of > 95% per station-year of operation, while rejecting 99.93% of the background (corresponding to 53 remaining experimental background events). When the new cuts are combined with a previously developed cut using neutrino waveform templates, all background is removed at no change of efficiency. In addition, the neutrino efficiency is extrapolated to 1,000 station-years of operation, obtaining 91%. This work then introduces a new selection method (the deep learning cut) to augment the identification of neutrino events by using deep learning methods and compares the efficiency to the physics-based analysis. The deep learning cut gives 99% signal efficiency per station-year of operation while rejecting 99.997% of the background (corresponding to 2 remaining experimental background events), which are subsequently removed by the waveform template cut at no significant change in efficiency. The results of the deep learning cut were verified using measured cosmic rays which shows that the simulations do not introduce artifacts with respect to experimental data. The paper demonstrates that the background rejection and signal efficiency of near surface antennas meets the requirements of a large scale future array, as considered in baseline design of the radio component of IceCube-Gen2.

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Section: Cosmic Ray Station 52mentioning

confidence: 99%

“…The extracted experimental cosmic ray data mentioned above is another data set in this analysis. Obtained from [17,37], this data set was gathered by making various cuts on trigger rate, correlation, and arrival zenith. In total, there are 85 events in this set called experimental tagged cosmic rays, or E-CR52.…”

Section: Cosmic Ray Station 52mentioning

confidence: 99%

Developing new analysis tools for near surface radio-based neutrino detectors

Anker,

Baldi,

Barwick

et al. 2023

J. Cosmol. Astropart. Phys.

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“…Two other stations at Moore's Bay and two at the South Pole were configured with eight antennas, which included a mixture of LPDAs and dipoles. These stations were simultaneously sensitive to cosmic rays that interact in the atmosphere [26,27] and neutrinos. The radio signals are digitized and captured using a custom-made chip design known as the SST [28].…”

Section: Detector Descriptionmentioning

confidence: 99%

“…To test the network, a set of cosmic-ray events from ARIANNA station 52, collected between November 2018 and March 2019, are used [27]. For this data, the two of four coincidence logic trigger is set to a threshold of 4.4 SNR, and the event set has an expected purity of > 95%.…”

Section: Jinst 17 P03007mentioning

confidence: 99%

Improving sensitivity of the ARIANNA detector by rejecting thermal noise with deep learning

Anker¹,

Baldi²,

Barwick³

et al. 2022

J. Inst.

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The ARIANNA experiment is an Askaryan detector designed to record radio signals induced by neutrino interactions in the Antarctic ice. Because of the low neutrino flux at high energies (Eν > 1016 eV), the physics output is limited by statistics. Hence, an increase in sensitivity significantly improves the interpretation of data and offers the ability to probe new parameter spaces. The amplitudes of the trigger threshold are limited by the rate of triggering on unavoidable thermal noise fluctuations. We present a real-time thermal noise rejection algorithm that enables the trigger thresholds to be lowered, which increases the sensitivity to neutrinos by up to a factor of two (depending on energy) compared to the current ARIANNA capabilities. A deep learning discriminator, based on a Convolutional Neural Network (CNN), is implemented to identify and remove thermal events in real time. We describe a CNN trained on MC data that runs on the current ARIANNA microcomputer and retains 95% of the neutrino signal at a thermal noise rejection factor of 105, compared to a template matching procedure which reaches only 102 for the same signal efficiency. Then the results are verified in a lab measurement by feeding in generated neutrino-like signal pulses and thermal noise directly into the ARIANNA data acquisition system. Lastly, the same CNN is used to classify cosmic-rays events to make sure they are not rejected. The network classified 102 out of 104 cosmic-ray events as signal.

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“…The gray shaded areas indicate communication periods [34]. Together with the resolution on polarization [39], this translates into a resolution on neutrinos arrival direction.…”

Section: Arianna Direction Reconstructionmentioning

confidence: 99%