Results from the ARIANNA high-energy neutrino detector

Gläser, Christian

doi:10.22323/1.424.0003

Cited by 2 publications

(3 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the next section, the expected gain in sensitivity is studied for a lower threshold of 3.6 SNR, which corresponds to 100Hz, the maximum operation rate of the stations. For more information on the ARIANNA detector, see [6,31].…”

Section: Detector Descriptionmentioning

confidence: 99%

“…While these experiments showed the technical feasibility, they were too small to measure the low neutrino flux. Undeterred, several radio-based experiments in development are further illustrating the capabilities of this detection method, such as ARIANNA-200 [6], the radio component of IceCube-Gen2 [7], the Radio Neutrino Observatory in Greenland (RNO-G) [8], Giant Radio Array for Neutrino Detection (GRAND) [9], Taiwan Astroparticle Radiowave Observatory for Geo-synchrotron Emissions (TAROGE) [10], and Payload for Ultrahigh Energy Observations (PUEO) [11], a successor to ANITA [12]. These experiments exploit various target materials such as ice, water, mountains, and air.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Anker¹,

Baldi²,

Barwick³

et al. 2022

J. Inst.

View full text Add to dashboard Cite

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.

show abstract

Section: Detector Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Anker¹,

Baldi²,

Barwick³

et al. 2022

J. Inst.

View full text Add to dashboard Cite

show abstract

“…The ARIANNA experiment has also given rise to the development of innovative simulation and reconstruction tools [13][14][15] that were validated in in-situ measurements or through the measurement of cosmic rays that serve as JCAP10(2023)060 a test beam for neutrino signals [11,[16][17][18][19]. A recent summary of previous results obtained by the ARIANNA experiment is provided in [20].…”

Section: Introductionmentioning

confidence: 99%

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

Anker,

Baldi,

Barwick

et al. 2023

J. Cosmol. Astropart. Phys.

View full text Add to dashboard Cite

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.

show abstract

Results from the ARIANNA high-energy neutrino detector

Cited by 2 publications

References 24 publications

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

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

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

Contact Info

Product

Resources

About