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Sökning: WFRF:(Plaisier I) > (2023)

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1.
  • Aguilar, J. A., et al. (författare)
  • Triboelectric backgrounds to radio-based polar ultra-high energy neutrino (UHEN) experiments
  • 2023
  • Ingår i: Astroparticle physics. - : Elsevier. - 0927-6505 .- 1873-2852. ; 145
  • Tidskriftsartikel (refereegranskat)abstract
    • In the hopes of observing the highest-energy neutrinos (E> 1 EeV) populating the Universe, both past (RICE, AURA, ANITA) and current (RNO-G, ARIANNA, ARA and TAROGE-M) polar-sited experiments exploit the impulsive radio emission produced by neutrino interactions. In such experiments, rare single event candidates must be unambiguously identified above backgrounds. Background rejection strategies to date primarily target thermal noise fluctuations and also impulsive radio-frequency signals of anthropogenic origin. In this paper, we consider the possibility that 'fake' neutrino signals may also be generated naturally via the `triboelectric effect' This broadly describes any process in which force applied at a boundary layer results in displacement of surface charge, leading to the production of an electrostatic potential difference AV. Wind blowing over granular surfaces such as snow can induce such a potential difference, with subsequent coronal discharge. Discharges over timescales as short as nanoseconds can then lead to radio-frequency emissions at characteristic MHz-GHz frequencies. Using data from various past (RICE, AURA, SATRA, ANITA) and current (RNO G, ARIANNA and ARA) neutrino experiments, we find evidence for such backgrounds, which are generally characterized by: (a) a threshold wind velocity which likely depends on the experimental trigger criteria and layout; for the experiments considered herein, this value is typically O(10 m/s), (b) frequency spectra generally shifted to the low-end of the frequency regime to which current radio experiments are typically sensitive (100-200 MHz), (c) for the strongest background signals, an apparent preference for discharges from above-surface structures, although the presence of more isotropic, lower amplitude triboelectric discharges cannot be excluded.
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2.
  • Anker, A., et al. (författare)
  • Developing new analysis tools for near surface radio-based neutrino detectors
  • 2023
  • Ingår i: Journal of Cosmology and Astroparticle Physics. - : Institute of Physics Publishing (IOPP). - 1475-7516. ; :10
  • Tidskriftsartikel (refereegranskat)abstract
    • 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 pre-viously 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 meth-ods 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 re-moved 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 require-ments of a large scale future array, as considered in baseline design of the radio component of IceCube-Gen2.
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