Baikal-GVD: status and first results

Safronov, G.

doi:10.22323/1.390.0606

Cited by 7 publications

(1 citation statement)

References 7 publications

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“…Among these problems, the first one would be overcomed with the operations of the next-generation neutrino telescopes [20][21][22][23][24] in the next two decades. Furthermore, because the energy spectrum and the flavor ratio of HANS at the source are determined by the production mechanism, the progress of the first one would also provide clues for the last one.…”

Section: Introductionmentioning

confidence: 99%

Matter effects on flavor transitions of high-energy astrophysical neutrinos based on different decoherence schemes

Xu¹,

Rong²

2022

Preprint

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The progress of neutrino astronomy makes the precise measurement of the flavor ratio of high energy astronomical neutrinos (HANs) possible in the near future. Then matter effects and new physics effects on the flavor transition of HANs could be tested by the nextgeneration neutrino telescopes. In this paper we consider matter effects in gas which is a realistic environment around the promising sources of HANs. The matter effects are dependent on the decoherence schemes and the sources of neutrinos. We examine the predictions on the flavor ratio at Earth for typical sources with five decoherence schemes. For the muondamping source, the matter effects could be identified in the adiabatic schemes. For the pion-decay source, the decoherence scheme with the non-negligible oscillation effects would be stringently constrained by the observations of the next-generation neutrino telescopes. So the precise measurement of the flavor ratio would provide information on the propagation schemes and the clues on the production mechanism of HANs.

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Section: Introductionmentioning

confidence: 99%

Matter effects on flavor transitions of high-energy astrophysical neutrinos based on different decoherence schemes

Xu¹,

Rong²

2022

Preprint

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The future of high-energy astrophysical neutrino flavor measurements

Song

Argüelles

et al. 2021

J. Cosmol. Astropart. Phys.

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We critically examine the ability of future neutrino telescopes, including Baikal-GVD, KM3NeT, P-ONE, TAMBO, and IceCube-Gen2, to determine the flavor composition of high-energy astrophysical neutrinos in light of data from next-generation of neutrino oscillation experiments including JUNO, DUNE, and Hyper-Kamiokande. By 2040, the region of allowed flavor composition at Earth will shrink ten-fold, and the flavor composition at the astrophysical sources of the neutrinos will be inferred to within 6%, enough to pinpoint the dominant neutrino production mechanism and to identify possible sub-dominant mechanisms. These conclusions hold even in the nonstandard scenario where neutrino mixing is non-unitary, a scenario that will be probed in next-generation experiments such as the IceCube-Upgrade. As an illustration, we show that future experiments are sensitive to decay rates of the heavier neutrinos to below 1.8 × 10-5 (m/eV) s-1 at 95% credibility by 2040.

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Two Watts is all you need: enabling in-detector real-time machine learning for neutrino telescopes via edge computing

Jin,

Hu,

Argüelles

2024

J. Cosmol. Astropart. Phys.

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The use of machine learning techniques has significantly increased the physics discovery potential of neutrino telescopes. In the upcoming years, we are expecting upgrades of currently existing detectors and new telescopes with novel experimental hardware, yielding more statistics as well as more complicated data signals. This calls for an upgrade on the software side needed to handle this more complex data in a more efficient way. Specifically, we seek low power and fast software methods to achieve real-time signal processing, where current machine learning methods are too expensive to be deployed in the resource-constrained regions where these experiments are located. We present the first attempt at and a proof-of-concept for enabling machine learning methods to be deployed in-detector for water/ice neutrino telescopes via quantization and deployment on Google Edge Tensor Processing Units (TPUs). We design a recursive neural network with a residual convolutional embedding and adapt a quantization process to deploy the algorithm on a Google Edge TPU. This algorithm can achieve similar reconstruction accuracy compared with traditional GPU-based machine learning solutions while requiring the same amount of power compared with CPU-based regression solutions, combining the high accuracy and low power advantages and enabling real-time in-detector machine learning in even the most power-restricted environments.

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Baikal-GVD: status and first results

Cited by 7 publications

References 7 publications

Matter effects on flavor transitions of high-energy astrophysical neutrinos based on different decoherence schemes

Matter effects on flavor transitions of high-energy astrophysical neutrinos based on different decoherence schemes

The future of high-energy astrophysical neutrino flavor measurements

Two Watts is all you need: enabling in-detector real-time machine learning for neutrino telescopes via edge computing

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