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Probing astrophysical GeV neutrino emissions with Ice-Cube and KM3NeT

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In the last decade, Cherenkov neutrino telescopes have provided valuable insights into the sources and acceleration mechanisms responsible for the high-energy neutrino flux observed at Earth. These instruments utilise large volumes of naturally occurring optically transparent materials, such as the Antarctic ice for IceCube and the Mediterranean Sea water for KM3NeT. Specifically, IceCube, encompassing a cubic kilometre of glacial ice, and KM3NeT, currently being deployed and soon reaching a similar size, offer complementary sky coverage, ushering in a new era of neutrino astronomy. Although both are optimised for detecting TeV to PeV neutrinos, recent advancements in analysis techniques have lowered the energy threshold and increased sensitivity to astrophysical GeV neutrinos. Despite high background rates at low energies, the large instrumented volumes allow for good sensitivity to transient sources, which in turn can be used to constrain theoretical flux predictions. We examine the case of GRB 221009A and the follow-up analysis of the observing runs of LIGO and Virgo. Furthermore, we discuss ongoing efforts to enhance these sensitivities through dedicated machine learning techniques aimed at improving signal-to-noise discrimination down to 100 MeV.
Title: Probing astrophysical GeV neutrino emissions with Ice-Cube and KM3NeT
Description:
In the last decade, Cherenkov neutrino telescopes have provided valuable insights into the sources and acceleration mechanisms responsible for the high-energy neutrino flux observed at Earth.
These instruments utilise large volumes of naturally occurring optically transparent materials, such as the Antarctic ice for IceCube and the Mediterranean Sea water for KM3NeT.
Specifically, IceCube, encompassing a cubic kilometre of glacial ice, and KM3NeT, currently being deployed and soon reaching a similar size, offer complementary sky coverage, ushering in a new era of neutrino astronomy.
Although both are optimised for detecting TeV to PeV neutrinos, recent advancements in analysis techniques have lowered the energy threshold and increased sensitivity to astrophysical GeV neutrinos.
Despite high background rates at low energies, the large instrumented volumes allow for good sensitivity to transient sources, which in turn can be used to constrain theoretical flux predictions.
We examine the case of GRB 221009A and the follow-up analysis of the observing runs of LIGO and Virgo.
Furthermore, we discuss ongoing efforts to enhance these sensitivities through dedicated machine learning techniques aimed at improving signal-to-noise discrimination down to 100 MeV.

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