| 1. |
- Abbasi, R., et al.
(författare)
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Characterization of the astrophysical diffuse neutrino flux using starting track events in IceCube
- 2024
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Ingår i: Physical Review D - Particles, Fields, Gravitation and Cosmology. - 2470-0010 .- 2470-0029. ; 110:2
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Tidskriftsartikel (refereegranskat)abstract
- A measurement of the diffuse astrophysical neutrino spectrum is presented using IceCube data collected from 2011-2022 (10.3 years). We developed novel detection techniques to search for events with a contained vertex and exiting track induced by muon neutrinos undergoing a charged-current interaction. Searching for these starting track events allows us to not only more effectively reject atmospheric muons but also atmospheric neutrino backgrounds in the southern sky, opening a new window to the sub-100 TeV astrophysical neutrino sky. The event selection is constructed using a dynamic starting track veto and machine learning algorithms. We use this data to measure the astrophysical diffuse flux as a single power law flux (SPL) with a best-fit spectral index of γ=2.58-0.09+0.10 and per-flavor normalization of φper-flavorAstro=1.68-0.22+0.19×10-18×GeV-1 cm-2 s-1 sr-1 (at 100 TeV). The sensitive energy range for this dataset is 3-550 TeV under the SPL assumption. This data was also used to measure the flux under a broken power law, however we did not find any evidence of a low energy cutoff.
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| 2. |
- Abbasi, R., et al.
(författare)
-
Citizen science for IceCube: Name that Neutrino
- 2024
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Ingår i: European Physical Journal Plus. - 2190-5444. ; 139:6
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Tidskriftsartikel (refereegranskat)abstract
- Name that Neutrino is a citizen science project where volunteers aid in classification of events for the IceCube Neutrino Observatory, an immense particle detector at the geographic South Pole. From March 2023 to September 2023, volunteers did classifications of videos produced from simulated data of both neutrino signal and background interactions. Name that Neutrino obtained more than 128,000 classifications by over 1800 registered volunteers that were compared to results obtained by a deep neural network machine-learning algorithm. Possible improvements for both Name that Neutrino and the deep neural network are discussed.
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| 3. |
- Abbasi, R., et al.
(författare)
-
Improved modeling of in-ice particle showers for IceCube event reconstruction
- 2024
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Ingår i: Journal of Instrumentation. - 1748-0221. ; 19:6
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Tidskriftsartikel (refereegranskat)abstract
- The IceCube Neutrino Observatory relies on an array of photomultiplier tubes to detect Cherenkov light produced by charged particles in the South Pole ice. IceCube data analyses depend on an in-depth characterization of the glacial ice, and on novel approaches in event reconstruction that utilize fast approximations of photoelectron yields. Here, a more accurate model is derived for event reconstruction that better captures our current knowledge of ice optical properties. When evaluated on a Monte Carlo simulation set, the median angular resolution for in-ice particle showers improves by over a factor of three compared to a reconstruction based on a simplified model of the ice. The most substantial improvement is obtained when including effects of birefringence due to the polycrystalline structure of the ice. When evaluated on data classified as particle showers in the high-energy starting events sample, a significantly improved description of the events is observed.
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