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- Niemi, MEK, et al.
(author)
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- 2021
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swepub:Mat__t
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| 2. |
- Murari, A., et al.
(author)
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A control oriented strategy of disruption prediction to avoid the configuration collapse of tokamak reactors
- 2024
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In: Nature Communications. - 2041-1723 .- 2041-1723. ; 15:1
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Journal article (peer-reviewed)abstract
- The objective of thermonuclear fusion consists of producing electricity from the coalescence of light nuclei in high temperature plasmas. The most promising route to fusion envisages the confinement of such plasmas with magnetic fields, whose most studied configuration is the tokamak. Disruptions are catastrophic collapses affecting all tokamak devices and one of the main potential showstoppers on the route to a commercial reactor. In this work we report how, deploying innovative analysis methods on thousands of JET experiments covering the isotopic compositions from hydrogen to full tritium and including the major D-T campaign, the nature of the various forms of collapse is investigated in all phases of the discharges. An original approach to proximity detection has been developed, which allows determining both the probability of and the time interval remaining before an incoming disruption, with adaptive, from scratch, real time compatible techniques. The results indicate that physics based prediction and control tools can be developed, to deploy realistic strategies of disruption avoidance and prevention, meeting the requirements of the next generation of devices.
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| 9. |
- Abbasi, R., et al.
(author)
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Citizen science for IceCube: Name that Neutrino
- 2024
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In: European Physical Journal Plus. - 2190-5444. ; 139:6
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Journal article (peer-reviewed)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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| 10. |
- Abbasi, R., et al.
(author)
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Characterization of the astrophysical diffuse neutrino flux using starting track events in IceCube
- 2024
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In: Physical Review D - Particles, Fields, Gravitation and Cosmology. - 2470-0010 .- 2470-0029. ; 110:2
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Journal article (peer-reviewed)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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