Summary
While it is currently undergoing rapid developments, the neutrino sector still has many open questions: the neutrino mass
hierarchy is not known, several parameters of the PMNS matrix are poorly constrained, and the inability to explain the nonzero neutrino masses is a clear indication of physics beyond the Standard Model. Neutrino oscillation experiments at the
IceCube Neutrino Observatory may be able to address these fundamental questions, but the reconstruction of neutrino
interactions in the detector is a challenge which urgently needs to be addressed: the current reconstruction algorithm is
prohibitively time-consuming, cannot account for all known optical anisotropies in the ice, and cannot make full use of all of
the information from new modules in the IceCube Upgrade due to excessive computing time and memory requirements. This
project proposes graph convolutional neural networks (GCN) as a machine learning paradigm excellently suited for neutrino
telescope experiments, with potential to revolutionise reconstruction in IceCube. GCNs impose no structural requirements on
data, requiring only a concept of adjacency, naturally afforded by the spatial, temporal, and causal separation of hits in the
detector. With expected improvements in particle identification of a factor of 10 compared to analytical methods and a factor
10,000 speed-up in reconstruction, GCN-based reconstruction will be developed and implemented in the IceCube-DeepCore
oscillation analysis, to better measure PMNS parameters by improving the atmospheric muon background rejection and
performing per-flavour event categorisation. Powerful and fast GCN-based reconstruction will benefit several physics
analyses in IceCube --- and possibly ANTARES, KM3NeT, and Baikal-GVD --- and help answer the open questions in the
neutrino sector. Finally, the possibility for private and public sector partners to benefit from these high-performance GCN
tools will be explored through intersectional partnerships.
hierarchy is not known, several parameters of the PMNS matrix are poorly constrained, and the inability to explain the nonzero neutrino masses is a clear indication of physics beyond the Standard Model. Neutrino oscillation experiments at the
IceCube Neutrino Observatory may be able to address these fundamental questions, but the reconstruction of neutrino
interactions in the detector is a challenge which urgently needs to be addressed: the current reconstruction algorithm is
prohibitively time-consuming, cannot account for all known optical anisotropies in the ice, and cannot make full use of all of
the information from new modules in the IceCube Upgrade due to excessive computing time and memory requirements. This
project proposes graph convolutional neural networks (GCN) as a machine learning paradigm excellently suited for neutrino
telescope experiments, with potential to revolutionise reconstruction in IceCube. GCNs impose no structural requirements on
data, requiring only a concept of adjacency, naturally afforded by the spatial, temporal, and causal separation of hits in the
detector. With expected improvements in particle identification of a factor of 10 compared to analytical methods and a factor
10,000 speed-up in reconstruction, GCN-based reconstruction will be developed and implemented in the IceCube-DeepCore
oscillation analysis, to better measure PMNS parameters by improving the atmospheric muon background rejection and
performing per-flavour event categorisation. Powerful and fast GCN-based reconstruction will benefit several physics
analyses in IceCube --- and possibly ANTARES, KM3NeT, and Baikal-GVD --- and help answer the open questions in the
neutrino sector. Finally, the possibility for private and public sector partners to benefit from these high-performance GCN
tools will be explored through intersectional partnerships.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/890778 |
| Start date: | 01-09-2021 |
| End date: | 30-08-2025 |
| Total budget - Public funding: | 207 312,00 Euro - 207 312,00 Euro |
Cordis data
Original description
While it is currently undergoing rapid developments, the neutrino sector still has many open questions: the neutrino masshierarchy is not known, several parameters of the PMNS matrix are poorly constrained, and the inability to explain the nonzero neutrino masses is a clear indication of physics beyond the Standard Model. Neutrino oscillation experiments at the
IceCube Neutrino Observatory may be able to address these fundamental questions, but the reconstruction of neutrino
interactions in the detector is a challenge which urgently needs to be addressed: the current reconstruction algorithm is
prohibitively time-consuming, cannot account for all known optical anisotropies in the ice, and cannot make full use of all of
the information from new modules in the IceCube Upgrade due to excessive computing time and memory requirements. This
project proposes graph convolutional neural networks (GCN) as a machine learning paradigm excellently suited for neutrino
telescope experiments, with potential to revolutionise reconstruction in IceCube. GCNs impose no structural requirements on
data, requiring only a concept of adjacency, naturally afforded by the spatial, temporal, and causal separation of hits in the
detector. With expected improvements in particle identification of a factor of 10 compared to analytical methods and a factor
10,000 speed-up in reconstruction, GCN-based reconstruction will be developed and implemented in the IceCube-DeepCore
oscillation analysis, to better measure PMNS parameters by improving the atmospheric muon background rejection and
performing per-flavour event categorisation. Powerful and fast GCN-based reconstruction will benefit several physics
analyses in IceCube --- and possibly ANTARES, KM3NeT, and Baikal-GVD --- and help answer the open questions in the
neutrino sector. Finally, the possibility for private and public sector partners to benefit from these high-performance GCN
tools will be explored through intersectional partnerships.
Status
TERMINATEDCall topic
MSCA-IF-2019Update Date
28-04-2024
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