H2020Индивидуална стипендия2021–2025

GraphNeT · Graph convolutional neural networks for neutrino telescopes

„Хоризонт 2020“ — Действия „Мария Склодовска-Кюри“

Период: 2021-09-01 → 2025-08-30
Финансиране от ЕС: 207 312 €
Участници: 1
Схема: MSCA-IF

Линиите свързват координатора с партньорите.

Накратко на български

Графовите невронни мрежи се използват за по-бързо и точно разпознаване на взаимодействията на неутрино в обсерваторията IceCube. Това помага за по-доброто разбиране на масата на тези частици и физичните процеси извън сегашния стандартен модел.

Този кратък обзор е генериран от изкуствен интелект

Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.

Резултати накратко

Graph convolutional neural networks for neutrino telescopes

Neutrinos, an elusive particles that comes in three flavours, can change their flavour as they travel through space, a phenomenon called neutrino oscillation. This unique characteristic sets them apart from other particles, and additionally implies that neutrinos are massive. Despite their significance, the neutrino sector of the Standard Model of particle physics remains poorly understood. The fact that neutrinos have mass, for instance, unlike what the Standard Model predicts, hints at physics beyond our current understanding. Neutrino oscillation experiments at the IceCube Neutrino Observatory may help answer this and other big scientific questions like it. However, a major challenge lies in the technical task of efficiently identifying and reconstructing neutrino interaction events in the vast stream of data recorded by IceCube. Existing algorithms for doing so are prohibitively slow and are not compatible with planned detector upgrades. To tackle this challenge, this project aimed to develop novel reconstruction algorithms using machine learning (ML), particularly graph neural networks (GNNs). These algorithms could significantly improve event reconstruction in IceCube and similar experiments and thereby provide better and more accurate data for physics analysis. Despite the project ending early, it successfully showcased the potential of ML and GNNs to enhance event reconstruction in IceCube, surpassing existing algorithms in both accuracy and speed. This breakthrough paves the way for addressing pressing questions in the neutrino sector and beyond.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз

Цел на проекта

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 theIceCube Neutrino Observatory may be able to address these fundamental questions, but the reconstruction of neutrinointeractions in the detector is a challenge which urgently needs to be addressed: the current reconstruction algorithm isprohibitively time-consuming, cannot account for all known optical anisotropies in the ice, and cannot make full use of all ofthe information from new modules in the IceCube Upgrade due to excessive computing time and memory requirements. Thisproject proposes graph convolutional neural networks (GCN) as a machine learning paradigm excellently suited for neutrinotelescope experiments, with potential to revolutionise reconstruction in IceCube. GCNs impose no structural requirements ondata, requiring only a concept of adjacency, naturally afforded by the spatial, temporal, and causal separation of hits in thedetector. With expected improvements in particle identification of a factor of 10 compared to analytical methods and a factor10,000 speed-up in reconstruction, GCN-based reconstruction will be developed and implemented in the IceCube-DeepCoreoscillation analysis, to better measure PMNS parameters by improving the atmospheric muon background rejection andperforming per-flavour event categorisation. Powerful and fast GCN-based reconstruction will benefit several physicsanalyses in IceCube --- and possibly ANTARES, KM3NeT, and Baikal-GVD --- and help answer the open questions in theneutrino sector. Finally, the possibility for private and public sector partners to benefit from these high-performance GCNtools will be explored through intersectional partnerships.

Оригинален текст от CORDIS (на английски).

Участници

KOBENHAVNS UNIVERSITET · KOBENHAVNКоординаторДания