Speaker
Description
We propose to leverage artificial intelligence to advance event reconstruction in neutrino detectors. The first focus of the project is on atmospheric neutrino interactions in liquid argon detectors such as DUNE. These events often involve invisible particles like neutrons, yet kinematic correlations between visible and invisible final states enable robust reconstruction of the energy and direction of the primary neutrino. Building on our recent theoretical study (https://arxiv.org/abs/arXiv:2405.15867), which demonstrated promising results with a simple multi-layer perceptron, and on experimental benchmarks from MicroBooNE (https://arxiv.org/abs/2504.17758), we will develop AI-based methods to enhance energy and angular resolution in liquid argon time projection chambers.
In the second part, we turn to neutrino telescopes such as IceCube and KM3NeT. Traditional analyses classify events only as track-like ($\nu_\mu$ CC) or shower-like ($\nu_e$ CC, $\nu_\tau$ CC, NC). While IceCube has already applied machine learning to improve flavor identification, we aim to go further by developing AI techniques for a more detailed classification of event substructure. In particular, we will explore discrimination between neutrino and antineutrino interactions, charged- and neutral-current events, as well as signatures of physics beyond the Standard Model.
Together, these studies will establish AI as a powerful framework for extracting maximal information from the diverse range of neutrino observatories.
CERN group/ Experiment
TH
| Working area | Area 1" Cutting Edge AI for Offline Data Processing |
|---|---|
| Project goals | at least one scientific publication on event reconstruction in LAr (together with the MicroBooNE collaboration); at least one scientific publication on event reconstruction in neutrino telescopes |
| Timeline | 3 years |
| Available person power | 2 staff + 4 external collaborators |
| Additional person power request | 1 Fellow (3 years) |
| Is this an already ongoing activity? | No |