Speaker
Description
The Deep Underground Neutrino Experiment (DUNE) is the flagship next-generation neutrino experiment in the United States, designed to decisively measure neutrino CP violation and determine the neutrino mass hierarchy. DUNE employs Liquid Argon Time Projection Chamber (LArTPC) technology, which provides exceptional spatial resolution and enables detailed reconstruction of final-state particles and neutrino interactions.
Artificial intelligence and machine learning (AI/ML) techniques—such as convolutional neural networks, graph neural networks, and transformers—are being actively developed within DUNE and have already demonstrated strong performance in signal processing, kinematic reconstruction, clustering, and interaction/particle identification. Beyond reconstruction, AI/ML methods are playing an increasingly important role in simulation, trigger and DAQ data processing, beam design and monitoring, documentation search, and quality assurance/quality control (QA/QC). More recently, DUNE has also begun exploring emerging AI approaches, including foundation models, large language models (LLMs), vision–language models (VLMs), and agentic AI systems. At the infrastructure level, DUNE extensively leverages leadership-class and distributed scientific computing infrastructures to support large-scale AI/ML workflows.
In this talk, I will review recent AI/ML advances and computing infrastructure in DUNE.
| Contribution types | Long talk (30min + 10min Q/A) |
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