Advancing AI/ML research with public datasets
Project: MicroBooNE public datasets
Fermilab and its affiliated experiments produce large amounts of high-quality scientific data, which can be useful for testing models not originally designed for physics applications, particularly in the field of foundation models. Fermilab actively supports the public use of its datasets for AI and machine learning research. In 2017, the MicroBoone neutrino experiment released a dataset containing over one million neutrino events for public analysis. This dataset has since been used to develop machine learning algorithms capable of handling complex point clouds of events — a challenging task that now serves as a benchmark for AI/ML techniques.
New AI tool to reconstruct neutrino events
Project: NuGraph: Graph neural network for neutrino physics event reconstruction
To analyze data from liquid-argon time projection chambers, scientists at Fermilab have developed a neural network called NuGraph. The system interprets the energy traces left behind by particles in the detector, transforming them into complex graphs where energy depositions are represented as interconnected nodes across multiple layers.
Building on this foundation, NuGraph2 introduces enhanced capabilities for both identifying and classifying particle interactions with greater accuracy.
Using a graph neural network with specialized decoders, NuGraph2 can distinguish true particle signal from cosmic background noise and classify those signals into different types of particle interactions. This innovative approach offers a powerful tool for studying elusive particles like neutrinos.
