Argonne is using artificial intelligence to identify particles traversing 10-story tall particle detector at the Large Hadron Collider in Geneva Switzerland.
Deep learning has enabled state-of-the-art results in high-energy neutrino physics. This application achieves 5x better background particle rejection compared to classical techniques.
At the Advanced Photon Source, scientists developed an artificial intelligence approach that replaces human intuition in identifying spurious data in X-ray diffraction patterns.
An Argonne team has developed a machine learning approach for calibrating the center of rotation in x-ray light source tomography data that provides better accuracy than conventional imaging processing-based methods.
From studying a sea slug, researchers have demonstrated a fundamental type of learning in an inorganic system that may serve as a building block for neuromorphic computing and AI applications.
A new machine learning technique that uses data from high-energy X-ray diffraction experiments will significantly reduce model development time and human effort.