SLAC National Accelerator Laboratory
SMASH Associate partner
Scientists and engineers at SLAC explore how the universe works at the biggest, smallest and fastest scales and invent powerful tools used by researchers around the globe. Since the construction of the linear accelerator in 1966, SLAC has conducted groundbreaking experiments in creating, identifying and studying subatomic particles. SLAC would go on to become a multi-program lab and a world leader in X-ray and ultrafast science, diversifying its scientific mission to include cosmology and astrophysics, materials and environmental sciences, biology, sustainable chemistry and energy research, scientific computing and more.
The Accelerator Neutrino Experiment Group within the Fundamental Physics Directorate at SLAC studies the properties of elusive particles called neutrinos, produced at a particle accelerator. Why is our universe dominated by matter rather than anti-matter? Is there a new type of neutrino that could explain mysterious signals observed by past experiments? What’s the physics behind supernovae and how are neutrinos produced? Our experimental programs aim to answer these questions using particle imaging detectors and advanced data analysis methods including machine learning and statistical inference.
Machine Learning (ML) algorithms are found across all scientific directorates at SLAC, with applications to a wide range of tasks including online data reduction, system controls, simulation, and analysis of big data. An important design principle of ML algorithms is the generalization of learning patterns across different tasks, which motivates shared tool-development and R&D at an inter-directorate level. The Machine Learning Initiatives group within the Technology and Innovation Directorate at SLAC is a hub for ML activities at the lab, providing resources and connections between ML experts and domain scientists.
https://www6.slac.stanford.edu/