Computational science for accelerators

Computational science is the study of numerical methods and computing resources to address scientific problems. Nearly all of the research at the FRIB Laboratory engages in some aspect of computational science. This includes the use of high-performance computing for:

  • calculations of the structure and dynamics of nuclei,
  • machine learning for fast particle identification,
  • computational modelling to determine detector efficiencies and background rates, and
  • simulations of the performance of the FRIB linear accelerator (linac)

High-performance computing

In addition to on-site computing resources, researchers at FRIB take advantage of high-performance computing resources at the Institute for Cyber-Enabled Research at Michigan State University. They also participate in numerous research collaborations, including:

Machine learning and Artificial intelligence

Researchers at FRIB use algorithms such as neural networks, Gaussian processes, clustering algorithms, and dimensionality reduction to:

  • improve calculations of nuclear structure and reactions and provide estimates of model uncertainties,
  • perform fast particle identification, and
  • enable automated beam-tuning and optimization for the FRIB linac.

Quantum Computing

Quantum computing is a new computational paradigm. It uses the properties of quantum states to enable calculations that might otherwise be impossible using classical computers. FRIB researchers are developing quantum algorithms to solve the nuclear many-body problem.

Nuclear data effort

The nuclear data effort at FRIB provides current, accurate, authoritative data for workers in pure and applied areas of nuclear science and engineering, and to address gaps in the body of available data through targeted experimental studies and the use of theoretical models.