Standard deep learning models for classification and regression applications are ideal for capturing complex system dynamics. However, their predictions can be arbitrarily inaccurate when the input samples are not similar to the training data. Implementation of distance aware uncertainty estimation can be used to detect these scenarios and provide a level of confidence associated with their predictions. In this paper, we present results from using Deep Gaussian Process Approximation (DGPA) methods for errant beam prediction at Spallation Neutron Source (SNS) accelerator (classification) and we provide an uncertainty aware surrogate model for the Fermi National Accelerator Lab (FNAL) Booster Accelerator Complex (regression).

The thrust of nuclear physics is studying the universe down to its smallest subatomic parts. Now, two physicists at the Department of Energy's Thomas Jefferson National Accelerator Facility have secured more than $2 million in federal funding dedicated to research projects that harness the power of data analytics to make that work faster and more efficient. David Lawrence and Chris Tennant are among 14 scientists at seven DOE national laboratories whose proposals were awarded a total of $37 million to be allocated over three years. "Artificial Intelligence and machine learning have the potential to transform a host of scientific disciplines and to revolutionize experimentation and operations at user facilities in the coming years," Chris Fall, director of DOE's Office of Science, said in announcing the funding. "These awards will help ensure America remains on the cutting edge of these critical technologies for science."

NEWPORT NEWS, Va., Jan. 30, 2020 – More than 1,600 nuclear physicists worldwide depend on the Continuous Electron Beam Accelerator Facility for their research. Located at the Department of Energy's Thomas Jefferson National Accelerator Facility in Newport News, Va., CEBAF is a DOE User Facility that is scheduled to conduct research for limited periods each year, so it must perform at its best during each scheduled run. But glitches in any one of CEBAF's tens of thousands of components can cause the particle accelerator to temporarily fault and interrupt beam delivery, sometimes by mere seconds but other times by many hours. Now, accelerator scientists are turning to machine learning in hopes that they can more quickly recover CEBAF from faults and one day even prevent them. Anna Shabalina is a Jefferson Lab staff member and principal investigator on the project, which has been funded by the Laboratory Directed Research & Development program for the fiscal year 2020.

IMAGE: Fanelli, who is currently a postdoctoral researcher at the Massachusetts Institute of Technology, is the winner of the 2018 Jefferson Science Associates Postdoctoral Prize for his project to use artificial... view more In the world of computing, there's a groundswell of excitement for what is perceived as the impending revolution in artificial intelligence. Like the industrial revolution in the 19th century and the digital revolution in the 20th, the AI revolution is expected to change the way we live and work. Now, Cristiano Fanelli aims to bring the AI revolution to nuclear physics. Fanelli, who is currently a postdoctoral researcher at the Massachusetts Institute of Technology, is the winner of the 2018 Jefferson Science Associates Postdoctoral Prize for his project to use artificial intelligence to optimize systems for nuclear physics research being carried out at the U.S. Department of Energy's Thomas Jefferson National Accelerator Facility. "It's an exciting time to do nuclear and particle physics research with the artificial intelligence revolution happening now," said Fanelli.