This work demonstrates electronic logbook (eLog) systems leveraging modern AI-driven information retrieval capabilities at the accelerator facilities of Fermilab, Jefferson Lab, Lawrence Berkeley National Laboratory (LBNL), SLAC National Accelerator Laboratory. We evaluate contemporary tools and methodologies for information retrieval with Retrieval Augmented Generation (RAGs), focusing on operational insights and integration with existing accelerator control systems. The study addresses challenges and proposes solutions for state-of-the-art eLog analysis through practical implementations, demonstrating applications and limitations. We present a framework for enhancing accelerator facility operations through improved information accessibility and knowledge management, which could potentially lead to more efficient operations.

A stable, reliable, and controllable orbit lock system is crucial to an electron (or ion) accelerator because the beam orbit and beam energy instability strongly affect the quality of the beam delivered to experimental halls. Currently, when the orbit lock system fails operators must manually intervene. This paper develops a Machine Learning based fault detection methodology to identify orbit lock anomalies and notify accelerator operations staff of the off-normal behavior. Our method is unsupervised, so it does not require labeled data. It uses Long-Short Memory Networks (LSTM) Auto Encoder to capture normal patterns and predict future values of monitoring sensors in the orbit lock system. Anomalies are detected when the prediction error exceeds a threshold. We conducted experiments using monitoring data from Jefferson Lab's Continuous Electron Beam Accelerator Facility (CEBAF). The results are promising: the percentage of real anomalies identified by our solution is 68.6%-89.3% using monitoring data of a single component in the orbit lock control system. The accuracy can be as high as 82%.

Scientists have begun turning to new tools offered by machine learning to help save time and money. In the past several years, nuclear physics has seen a flurry of machine learning projects come online, with many papers published on the subject. Now, 18 authors from 11 institutions summarize this explosion of artificial intelligence-aided work in "Machine Learning in Nuclear Physics," a paper recently published in Reviews of Modern Physics. "It was important to document the work that has been done. We really do want to raise the profile of the use of machine learning in nuclear physics to help people see the breadth of the activities," said Amber Boehnlein, lead author of the paper and the associate director for computational science and technology at the U.S. Department of Energy's Thomas Jefferson National Accelerator Facility.

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."

Jefferson Lab is hosting an A.I. Hack-A-Thon for those interested in learning about artificial intelligence. The purpose is to generate interest in A.I. in the field of nuclear physics by giving participants a free, hands on experience, according to the news release. The event is free and open to the public. The deadline to register is Friday. "The last 10 years have seen explosive growth in the field of A.I." according to the news release.

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.