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DOE OSTI2021

Fermi National Accelerator Laboratory

Fermilab is America’s premier laboratory for particle physics and accelerator research, funded by the U.S. Department of Energy. Thousands of scientists around the world collaborate with Fermilab on research at the frontiers of discovery.

Source record↗
DOE OSTI2023

Fermilab Report - Fermi National Accelerator Laboratory Monthly Report, June 1979

Fermilab is engaged in several new ventures building for the future of the Laboratory and for excursions into new energy ranges. At the same time, it should be remembered that Fermilab also carries out a vigorous program of particle physics at 400 GeV, or thereabouts, with experimenters from a host of different institutions and a number of different countries. This program, which will be the backbone of Fermilab until the era of TeV physics begins, depends completely on reliable operation of the Fermilab accelerator at the intensities and in the modes needed for experiments. There has not been a review of accelerator operation in these pages since August, 1976, and it seems appropriate to discuss the progress of the accelerator.

Cole, F. T.↗
DOE OSTI2025

AI-Enabled Operations at Fermi Complex: Multivariate Time Series Prediction for Outage Prediction and Diagnosis

The Main Control Room of the Fermilab accelerator complex continuously gathers extensive time-series data from thousands of sensors monitoring the beam. However, unplanned events such as trips or voltage fluctuations often result in beam outages, causing operational downtime. This downtime not only consumes operator effort in diagnosing and addressing the issue but also leads to unnecessary energy consumption by idle machines awaiting beam restoration. The current threshold-based alarm system is reactive and faces challenges including frequent false alarms and inconsistent outage-cause labeling. To address these limitations, we propose an AI-enabled framework that leverages predictive analytics and automated labeling. Using data from $2,703$ Linac devices and $80$ operator-labeled outages, we evaluate state-of-the-art deep learning architectures, including recurrent, attention-based, and linear models, for beam outage prediction. Additionally, we assess a Random Forest-based labeling system for providing consistent, confidence-scored outage annotations. Our findings highlight the strengths and weaknesses of these architectures for beam outage prediction and identify critical gaps that must be addressed to fully harness AI for transitioning downtime handling from reactive to predictive, ultimately reducing downtime and improving decision-making in accelerator management.

Jain, Milan [PNL, Richland] (ORCID:000000021676111↗