DOE · DOE-73 · record R3588

Machine Learning for Accelerator Operations Using Big Data Analytics / L-CAPE

Department of Energy · FNAL - Fermi National Accelerator (SC43 OIM)

Overview

Development stage: PilotSource: b) Pilot – The use case has been deployed in a limited test or pilot capacity.
High-impact designation: Not high-impactSource: c) Not high-impact
Impact justification: The use case does not have an effect on civil rights/liberties/privacy, access to education/housing/insurance/credit/employment, access to critical government resources/services, human health/safety, critical infrastructure/public safety
Start date: 2025-09-25 (day precision)Source: 2025-09-25T00:00:00
Withheld from public reporting: NoSource: a) No

Mission

Topic area: Science
Operational functions: Forecasting and prediction Derived by keyword rules; see methodology

Problem the AI is intended to solve

Big data analytics for anomaly prediction and classification, enabling automatic mitigation, operational savings, and predictive maintenance of the Fermilab LINAC

Expected benefits

ML models are deployed for the FNAL's Linac.to detect, label andact upon faults. The usage of ML will jimporve our fault labelingand detection. This will allow for improved operatioal efficeincy, fault statistics, and preventitive maintenance. To my knowledge this is the first global accelerator operations ML system.

System outputs

The ML outputs to a dashboard withfault labels and downtime predictiojns.The model will also try and predict dwwontime and possible actions.

Technology

AI classification: Classical / predictive MLSource: Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relatio…
System name(s): Not reported
Custom-developed code: Yes
Public source code: unknown

Sourcing

How it was built: Developed in-houseSource: b) Developed in-house
Vendor (as reported): Not reported
Vendors (standardized): None on the standard list

A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.

Data and privacy

Involves PII: No
Privacy Impact Assessment: Not reported
Authorization to Operate: No
Demographic features: Not reported
Training and evaluation data: my own simulated data; research datasets from scientific experiments
Federal Data Catalog: Not reported

Governance

0 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.

Pre-deployment testingNo answer reported

AI impact assessmentNo answer reported

Independent reviewNo answer reported

Ongoing monitoringNo answer reported

Operator trainingNo answer reported

Fail-safeNo answer reported

Appeal processNo answer reported

User and public consultationNo answer reported

Potential impacts and how they were identified

Not reported