7–11 Sept 2025
Teaching Hub 502
Europe/London timezone

Towards continual machine learning for your ever changing accelerator (K. Rajput, JLAB)

TUAI02
9 Sept 2025, 09:30
30m
Teaching Hub 502

Teaching Hub 502

The University of Liverpool 160 Mount Pleasant L3 5TR Liverpool
Invited Oral Presentation MC07: Data Acquisition and Processing Platforms TUA

Speaker

Kishansingh Rajput (Thomas Jefferson National Accelerator Facility, University of Houston)

Description

This talk covers our work on errant beam prognostics at the Spallation Neutron Source (SNS), focusing on the end-to-end process from data collection to the development and deployment of predictive models in specific. A short overview of AIML work done for accelerators and current trends will be presented. We will walk through key steps involved in creating robust Machine Learning (ML) models, including model training, validation, and deployment in an operational setting. In addition to presenting our technical approach, we will share valuable lessons learned, emphasizing the importance of infrastructure to support the continuous adaptation of models to evolving data and system behaviors. This talk will provide insights into the challenges and solutions involved in applying ML to real-world operational environments, with a particular focus on managing data drift and changes in accelerator setup while ensuring model resilience over time.

Funding Agency

This work was supported by the DOE Office of Science, United States under Grant No. DE-SC0009915 (Office of Basic Energy Sciences,Scientific User Facilities Program).

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Author

Kishansingh Rajput (Thomas Jefferson National Accelerator Facility, University of Houston)

Co-authors

Alexander Zhukov (Oak Ridge National Laboratory) Malachi Schram (Thomas Jefferson National Accelerator Facility) Sen Lin (University of Houston) Willem Blokland (Oak Ridge National Laboratory)

Presentation materials

There are no materials yet.