AI-Ready Control System for the Fermilab Accelerator Complex
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866910070093119488 |
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| author | Miceli, Tia Gottschalk, Erik Tooke, Donovan Milton, Evan Santucci, Robert Hoschouer, Hayden Balcewicz, Michael Case, Jennifer Deshpande, Abhishek Fieldhouse, Kit Ganguly, Sudeshna Harrison, Beau Ibrahim, Aisha Kobilarcik, Thomas Olander, Michael Pathak, Abhishek John, Jason St. Sauers, Aaron |
| author_facet | Miceli, Tia Gottschalk, Erik Tooke, Donovan Milton, Evan Santucci, Robert Hoschouer, Hayden Balcewicz, Michael Case, Jennifer Deshpande, Abhishek Fieldhouse, Kit Ganguly, Sudeshna Harrison, Beau Ibrahim, Aisha Kobilarcik, Thomas Olander, Michael Pathak, Abhishek John, Jason St. Sauers, Aaron |
| contents | Reliable, high-intensity operation of the Fermilab Accelerator Complex is critical to the success of the Long-Baseline Neutrino Facility and Deep Underground Neutrino Experiment. We describe the requirements and infrastructure necessary to support routine use of artificial intelligence and machine learning (AI/ML) in the accelerator control system. Three capabilities are identified: a machine learning operations (MLOps) framework standardizing the lifecycle of AI/ML automation from data management through deployment and monitoring; a data quality framework defining and enforcing standards required to build trustworthy AI/ML applications; and workflow integration with large language models to assist physicists, engineers, and operators with information retrieval, code development, and routine analysis. Use cases spanning beam diagnostics, beam control, and support system automation illustrate the technical requirements across the complex. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_19507 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | AI-Ready Control System for the Fermilab Accelerator Complex Miceli, Tia Gottschalk, Erik Tooke, Donovan Milton, Evan Santucci, Robert Hoschouer, Hayden Balcewicz, Michael Case, Jennifer Deshpande, Abhishek Fieldhouse, Kit Ganguly, Sudeshna Harrison, Beau Ibrahim, Aisha Kobilarcik, Thomas Olander, Michael Pathak, Abhishek John, Jason St. Sauers, Aaron Accelerator Physics Reliable, high-intensity operation of the Fermilab Accelerator Complex is critical to the success of the Long-Baseline Neutrino Facility and Deep Underground Neutrino Experiment. We describe the requirements and infrastructure necessary to support routine use of artificial intelligence and machine learning (AI/ML) in the accelerator control system. Three capabilities are identified: a machine learning operations (MLOps) framework standardizing the lifecycle of AI/ML automation from data management through deployment and monitoring; a data quality framework defining and enforcing standards required to build trustworthy AI/ML applications; and workflow integration with large language models to assist physicists, engineers, and operators with information retrieval, code development, and routine analysis. Use cases spanning beam diagnostics, beam control, and support system automation illustrate the technical requirements across the complex. |
| title | AI-Ready Control System for the Fermilab Accelerator Complex |
| topic | Accelerator Physics |
| url | https://arxiv.org/abs/2603.19507 |