arXiv subjects

Michael Balcewicz

Publications and source records attributed to Michael Balcewicz.

3 recordsLinked to original sources

arXiv2026

AI-Ready Control System for the Fermilab Accelerator Complex

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.

physics.acc-ph↗

arXiv2024

Preliminary Results of the 2023 International Fermilab Booster Studies

An overview is given of the methods and preliminary results from dedicated beam studies on three topics conducted over five days in July 2023. In the first study, the Fermilab Booster magnets were held constant at magnetic fields corresponding to the injection energy. The beam loss and emittance growth were observed under varying intensity, tunes, and sextupole resonances. The corresponding beam conditions were also simulated with the MADX-SC code~\cite{Schmidt:2644660}. In the second study, measurements of the vertical half-integer resonance and correction methods are conducted for high-intensity beams ramping in the Booster. Finally, syncho-betatron instabilities are observed during transition-crossing in the Booster under strong space-charge conditions.

physics.acc-ph↗

arXiv2019

Reconstruction of Storage Ring 's Linear Optics with Bayesian Inference

A novel approach of accurately reconstructing storage ring's linear optics from turn-by-turn (TbT) data containing measurement error is introduced. This approach adopts a Bayesian inference based on the Markov Chain Monte-Carlo (MCMC) algorithm, which is widely used in data-driven discoveries. By assuming a preset accelerator model with unknown parameters, the inference process yields the their posterior distribution. This approach is demonstrated by inferring the linear optics Twiss parameters and their measurement uncertainties using a set of data measured at the National Synchrotron Light Source-II (NSLS-II) storage ring. Some critical effects, such as radiation damping rate, decoherence due to nonlinearity and chromaticity can also be included in the model and inferred. These effects are usually ignored in existing approaches. One advantage of the MCMC based Bayesian inference is that it doesn't require a large data pool, thus a complete optics reconstruction can be accomplished from a limited number of turns in a single data snapshot, before a significant machine drift can happen. The precise reconstruction of the parameter in accelerator model with the uncertainties is crucial prior information for applying the them to improve machine performance.

physics.acc-ph↗