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Thurman-Keup, R. M.

Publications and source records attributed to Thurman-Keup, R. M..

7 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Geant4-based Analysis of Faraday Cup Performance for PIP-II Laser Wire Scanner System

The Proton Improvement Plan-II (PIP-II) accelerator upgrade at Fermilab marks a significant advancement in high-energy physics research. This initiative aims to enhance Fermilab's accelerator complex by replacing the existing linear accelerator (linac) with a warm front end (WFE) capable of accelerating H- beams up to 2.1 MeV. Subsequently, a superconducting linac (SCL), that further accelerates these beams up to 800 MeV. To accurately measure the transverse beam profile, traditional wire scanners will be utilized in the WFE section, while Laser wire scanners will be implemented along the SCL. The Faraday cup for the Laser wire scanners has been designed using the GEANT4 simulation toolkit. This poster presents a detailed analysis of its performance along the SCL, focusing on electron absorption, secondary electron emission, backscattering, etc.

Wijethunga, S. A.K.↗
DOE OSTI2024

Geant4 Simulations on Faraday Cup Design for PIP-II Laser Wire Scanner System

The Proton Improvement Plan-II (PIP-II) accelerator upgrade at Fermilab represents a groundbreaking leap forward in high-energy physics research. This ambitious initiative involves enhancing Fermilab's accelerator complexby replacing the current linear accelerator (linac) with awarm front end (WFE) capable of accelerating H- beamsup to 2.1 MeV. Subsequently, a superconducting linac further accelerates these beams up to 800 MeV. To preciselymeasure the transverse beam profile, a combination of traditional wire scanners at the WFE section and Laser wirescanners along the superconducting linac are planned forimplementation. This investigation is centered on refiningthe Faraday cup design for the PIP-II Laser wire scannersby utilizing GEANT4, a Monte Carlo simulation toolkit.Leveraging this method enables a comprehensive analysisof particle trajectories, energy deposition, secondary particle emission, backscattering, etc., facilitating optimizationthrough adjustments to cup geometries, materials, andplacement to maximize its efficacy in beam diagnostics.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Beyond PID Controllers: PPO with Neuralized PID Policy for Proton Beam Intensity Control in Mu2e

We introduce a novel Proximal Policy Optimization (PPO) algorithm aimed at addressing the challenge of maintaining a uniform proton beam intensity delivery in the Muon to Electron Conversion Experiment (Mu2e) at Fermi National Accelerator Laboratory (Fermilab). Our primary objective is to regulate the spill process to ensure a consistent intensity profile, with the ultimate goal of creating an automated controller capable of providing real-time feedback and calibration of the Spill Regulation System (SRS) parameters on a millisecond timescale. We treat the Mu2e accelerator system as a Markov Decision Process suitable for Reinforcement Learning (RL), utilizing PPO to reduce bias and enhance training stability. A key innovation in our approach is the integration of a neuralized Proportional-Integral-Derivative (PID) controller into the policy function, resulting in a significant improvement in the Spill Duty Factor (SDF) by 13.6%, surpassing the performance of the current PID controller baseline by an additional 1.6%. This paper presents the preliminary offline results based on a differentiable simulator of the Mu2e accelerator. It paves the groundwork for real-time implementations and applications, representing a crucial step towards automated proton beam intensity control for the Mu2e experiment.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

ML-based Real-Time Control at the Edge: An Approach Using hls4ml

This study focuses on implementing a real-time control system for a particle accelerator facility that performs high energy physics experiments. A critical operating parameter in this facility is beam loss, which is the fraction of particles deviating from the accelerated proton beam into a cascade of secondary particles. Accelerators employ a large number of sensors to monitor beam loss. The data from these sensors is monitored by human operators who predict the relative contribution of different sub-systems to the beam loss. Using this information, they engage control interventions. In this paper, we present a controller to track this phenomenon in real-time using edge-Machine Learning (ML) and support control with low latency and high accuracy. We implemented this system on an Intel Arria 10 SoC. Optimizations at the algorithm, high-level synthesis, and interface levels to improve latency and resource usage are presented. Our design implements a neural network, which can predict the main source of beam loss (between two possible causes) at speeds up to 575 frames per second (fps) (average latency of 1.74 ms). The practical deployed system is required to operate at 320 fps, with a 3ms latency requirement, which has been met by our design successfully.

Shi, R.↗
DOE OSTI2023

FPGA Architectures for Distributed ML Systems for Real-time Beam Loss De-blending

The Accelerator Real-time Edge AI for Distributed Systems (READS) project’s goal is to create a Artificial Intelligence (AI) system for real-time beam loss de-blending within the accelerator enclosure, which houses two accelerators: the Main Injector (MI) and the Recycler Ring (RR). In periods of joint operation, when both machines contain high intensity beam, radioactive beam losses from MI and RR overlap on the enclosure’s beam loss monitoring Beam Loss Monitor (BLM) system, making it difficult to attribute those losses to a single machine. Incorrect diagnoses result in unnecessary downtime that incurs both financialand experimental cost. The ML system will automatically disentangle each machine’s contributions to those measured losses, while not disrupting the existing operations-critical functions of the BLM system. This paper will focus on the evolution of the architectures, which provided the high-frequency, low-latency collection of synchronized data streams to make real-time inferences. The ML models, used for learning both local and global machine signatures and producing high quality inferences based on raw BLM loss measurements, will only be discussed at a high-level.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗